{"id": "eebb5f82634b672b77e763fad1ac6c54b17384f857471be86c32a6883226b91f", "sources": ["arxiv"], "title": "Structured Inference with Large Language Gibbs", "abstract": "The knowledge encoded in large language models (LLMs) can serve as a substrate for structured reasoning over variables describing a complex world, but accessing this knowledge in a probabilistically coherent manner poses a difficult inference problem. We propose Large Language Gibbs, a scheme for structured probabilistic inference that uses conditional distributions of an LLM as transition operators. Rather than sampling structured objects through single-pass autoregressive generation, we iteratively resample individual variables conditioned on others using an LLM's next-token conditionals. This approach avoids order-dependent biases and produces a stationary distribution that reflects a compromise between all local conditionals. We apply this approach to sampling from synthetic distributions, consistent reasoning tasks, and Bayesian structure learning. The results suggest that the use of LLM conditionals in MCMC is a practical alternative to one-pass generation for structured probabilistic inference under a world prior accessible through noisy LLM conditionals.", "authors": ["Sanghyeok Choi", "Henry Gouk", "Esmeralda S. Whitammer"], "categories": ["cs.LG", "cs.CL"], "fields_of_study": [], "published_date": "2026-06-17", "url": "https://arxiv.org/abs/2606.19264", "pdf_url": "https://arxiv.org/pdf/2606.19264v1", "arxiv_id": "2606.19264", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/hyeok9855/large-language-gibbs", "venue": null, "quality_score": 0.65}
{"id": "e62eac19d405492b987241008282a6f5f4ab31ba1c081e4fa3ff8ea47c697650", "sources": ["arxiv", "semantic_scholar"], "title": "Circuit Tracing in Autoregressive Protein Language Models", "abstract": "Protein language models (pLMs) can generate novel protein sequences with properties beyond those observed in nature, yet the mechanisms underlying protein generation remain poorly understood. Existing mechanistic interpretability methods based on sparse autoencoders and transcoders primarily focus on protein representation learning models and do not capture the computation required for autoregressive generation. Here, we introduce ProGenMech, a mechanistic interpretability framework for generative protein language models that extends cross-layer transcoders (CLTs) to ProGen3, a sparse Mixture-of-Experts model trained for both causal generation and span infilling. Unlike per-layer approaches, CLTs reconstruct each layer using sparse latent variables from all preceding layers, enabling faithful recovery of inter-layer generative computation. We further develop a zero-shot circuit discovery framework to identify sparse latent circuits responsible for protein generation and fitness prediction. In causal generation and zero-shot fitness estimation tasks, ProGenMech outperforms local transcoder baselines in recovering ProGen3's probability distribution and functional scoring behavior, while matching the original model's generative distribution in span infilling tasks. Moreover, the recovered circuits reveal biologically meaningful motifs and functional regions associated with conserved sequence patterns and protein fitness landscapes, establishing a foundation for interpretable and steerable protein generation.", "authors": ["Darin Tsui", "William Deinzer", "Daniel Saeedi", "Amirali Aghazadeh"], "categories": ["cs.LG", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2026-06-14", "url": "https://arxiv.org/abs/2606.16044", "pdf_url": "https://arxiv.org/pdf/2606.16044v1", "arxiv_id": "2606.16044", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "b4ea4f2bd4905955546c4b718c1176776c1aa0eca5c37d120f4c93bce089dd0e", "sources": ["arxiv", "semantic_scholar"], "title": "Viral Proteins Reveal Geometry of Protein Language Models", "abstract": "Protein language models are trained on highly imbalanced datasets, raising the question of how they represent underrepresented biological sequences. Using viral proteins as a case study across ESM model families, we identify a dominant nativeness axis in embedding space, aligned with masked reconstruction perplexity, that orders sequences from well-modeled cellular proteins through viral proteins to shuffled and random sequences. Scaling contracts this axis unevenly across viral families. Despite this, protein language model embeddings retain viral-specific signal: viral proteins remain linearly separable beyond zero-shot perplexity and shallow sequence features. Together, these results suggest that pLM representations are structured by a general notion of nativeness while preserving information specific to distinct biological groups.", "authors": ["Arthur Bigot", "Harmon Bhasin", "Core Francisco Park", "Eugene Shakhnovich", "Dianzhuo Wang"], "categories": ["cs.LG", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2026-06-10", "url": "https://arxiv.org/abs/2606.12609", "pdf_url": "https://arxiv.org/pdf/2606.12609v1", "arxiv_id": "2606.12609", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/MisteFr/viral-proteins-plms", "venue": null, "quality_score": 0.65}
{"id": "c49810ee97d0a51419cf749c4b4fa9f086d8e584ea6e8b149b965b62160da914", "sources": ["arxiv", "semantic_scholar"], "title": "Interpretable enzyme function prediction via sparse autoencoder features of ESMC across the microbial protein universe", "abstract": "Microbial genomes and metagenomes contain millions of proteins whose enzymatic functions remain unknown, the enzyme dark matter. While deep learning has improved protein function prediction, most methods are black boxes relying on sequence or structural similarity, limiting discovery of novel catalytic activities. The ESMC-6B protein language model and its sparse autoencoder with a 16,384-dimensional codebook of interpretable biological concepts, each annotated by GPT-5, creates a new opportunity: using these features directly as semantic signatures for enzyme function. Here, we show that ESMC-SAE features enable accurate and interpretable enzyme commission (EC) number prediction without task-specific training or GPU-intensive computation. On a balanced benchmark of 4,868 microbial SwissProt enzymes across 161 EC3 subclasses, ESMC-SAE binary features achieve 78.9% top-1 and 88.5% top-5 accuracy, 37.6% higher than 3-mer baselines (57.3%). In leave-one-EC3-class-out evaluation simulating discovery of novel enzyme classes, SAE features recover the EC1 superclass in 47.7% of cases (3.3x random, 14.3%), versus 26.6% for sequence methods. Discriminative features correspond to mechanistically interpretable concepts: catalytic triad geometry for hydrolases, NAD(P)H-binding Rossmann folds for oxidoreductases, phosphate-binding P-loops for transferases. We also survey the ESM Atlas of 7.7 million clusters and identify 169,859 dark enzyme-like candidates across all major microbial phyla. Our results establish a paradigm for enzyme function discovery in microbial dark matter: interpretable by design, scalable without GPU clusters, and applicable to the billions of proteins in the ESM Atlas.", "authors": ["Yue Hu", "Wanyu Cheng", "Junqing Wang", "Yingchao Liu"], "categories": ["q-bio.QM"], "fields_of_study": ["Biology"], "published_date": "2026-06-10", "url": "https://arxiv.org/abs/2606.12209", "pdf_url": "https://arxiv.org/pdf/2606.12209v1", "arxiv_id": "2606.12209", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "11fb3a5074ccf50b67955588d254dd0415c57abf44f31d2d4a86f30758314396", "sources": ["arxiv", "semantic_scholar"], "title": "The Dynamics of Human and AI-Generated Language: How Semantics Fluctuates across Different Timescales", "abstract": "Spoken language, whether produced by humans or large language models (LLM), unfolds over time with varying semantic content. However, we still lack simple, interpretable time-series features that capture how generic versus specific content is distributed over time, and that can be used to compare human and AI-generated speech. We introduce a semantic-timescale analysis pipeline that turns word-level transcripts with timestamps into semantic time-series. For each spoken narrative, we compute (i) semantic specificity using WordNet-based word depth and (ii) contextual similarity using SBERT embeddings and quantify their temporal dependence using autocorrelation-window measures (ACW-0 and related metrics). We then compare original speech to multiple shuffled controls that selectively disrupt lexical identity, temporal order, and word duration. Across human-read autobiographical narratives, TTS readings, and LLM-generated texts rendered with TTS, we find that segments with longer ACW-0 in the semantic time-series tend to contain more generic vocabulary, whereas segments with shorter ACW-0 are enriched in more specific words. These associations are strongly attenuated or abolished when word order and timing are randomized, indicating that ACW-based measures capture non-trivial temporal organization of semantic content beyond static lexical distributions. Our results suggest that ACW-based semantic timescales are a useful family of features for analyzing and comparing the temporal structure of human and AI-generated speech.", "authors": ["Han-Jen Chang", "Yasir Çatal", "Angelika Wolman", "Agustín Ibáñez", "David Smith", "I-Wen Su", "Kai-Yuan Cheng", "Georg Northoff"], "categories": ["cs.CL", "cs.AI", "eess.AS", "eess.SP"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2026-06-09", "url": "https://arxiv.org/abs/2606.11371", "pdf_url": "https://arxiv.org/pdf/2606.11371v1", "arxiv_id": "2606.11371", "doi": "10.1016/j.csl.2026.102013", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Computer Speech & Language (2026) 102013", "quality_score": 0.55}
{"id": "73b2733d2ce24b808a8208758c7cf16e243f584bf9eb4076327effc258f373bb", "sources": ["arxiv", "semantic_scholar"], "title": "Protein Dynamics Beyond Structure Prediction", "abstract": "The ability to predict protein three-dimensional structures from amino acid sequences is a landmark achievement in molecular biology, where recent deep learning approaches such as AlphaFold are the culmination of decades of work. Yet, the quantitative understanding of how protein sequences give rise to dynamic conformational changes and higher-order assemblies remains unsolved. Folding and conformational states are dynamic, stochastic processes, shaped by sequence, energy, co-translational constraints, chaperone machineries, and the physicochemical conditions of the cellular environment. Recent advances now position the field to move beyond static structural endpoints toward a mechanistic understanding of folding dynamics in living systems. Single-molecule techniques enable time-resolved observation of folding trajectories and intermediate states hitherto hidden by traditional structural biology approaches, while computational innovations and data-driven approaches offer new ways to integrate heterogeneous data across scales. In this Roadmap, we review the current conceptual landscape of protein folding, examine the experimental and theoretical gaps that remain, and discuss emerging strategies that integrate high-resolution measurements with multiscale modeling. We outline a roadmap toward a quantitative and predictive science of protein folding dynamics, conformational kinetics, and macromolecular self-assembly. Realizing this vision would transform our understanding of the dynamics of molecular self-organization, from the folding of individual polypeptides to the emergence of dynamic macromolecular complexes. This will enable rational control of folding and misfolding in health and disease, extend protein engineering principles beyond static structural design, and establish a mechanistic foundation for predictive and personalized interventions in proteostasis-related disorders.", "authors": ["Juliette Griffié", "Sviatlana Shashkova", "Antonio Ciarlo", "Sreekanth K. Manikandan", "Claes Andréasson", "Malin Bäckström", "Tristan Bereau", "Hjalmar Brismar", "Carlos Bustamante", "Marta Carroni", "Roberto Covino", "Andreas Dahlin", "Sebastian Deindl", "Lucie Delemotte", "Arne Elofsson", "John Eriksson", "Giovanna Fragneto", "Anders Gunnarsson", "Per Hammarström", "Caroline Ingre", "Christian Kaiser", "Petronella Kettunen", "Mark C. Leake", "Benjamin Loos", "Anna Månberg", "Antonia S. J. S. Mey", "Richard Neutze", "Thomas Nyström", "Karl Palmås", "Charley Schaefer", "Markus J. Tamás", "Nicola Ticozzi", "Tomás S. Pilvelic", "Jacopo Sacquegno", "B. M.", " Tijms", "Gunnar von Heijne", "Björn Wallner", "Vitali Zhaunerchyk", "Simon Olsson", "Joana B. Pereira", "Julia Fernandez-Rodriguez", "Fredrik Westerlund", "Giovanni Volpe"], "categories": ["q-bio.BM", "cond-mat.mes-hall", "cond-mat.soft"], "fields_of_study": ["Biology", "Physics"], "published_date": "2026-06-07", "url": "https://arxiv.org/abs/2606.08647", "pdf_url": "https://arxiv.org/pdf/2606.08647v1", "arxiv_id": "2606.08647", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "8c80f94a1a6012d40c0562932106544a7d6f569f69e4ef60962c84aaf6c77f9e", "sources": ["arxiv", "semantic_scholar"], "title": "AF_Cache: Efficient Pipeline for Running AlphaFold for High-Throughput Protein-Protein Interaction Prediction", "abstract": "Motivation: Accurate prediction of protein-protein interactions is essential for understanding biological processes, and recent advances such as AlphaFold2 and AlphaFold3 have enabled structure-based interaction prediction at unprecedented accuracy. However, the high computational cost of these methods, driven primarily by CPU-based repeated multiple sequence alignment (MSA) generation and, for AlphaFold2, repeated model recompilations, limits their applicability in large-scale, high-throughput settings. This creates a need for efficient pipelines that retain predictive performance while substantially reducing runtime. Results: We present AF_Cache, a high-throughput Nextflow pipeline for accelerating protein-protein interaction prediction using AlphaFold2 and AlphaFold3. AF_Cache combines GPU-accelerated MSA generation with MMseqs2, feature caching to eliminate redundant alignment computations, and sequence length bucketing to minimise repeated JAX compilations. Benchmarking on a dataset of 5,050 human mitochondrial protein pairs demonstrates a $\\sim$2-fold reduction in inference time for AlphaFold2 and up to a 13-fold speedup of the MSA generation. AF\\_Cache enables efficient large-scale interaction screening and provides a practical framework for deploying AlphaFold-based methods in high-throughput applications. Availability and implementation: The code and Nextflow pipeline are available on GitHub here: https://github.com/clami66/AF_cache. The code for reproducing the results of the paper, the MSAs, and the predicted models can be found at Zenodo: https://zenodo.org/records/20478892", "authors": ["Sarah Narrowe", "Arne Elofsson Claudio Mirabello"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2026-06-03", "url": "https://arxiv.org/abs/2606.04566", "pdf_url": "https://arxiv.org/pdf/2606.04566v1", "arxiv_id": "2606.04566", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/clami66/AF_cache", "venue": null, "quality_score": 0.65}
{"id": "40e0bc6e12f212225681f7b0f972a0b20191b5b0c8b6c7a60c1372f27ea3d36d", "sources": ["arxiv", "semantic_scholar"], "title": "Structure-Aware Prediction of PROTAC-Mediated Protein Degradability via Graph Neural Networks", "abstract": "Proteolysis-targeting chimeras (PROTACs) can selectively degrade disease-causing proteins, yet predicting which targets are amenable to degradation remains a critical bottleneck: existing computational methods require the complete PROTAC molecular structure, information unavailable before synthesis. We present DegradoMap, a graph neural network that predicts PROTAC-mediated degradability from protein structure and E3 ligase identity alone -- the minimal information available at the target selection stage. The model encodes biophysical priors through lysine-weighted graph pooling with per-protein normalization, models protein-E3 compatibility via cross-attention, and integrates cellular context from the Cancer Dependency Map. On the PROTAC-8K benchmark (3,101 samples, 155 targets, 10 E3 ligases), DegradoMap achieves 0.646+-0.124 AUROC on target-unseen evaluation (best seed: 0.7449) and 0.811 AUROC on CRBN->VHL E3-unseen transfer, outperforming GNN and machine learning baselines. The model additionally recommends optimal E3 ligases with 74% Hit@3 accuracy. Two findings carry broader implications: E(3)-equivariant architectures underperform the simpler invariant design for this scalar prediction task, and ESM-2 embeddings improve peak performance only with careful regularization -- naive integration fails. DegradoMap provides pre-synthesis computational guidance for degradability assessment; its well-calibrated confidence scores (ECE = 0.029, target-unseen) enable practitioners to prioritize high-confidence predictions for experimental follow-up. However, the high seed variance (std = 0.124) and limited E3 coverage require ensembling for reliable deployment.", "authors": ["Bryan Cheng", "Austin Jin"], "categories": ["q-bio.QM", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2026-06-01", "url": "https://arxiv.org/abs/2606.04021", "pdf_url": "https://arxiv.org/pdf/2606.04021v1", "arxiv_id": "2606.04021", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "2bbd135e974bdbf6d0306032a4adb89962a70f145e2c380cbb4063d72814d5e0", "sources": ["arxiv", "semantic_scholar"], "title": "Structure-Guided Adaptive Propagation for Protein-Protein Interaction Site Prediction", "abstract": "Accurate prediction of protein-protein interaction sites (PPIS) is essential for understanding cellular processes, disease mechanisms, and therapeutic target discovery. Graph-based deep learning has advanced PPIS prediction by incorporating residue-level structural context. However, most graph-based models still rely on fixed propagation schemes that treat all residues similarly, despite the structural and functional heterogeneity of protein interfaces. Such propagation may limit the ability to adapt information diffusion to local geometric environments, making it difficult to distinguish true interaction sites from structurally similar non-interacting neighbors. We present SGAP-PPIS, a structure-guided adaptive propagation model for PPIS prediction. Rather than using a fixed propagation mechanism, SGAP-PPIS leverages multi-scale geometric states from an equivariant graph neural network to generate residue-wise propagation coefficients. This design allows each residue to adaptively balance local feature preservation and neighborhood diffusion according to its geometric microenvironment. Experimental results show that SGAP-PPIS achieves competitive performance among the state-of-the-art methods on Test\\_60. Ablation studies show that geometry-conditioned adaptive propagation, scale-aligned geometric guidance, and multi-step propagation-state representation jointly drive these improvements.", "authors": ["Enqiang Zhu", "Yizi Liu", "Yilong Luo", "Yao Chen", "Yu Zhang", "Baoshan Ma"], "categories": ["cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-06-01", "url": "https://arxiv.org/abs/2606.01781", "pdf_url": "https://arxiv.org/pdf/2606.01781v1", "arxiv_id": "2606.01781", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "8ca15f915155713f54943353f5b4b9e6af4ceabda10e34de0f67d0ef46d07d85", "sources": ["arxiv", "semantic_scholar"], "title": "AMix-2: Establishing Protein as a Native Modality in Large Language Models", "abstract": "We present AMix-2, a protein-text foundation model that establishes protein as a native modality in large language models (LLMs), unifying protein understanding and sequence design within a single foundation model. AMix-2 is built upon two key ideas: (1) a unified protein-text formulation that embeds natural language and protein sequence in a shared token space, enabling one model to perform biological reasoning and conditional design instead of separate downstream task-specialized models; and (2) a block-wise diffusion language modeling backbone that combines causal generation across blocks with bidirectional context and iterative refinement within blocks. This scheme better matches the intrinsic nature of proteins than a strict left-to-right factorization. To evaluate protein foundation models under realistic generalization settings, we further introduce ProteinArena, a comprehensive benchmark with time-aware and homology-aware protocols across various understanding and design tasks, and with baselines covering classical bioinformatics tools, protein-specialized models and LLMs. On ProteinArena, AMix-2 outperforms frontier LLMs and demonstrates competitive performance to task-specific protein models. Controlled experiments further show that the diffusion-based paradigm generally surpasses its autoregressive counterpart, highlighting the advantage of flexible generation order for protein sequences. We release both AMix-2 and ProteinArena to facilitate open research in protein foundation models.", "authors": ["Keyue Qiu", "Yixin Wu", "Lihao Wang", "Yawen Ouyang", "Jixiang Yu", "Zihan Zhou", "Changze Lv", "Dongyu Xue", "Yuxuan Song", "Xinbo Zhang", "Hao Wang", "Jiangtao Feng", "Zhiqiang Gao", "Lijun Wu", "Xiaoqing Zheng", "Ka-Chun Wong", "Lei Bai", "Ya-Qin Zhang", "Wei-Ying Ma", "Dahua Lin", "Bowen Zhou", "Hao Zhou"], "categories": ["q-bio.BM", "cs.AI"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2026-05-29", "url": "https://arxiv.org/abs/2605.30963", "pdf_url": "https://arxiv.org/pdf/2605.30963v1", "arxiv_id": "2605.30963", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "27ca13b36cf993fbd2645d1eeff7b54b139fc4615dc893021602240f66fde2fd", "sources": ["arxiv", "semantic_scholar"], "title": "Atom-level Protein Representation Learning Improves Protein Structure Prediction", "abstract": "Recent advances in generative modeling show that pretrained representations can improve generation as conditioning features or alignment targets. Motivated by this, we study protein representations for predicting structures beyond conventional function annotation. We propose TriProRep, a structure-aware pretraining method that jointly models three aligned residue-level views: amino-acid identity, backbone geometry, and local full-atom geometry, discretely encoded via VQ-VAE tokenizers. By pretraining to recover original tokens from generator-corrupted views, TriProRep learns to distinguish plausible but incorrect cross-view augmentations from the original protein. We further introduce RepSP, a benchmark for evaluating protein representations in structure-predictive settings. RepSP tests three uses of representations: homodimer co-folding from apo-chain representations, residue-level prediction of homodimer-derived interaction properties, and representation-aligned monomer structure prediction. Across these tasks, TriProRep improves over sequence-only and prior structure-aware representation models, while maintaining competitive performance on conventional benchmarks.", "authors": ["Taewon Kim", "Hyosoon Jang", "Hyunjin Seo", "Seonghwan Seo", "Hyeongwoo Kim", "Wonho Zhung", "Mingyeong Shin", "Wooyoun Kim", "Sungsoo Ahn"], "categories": ["q-bio.BM", "cs.AI"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2026-05-21", "url": "https://arxiv.org/abs/2605.22133", "pdf_url": "https://arxiv.org/pdf/2605.22133v3", "arxiv_id": "2605.22133", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "877b175f6bb2cb864fffd0f095bb42ae4943f860f74df5445e19a4bbf23378de", "sources": ["arxiv", "semantic_scholar"], "title": "Structural Interpretations of Protein Language Model Representations via Differentiable Graph Partitioning", "abstract": "Protein language models such as ESM-2 learn rich residue representations that achieve strong performance on protein function prediction, but their features remain difficult to interpret as structural $\\&$ evolutionary signals are encoded in dense latent spaces. We propose a plug-$\\&$-play framework that projects ESM-2 representations onto protein contact graphs $\\&$ applies $\\textbf{SoftBlobGIN}$, a lightweight Graph Isomorphism Network with differentiable Gumbel-softmax substructure pooling, to perform structure-aware message passing $\\&$ learn coarse functional substructures for downstream prediction tasks. Across enzyme classification, SoftBlobGIN achieves 92.8\\% accuracy $\\&$ 0.898 macro-F1. Unlike post hoc analysis of protein language models alone, our method produces directly auditable structural explanations: GNNExplainer recovers biologically meaningful active-site residues, spatially localized functional clusters, $\\&$ catalytic contact patterns. On binding-site detection, SoftBlobGIN improves residue AUROC from $0.885$ using an ESM-2 linear probe to $0.983$, indicating that these structural explanations are not recoverable from language-model features alone. Learned blob partitions provide an additional layer of interpretability by automatically grouping residues into functional substructures, with blobs containing annotated active-site residues showing $1.85\\times$ higher importance than other blobs ($ρ{=}0.339$, $p{=}0.009$), without any active-site supervision. Our framework requires no retraining of the language model, adds only $\\sim$1.1M parameters, $\\&$ generalises across ProteinShake tasks, achieving $F_{\\max}$ of $0.733$ on Gene Ontology prediction $\\&$ AUROC of $0.969$ on binding-site detection. We position this as an interpretable structural companion to protein language models that makes their predictions more transparent $\\&$ auditable.", "authors": ["Siddhant Dutta", "Edward Tan Beng Wai", "Soumick Sarker", "Pasan Gunawardane", "Jagath C. Rajapakse"], "categories": ["cs.LG", "cs.AI", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2026-05-09", "url": "https://arxiv.org/abs/2605.10985", "pdf_url": "https://arxiv.org/pdf/2605.10985v1", "arxiv_id": "2605.10985", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "db93d0bc6e7ba59e810d3ca8284e31894b682acdf22b257e42ac0fbdac7dc1e6", "sources": ["arxiv", "semantic_scholar"], "title": "ProteinJEPA: Latent prediction complements protein language models", "abstract": "Protein language models are trained primarily with masked language modeling (MLM), which predicts amino-acid identities at masked positions. We ask whether latent-space prediction can complement these token-level objectives under matched wall-clock budget. Across pretrained and random-init protein sequence encoders at 35--150M parameters, we find that the best protein-JEPA design is not all-position latent prediction but a variant: predicting latent targets only at masked positions, and retaining the MLM cross-entropy. We call this recipe masked-position MLM+JEPA. On a 16-task downstream suite (15 frozen linear probes plus SCOPe-40 zero-shot fold retrieval), under matched wall-clock budgets, this recipe wins more tasks than it loses against MLM-only continuation: 10 wins / 3 losses / 3 ties (hereafter W/L/T) on pretrained ESM2-35M, 11/2/3 on ESM2-150M while results in pretraining from scratch are mixed (6/8/2). Gains are seen for multiple models on 11 of 16 tasks, including stability, \\b{eta}β\\b{eta}-lactamase fitness, variant effect, intrinsic disorder, remote homology, enzyme classification, and SCOPe-40 fold retrieval. Tasks with more losses than wins are Fluorescence (TAPE) and Peptide-HLA Binding. All-position MLM+JEPA matches MLM-only overall but does not reproduce the masked-position gains. JEPA-only (no MLM) collapses in nearly every experiment. We conclude that JEPA, when combined with MLM, is competitive and can outperform pure MLM in pretraining and continued training, even under matched wall-clock budgets.", "authors": ["Dan Ofer", "Dafna Shahaf", "Michal Linial"], "categories": ["cs.LG", "cs.AI", "q-bio.BM", "stat.ML"], "fields_of_study": ["Computer Science", "Biology", "Mathematics"], "published_date": "2026-05-08", "url": "https://arxiv.org/abs/2605.07554", "pdf_url": "https://arxiv.org/pdf/2605.07554v1", "arxiv_id": "2605.07554", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "0e173453c857154935b528bc7b7cb2eabb08e7d9bcd1e397e97bf180b93df6c4", "sources": ["arxiv", "semantic_scholar"], "title": "ProtSent: Protein Sentence Transformers", "abstract": "Protein language models (pLMs) produce per-residue representations that capture evolutionary and structural information, yet their mean-pooled sequence embeddings are not explicitly trained to reflect functional, evolutionary or structural similarity between proteins. We present Protein Sentence Transformers (ProtSent), a contrastive fine-tuning framework for adapting PLMs into general-purpose embedding models. ProtSent trains with MultipleNegativesRankingLoss across five protein-pair datasets: Pfam families, structurally derived hard negatives, AlphaFold DB structural pairs, and StringDB protein--protein interactions, and Deep Mutational Scanning data. We evaluate on 23~downstream tasks using frozen embeddings with a k-nearest-neighbor probe to measure embedding neighborhood quality. On ESM-2 150M, ProtSent improves 15 of 23 tasks, with gains of +105% on remote homology detection, +17% on variant effect prediction, and +19.9% Recall@1 on SCOPe-40 structural retrieval. The 35M variant improves 16 of 23 tasks with +40.5% on remote homology and +15.5% Recall@1 on SCOPe-40. Contrastive fine-tuning restructures the embedding space to better capture protein function and structure, without any task-specific supervision. We release the models, public data, and training recipe and code.", "authors": ["Dan Ofer", "Oriel Perets", "Michal Linial", "Nadav Rappoport"], "categories": ["cs.LG", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2026-05-07", "url": "https://arxiv.org/abs/2605.06830", "pdf_url": "https://arxiv.org/pdf/2605.06830v1", "arxiv_id": "2605.06830", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "a002f1dab6946b4f6d0de16dc2bea7cb2812c9d0637198b47185b56397bfc963", "sources": ["arxiv", "semantic_scholar"], "title": "Better Protein Function Prediction by Modeling Survivorship Bias", "abstract": "Protein sequence data from nature exhibits survivorship bias: we only observe data from those organisms that survive and reproduce, while non-functional protein mutations are eliminated by natural selection. Thus, predicting whether a protein sequence is functional often requires learning from positive examples alone. While positive-unlabeled (PU) learning frameworks offer a generic solution to this problem, existing PU methods ignore the evolutionary processes that shape sequence observability and cause survivorship bias. Consider a sequence that is one mutation away from a commonly-observed protein variant in a well-surveilled organism. If the sequence were functional, it would likely be observed. If it is not observed, this suggests non-functionality. In contrast, sequences that are unlikely to arise through mutation may be missing simply because they never arose. Thus, these two kinds of missing sequences should be treated differently when training models. In this work, we propose Evo-PU, a PU learning framework that uses a scientific understanding of nucleotide mutation to model survivorship bias for well-surveilled single-organism sequence data. On three prediction tasks using single-organism uniform-coverage surveillance data -- predicting results from held-out influenza and respiratory syncytial virus (RSV) mutagenesis studies, and predicting future SARS-CoV-2 variants -- Evo-PU outperforms standard PU learning, one-class classification (OCC), and protein language models (PLMs). On prediction tasks from multi-organism ProteinGym datasets with more heterogeneous surveillance coverage, we identify opportunities to generalize our approach.", "authors": ["Zhongmou Chao", "Poompol Buathong", "Ekaterina Selivanovitch", "Susan Daniel", "Peter I. Frazier"], "categories": ["cs.LG", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2026-05-07", "url": "https://arxiv.org/abs/2605.06879", "pdf_url": "https://arxiv.org/pdf/2605.06879v1", "arxiv_id": "2605.06879", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.35}
{"id": "0160c746d795fb93edde86692344de66f756e9585e28f87f19d3c574e4c56d48", "sources": ["arxiv", "semantic_scholar"], "title": "Targeted Linguistic Analysis of Sign Language Models with Minimal Translation Pairs", "abstract": "Models of sign language have historically lagged behind those for spoken language (text and speech). Recent work has greatly improved their performance on tasks like sign language translation and isolated sign recognition. However, it remains unclear to what extent existing models capture various linguistic phenomena of sign language, and how well they use cues from the multiple articulators used in sign language (hands, upper body, face). We introduce a new benchmark dataset for American Sign Language, ASL Minimal Translation Pairs (ASL-MTP), divided into multiple types of sign language phenomena and corresponding minimal pairs of translations, for performing such linguistic analyses. As a case study, we use ASL-MTP to analyze a state-of-the-art ASL-to-English translation model. We conduct a targeted analysis of the model by ablating various input cues during training and inference and evaluating on the phenomena in ASL-MTP. Our results show that, while the model performs above chance level on most of the phenomena, it relies strongly on manual cues while often missing crucial non-manual cues.", "authors": ["Serpil Karabüklü", "Kanishka Misra", "Shester Gueuwou", "Diane Brentari", "Greg Shakhnarovich", "Karen Livescu"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-29", "url": "https://arxiv.org/abs/2604.27232", "pdf_url": "https://arxiv.org/pdf/2604.27232v2", "arxiv_id": "2604.27232", "doi": "10.48550/arXiv.2604.27232", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.55}
{"id": "7b983dc87dcd7c6de4d0a1e0eaa81de2c6bbc08aecaf31285565e7426bb841a8", "sources": ["arxiv", "semantic_scholar"], "title": "TriFit: Trimodal Fusion with Protein Dynamics for Mutation Fitness Prediction", "abstract": "Predicting the functional impact of single amino acid substitutions (SAVs) is central to understanding genetic disease and engineering therapeutic proteins. While protein language models and structure-based methods have achieved strong performance on this task, they systematically neglect protein dynamics; residue flexibility, correlated motions, and allosteric coupling are well-established determinants of mutational tolerance in structural biology, yet have not been incorporated into supervised variant effect predictors. We present TriFit, a multimodal framework that integrates sequence, structure, and protein dynamics through a four-expert Mixture-of-Experts (MoE) fusion module with trimodal cross-modal contrastive learning. Sequence embeddings are extracted via masked marginal scoring with ESM-2 (650M); structural embeddings from AlphaFold2-predicted C-alpha geometries; and dynamics embeddings from Gaussian Network Model (GNM) B-factors, mode shapes, and residue-residue cross-correlations. The MoE router adaptively weights modality combinations conditioned on the input, enabling protein-specific fusion without fixed modality assumptions. On the ProteinGym substitution benchmark (217 DMS assays, 696k SAVs), TriFit achieves AUROC 0.897 +/- 0.0002, outperforming all supervised baselines including Kermut (0.864) and ProteinNPT (0.844), and the best zero-shot model ESM3 (0.769). Ablation studies confirm that dynamics provides the largest marginal contribution over pairwise modality combinations, and TriFit achieves well-calibrated probabilistic outputs (ECE = 0.044) without post-hoc correction.", "authors": ["Seungik Cho"], "categories": ["cs.LG", "q-bio.BM", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2026-04-13", "url": "https://arxiv.org/abs/2604.12026", "pdf_url": "https://arxiv.org/pdf/2604.12026v1", "arxiv_id": "2604.12026", "doi": "10.48550/arXiv.2604.12026", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.542}
{"id": "f6ef3dbd4c2d0ecc1c194c9780bc2c4a5c1737fcc4f9ca45959a2d45664202c8", "sources": ["arxiv", "semantic_scholar"], "title": "Rethinking Token Prediction: Tree-Structured Diffusion Language Model", "abstract": "Discrete diffusion language models have emerged as a competitive alternative to auto-regressive language models, but training them efficiently under limited parameter and memory budgets remains challenging. Modern architectures are predominantly based on a full-vocabulary token prediction layer, which accounts for a substantial fraction of model parameters (e.g., more than 20% in small scale DiT-style designs) and often dominates peak GPU memory usage. This leads to inefficient use of both parameters and memory under constrained training resources. To address this issue, we revisit the necessity of explicit full-vocabulary prediction, and instead exploit the inherent structure among tokens to build a tree-structured diffusion language model. Specifically, we model the diffusion process with intermediate latent states corresponding to a token's ancestor nodes in a pre-constructed vocabulary tree. This tree-structured factorization exponentially reduces the classification dimensionality, makes the prediction head negligible in size, and enables reallocation of parameters to deepen the attention blocks. Empirically, under the same parameter budget, our method reduces peak GPU memory usage by half while matching the perplexity performance of state-of-the-art discrete diffusion language models.", "authors": ["Zihao Wu", "Haoming Yang", "Juncheng Dong", "Vahid Tarokh"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-04-04", "url": "https://arxiv.org/abs/2604.03537", "pdf_url": "https://arxiv.org/pdf/2604.03537v1", "arxiv_id": "2604.03537", "doi": "10.48550/arXiv.2604.03537", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5317}
{"id": "5cd5b775f73bda757c9e0ee7d672d210d6921b01a58fc4bd66b545c57fabd49d", "sources": ["arxiv", "semantic_scholar"], "title": "Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction", "abstract": "We investigate the parameter space of transformer models trained on protein sequence data using a statistical mechanics framework, sampling the loss landscape at varying temperatures by Langevin dynamics to characterize the low-loss manifold and understand the mechanisms underlying the superior performance of transformers in protein structure prediction. We find that, at variance with feedforward networks, the lack of a first--order--like transition in the loss of the transformer produces a range of intermediate temperatures with good learning properties. We show that the parameters of most layers are highly conserved at these temperatures if the dimension of the embedding is optimal, and we provide an operative way to find this dimension. Finally, we show that the attention matrix is more predictive of the contact maps of the protein at higher temperatures and for higher dimensions of the embedding than those optimal for learning.", "authors": ["L. Ghiringhelli", "A. Zambon", "G. Tiana"], "categories": ["cond-mat.dis-nn", "cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Physics", "Biology"], "published_date": "2026-03-31", "url": "https://arxiv.org/abs/2603.29529", "pdf_url": "https://arxiv.org/pdf/2603.29529v1", "arxiv_id": "2603.29529", "doi": "10.48550/arXiv.2603.29529", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5271}
{"id": "5a7bfbdbb17debee63d7dc76a7d9cd3d77aa9dc92a88a2dd96ce4a58b472aa10", "sources": ["arxiv", "semantic_scholar"], "title": "Merge and Conquer: Instructing Multilingual Models by Adding Target Language Weights", "abstract": "Large Language Models (LLMs) remain heavily centered on English, with limited performance in low-resource languages. Existing adaptation approaches, such as continual pre-training, demand significant computational resources. In the case of instructed models, high-quality instruction data is also required, both of which are often inaccessible for low-resource language communities. Under these constraints, model merging offers a lightweight alternative, but its potential in low-resource contexts has not been systematically explored. In this work, we explore whether it is possible to transfer language knowledge to an instruction-tuned LLM by merging it with a language-specific base model, thereby eliminating the need of language-specific instructions and repeated fine-tuning processes whenever stronger instructed variants become available. Through experiments covering four Iberian languages (Basque, Catalan, Galician, and Spanish) and two model families, we show that merging enables effective instruction following behavior in new languages and even supports multilingual capability through the combination of multiple language-specific models. Our results indicate that model merging is a viable and efficient alternative to traditional adaptation methods for low-resource languages, achieving competitive performance while greatly reducing computational cost.", "authors": ["Eneko Valero", "Maria Ribalta i Albado", "Oscar Sainz", "Naiara Perez", "German Rigau"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-30", "url": "https://arxiv.org/abs/2603.28263", "pdf_url": "https://arxiv.org/pdf/2603.28263v1", "arxiv_id": "2603.28263", "doi": "10.48550/arXiv.2603.28263", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5259}
{"id": "9847154ed392bb82e7bc3ce3b3a65071cf778f1242631fc4cef375fc4dae1124", "sources": ["arxiv", "semantic_scholar"], "title": "Introducing MELI: the Mandarin-English Language Interview Corpus", "abstract": "We introduce the Mandarin-English Language Interview (MELI) Corpus, an open-source resource of 29.8 hours of speech from 51 Mandarin-English bilingual speakers. MELI combines matched sessions in Mandarin and English with two speaking styles: read sentences and spontaneous interviews about language varieties, standardness, and learning experiences. Audio was recorded at 44.1 kHz (16-bit, stereo). Interviews were fully transcribed, force-aligned at word and phone levels, and anonymized. Descriptively, the Mandarin component totals ~14.7 hours (mean duration 17.3 minutes) and the English component ~15.1 hours (mean duration 17.8 minutes). We report token/type statistics for each language and document code-switching patterns (frequent in Mandarin sessions; more limited in English sessions). The corpus design supports within-/cross-speaker, within/cross-language acoustic comparison and links acoustics to speakers' stated language attitudes, enabling both quantitative and qualitative analyses. The MELI Corpus will be released with transcriptions, alignments, metadata, scans of labelled maps and documentation under a CC BY-NC 4.0 license.", "authors": ["Suyuan Liu", "Molly Babel"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-27", "url": "https://arxiv.org/abs/2603.27043", "pdf_url": "https://arxiv.org/pdf/2603.27043v2", "arxiv_id": "2603.27043", "doi": "10.63317/3umiyc4sxwhk", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.8075}
{"id": "1a8e942c570ec1a135a7c5d4dc93befb71353719b61f0b94db814cd5c9d2833d", "sources": ["arxiv", "semantic_scholar"], "title": "Central Dogma Transformer III: Interpretable AI Across DNA, RNA, and Protein", "abstract": "Biological AI models increasingly predict complex cellular responses, yet their learned representations remain disconnected from the molecular processes they aim to capture. We present CDT-III, which extends mechanism-oriented AI across the full central dogma: DNA, RNA, and protein. Its two-stage Virtual Cell Embedder architecture mirrors the spatial compartmentalization of the cell: VCE-N models transcription in the nucleus and VCE-C models translation in the cytosol. On five held-out genes, CDT-III achieves per-gene RNA r=0.843 and protein r=0.969. Adding protein prediction improves RNA performance (r=0.804 to 0.843), demonstrating that downstream tasks regularize upstream representations. Protein supervision sharpens DNA-level interpretability, increasing CTCF enrichment by 30%. Analysis of experimentally measured mRNA and protein responses reveals that the majority of genes with observable mRNA changes show opposite protein-level changes (66.7% at |log2FC|>0.01, rising to 87.5% at |log2FC|>0.02), exposing a fundamental limitation of RNA-only perturbation models. Despite this pervasive direction discordance, CDT-III correctly predicts both mRNA and protein responses. Applied to in silico CD52 knockdown approximating Alemtuzumab, the model predicts 29/29 protein changes correctly and rediscovers 5 of 7 known clinical side effects without clinical data. Gradient-based side effect profiling requires only unperturbed baseline data (r=0.939), enabling screening of all 2,361 genes without new experiments.", "authors": ["Nobuyuki Ota"], "categories": ["cs.LG", "q-bio.GN"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2026-03-24", "url": "https://arxiv.org/abs/2603.23361", "pdf_url": "https://arxiv.org/pdf/2603.23361v2", "arxiv_id": "2603.23361", "doi": "10.48550/arXiv.2603.23361", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5191}
{"id": "74b4d0a833d0a0d211a85ff9a40d6f83f116c8f371a835e80fef0e0af83894b0", "sources": ["arxiv", "semantic_scholar"], "title": "Task-Specific Efficiency Analysis: When Small Language Models Outperform Large Language Models", "abstract": "Large Language Models achieve remarkable performance but incur substantial computational costs unsuitable for resource-constrained deployments. This paper presents the first comprehensive task-specific efficiency analysis comparing 16 language models across five diverse NLP tasks. We introduce the Performance-Efficiency Ratio (PER), a novel metric integrating accuracy, throughput, memory, and latency through geometric mean normalization. Our systematic evaluation reveals that small models (0.5--3B parameters) achieve superior PER scores across all given tasks. These findings establish quantitative foundations for deploying small models in production environments prioritizing inference efficiency over marginal accuracy gains.", "authors": ["Jinghan Cao", "Yu Ma", "Xinjin Li", "Qingyang Ren", "Xiangyun Chen"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-22", "url": "https://arxiv.org/abs/2603.21389", "pdf_url": "https://arxiv.org/pdf/2603.21389v1", "arxiv_id": "2603.21389", "doi": "10.14428/esann/2026.es2026-274", "citation_count": 9, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "The European Symposium on Artificial Neural Networks", "quality_score": 0.5168}
{"id": "ca82ff6b387612787890f2f522dc113d867a0ef39f15c8b538fcc7d49f7fa677", "sources": ["arxiv", "semantic_scholar"], "title": "Cross-Granularity Representations for Biological Sequences: Insights from ESM and BiGCARP", "abstract": "Recent advances in general-purpose foundation models have stimulated the development of large biological sequence models. While natural language shows symbolic granularity (characters, words, sentences), biological sequences exhibit hierarchical granularity whose levels (nucleotides, amino acids, protein domains, genes) further encode biologically functional information. In this paper, we investigate the integration of cross-granularity knowledge from models through a case study of BiGCARP, a Pfam domain-level model for biosynthetic gene clusters, and ESM, an amino acid-level protein language model. Using representation analysis tools and a set of probe tasks, we first explain why a straightforward cross-model embedding initialization fails to improve downstream performance in BiGCARP, and show that deeper-layer embeddings capture a more contextual and faithful representation of the model's learned knowledge. Furthermore, we demonstrate that representations at different granularities encode complementary biological knowledge, and that combining them yields measurable performance gains in intermediate-level prediction tasks. Our findings highlight cross-granularity integration as a promising strategy for improving both the performance and interpretability of biological foundation models.", "authors": ["Hanlin Xiao", "Rainer Breitling", "Eriko Takano", "Mauricio A. Álvarez"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-21", "url": "https://arxiv.org/abs/2603.20825", "pdf_url": "https://arxiv.org/pdf/2603.20825v1", "arxiv_id": "2603.20825", "doi": "10.1109/BIBM66473.2025.11356265", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Bioinformatics and Biomedicine", "quality_score": 0.5156}
{"id": "f62f079d344668a8da9e108e465f468bc97ae2dfc32bc99307cd0a72e3d24188", "sources": ["arxiv", "semantic_scholar"], "title": "From Snapshots to Symphonies: The Evolution of Protein Prediction from Static Structures to Generative Dynamics and Multimodal Interactions", "abstract": "The protein folding problem has been fundamentally transformed by artificial intelligence, evolving from static structure prediction toward the modeling of dynamic conformational ensembles and complex biomolecular interactions. This review systematically examines the paradigm shift in AI driven protein science across five interconnected dimensions: unified multimodal representations that integrate sequences, geometries, and textual knowledge; refinement of static prediction through MSA free architectures and all atom complex modeling; generative frameworks, including diffusion models and flow matching, that capture conformational distributions consistent with thermodynamic ensembles; prediction of heterogeneous interactions spanning protein ligand, protein nucleic acid, and protein protein complexes; and functional inference of fitness landscapes, mutational effects, and text guided property prediction. We critically analyze current bottlenecks, including data distribution biases, limited mechanistic interpretability, and the disconnect between geometric metrics and biophysical reality, while identifying future directions toward physically consistent generative models, multimodal foundation architectures, and experimental closed loop systems. This methodological transformation marks artificial intelligence's transition from a structural analysis tool into a universal simulator capable of understanding and ultimately rewriting the dynamic language of life.", "authors": ["Jingzhi Chen", "Lijian Xu"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-19", "url": "https://arxiv.org/abs/2603.18505", "pdf_url": "https://arxiv.org/pdf/2603.18505v1", "arxiv_id": "2603.18505", "doi": "10.48550/arXiv.2603.18505", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5133}
{"id": "8b422bdf48b01054475ad524262a98b442d93a3196c1372bab5ed383b7c27c79", "sources": ["arxiv", "semantic_scholar"], "title": "Integrative modelling of protein-glycan interactions with HADDOCK3", "abstract": "Glycans are structurally diverse and flexible biomolecules that play key roles in many biological processes. Their conformational variability makes the modeling of their interactions with proteins particularly challenging. This chapter presents a step-by-step protocol for modeling protein-glycan interactions using HADDOCK3, an integrative modeling platform that supports the inclusion of experimental or predicted interaction restraints and allows for flexible refinement of the solutions. The workflow is illustrated using the interaction between a linear homopolymer glycan, 4-beta-glucopyranose, and the catalytic domain of the Humicola grisea Cel12A enzyme, for which an experimental X-ray structure is available as a reference. Detailed instructions are provided for input structure preparation, restraint definition, docking setup, execution, and result analysis. Application of the protocol starting from unbound structures yields models of acceptable to medium quality, with interface-ligand RMSD values below 3 angstroms. Although illustrated on a specific system, the protocol has been optimized and benchmarked on multiple protein-glycan complexes and is broadly applicable to similar systems, providing a framework for integrative modeling of protein-glycan interactions.", "authors": ["Victor Reys", "Marco Giulini", "Alexandre M. J. J. Bonvin"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2026-03-18", "url": "https://arxiv.org/abs/2603.17251", "pdf_url": "https://arxiv.org/pdf/2603.17251v1", "arxiv_id": "2603.17251", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3259}
{"id": "5c142e2cc97364a477a5751796bb3145d048a3a33452ce2c4487484b411bf719", "sources": ["arxiv", "semantic_scholar"], "title": "Robust Language Identification for Romansh Varieties", "abstract": "The Romansh language has several regional varieties, called idioms, which sometimes have limited mutual intelligibility. Despite this linguistic diversity, there has been a lack of documented efforts to build a language identification (LID) system that can distinguish between these idioms. Since Romansh LID should also be able to recognize Rumantsch Grischun, a supra-regional variety that combines elements of several idioms, this makes for a novel and interesting classification problem. In this paper, we present a LID system for Romansh idioms based on an SVM approach. We evaluate our model on a newly curated benchmark across two domains and find that it reaches an average in-domain accuracy of 97%, enabling applications such as idiom-aware spell checking or machine translation. Our classifier is publicly available.", "authors": ["Charlotte Model", "Sina Ahmadi", "Jannis Vamvas"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-16", "url": "https://arxiv.org/abs/2603.15969", "pdf_url": "https://arxiv.org/pdf/2603.15969v2", "arxiv_id": "2603.15969", "doi": "10.48550/arXiv.2603.15969", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5099}
{"id": "546c6c9d10d36a66d8559116e5310a5fac6b86ec200f9c2eeb9fc5a55770ee28", "sources": ["arxiv", "semantic_scholar"], "title": "Reverse Distillation: Consistently Scaling Protein Language Model Representations", "abstract": "Unlike the predictable scaling laws in natural language processing and computer vision, protein language models (PLMs) scale poorly: for many tasks, models within the same family plateau or even decrease in performance, with mid-sized models often outperforming the largest in the family. We introduce Reverse Distillation, a principled framework that decomposes large PLM representations into orthogonal subspaces guided by smaller models of the same family. The resulting embeddings have a nested, Matryoshka-style structure: the first k dimensions of a larger model's embedding are exactly the representation from the smaller model. This ensures that larger reverse-distilled models consistently outperform smaller ones. A motivating intuition is that smaller models, constrained by capacity, preferentially encode broadly-shared protein features. Reverse distillation isolates these shared features and orthogonally extracts additional contributions from larger models, preventing interference between the two. On ProteinGym benchmarks, reverse-distilled ESM-2 variants outperform their respective baselines at the same embedding dimensionality, with the reverse-distilled 15 billion parameter model achieving the strongest performance. Our framework is generalizable to any model family where scaling challenges persist. Code and trained models are available at https://github.com/rohitsinghlab/plm_reverse_distillation.", "authors": ["Darius Catrina", "Christian Bepler", "Samuel Sledzieski", "Rohit Singh"], "categories": ["cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2026-03-08", "url": "https://arxiv.org/abs/2603.07710", "pdf_url": "https://arxiv.org/pdf/2603.07710v1", "arxiv_id": "2603.07710", "doi": "10.48550/arXiv.2603.07710", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/rohitsinghlab/plm_reverse_distillation", "venue": "arXiv.org", "quality_score": 0.7739}
{"id": "354177cd48b5857eb8de69946e5d2468cdcc5165bb0f02b26bc6e2c5313fd3ae", "sources": ["arxiv", "semantic_scholar"], "title": "Inference-Time Toxicity Mitigation in Protein Language Models", "abstract": "Protein language models (PLMs) are becoming practical tools for de novo protein design, yet their dual-use potential raises safety concerns. We show that domain adaptation to specific taxonomic groups can elicit toxic protein generation, even when toxicity is not the training objective. To address this, we adapt Logit Diff Amplification (LDA) as an inference-time control mechanism for PLMs. LDA modifies token probabilities by amplifying the logit difference between a baseline model and a toxicity-finetuned model, requiring no retraining. Across four taxonomic groups, LDA consistently reduces predicted toxicity rate (measured via ToxDL2) below the taxon-finetuned baseline while preserving biological plausibility. We evaluate quality using Fréchet ESM Distance and predicted foldability (pLDDT), finding that LDA maintains distributional similarity to natural proteins and structural viability (unlike activation-based steering methods that tend to degrade sequence properties). Our results demonstrate that LDA provides a practical safety knob for protein generators that mitigates elicited toxicity while retaining generative quality.", "authors": ["Manuel Fernández Burda", "Santiago Aranguri", "Iván Arcuschin Moreno", "Enzo Ferrante"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-04", "url": "https://arxiv.org/abs/2603.04045", "pdf_url": "https://arxiv.org/pdf/2603.04045v1", "arxiv_id": "2603.04045", "doi": "10.48550/arXiv.2603.04045", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4961}
{"id": "7d765f6bd5fc5ccd6161eea473c9184004ad2e915edd17dadba0cc3210530035", "sources": ["arxiv", "semantic_scholar"], "title": "Building a Strong Instruction Language Model for a Less-Resourced Language", "abstract": "Large language models (LLMs) have become an essential tool for natural language processing and artificial intelligence in general. Current open-source models are primarily trained on English texts, resulting in poorer performance on less-resourced languages and cultures. We present a set of methodological approaches necessary for the successful adaptation of an LLM to a less-resourced language, and demonstrate them using the Slovene language. We present GaMS3-12B, a generative model for Slovene with 12 billion parameters, and demonstrate that it is the best-performing open-source model for Slovene within its parameter range. We adapted the model to the Slovene language using three-stage continual pre-training of the Gemma 3 model, followed by two-stage supervised fine-tuning (SFT). We trained the model on a combination of 140B Slovene, English, Bosnian, Serbian, and Croatian pretraining tokens, and over 200 thousand English and Slovene SFT examples. We evaluate GaMS3-12B on the Slovenian-LLM-Eval datasets, English-to-Slovene translation, and the Slovene LLM arena. We show that the described model outperforms 12B Gemma 3 across all three scenarios and performs comparably to much larger commercial GPT-4o in the Slovene LLM arena, achieving a win rate of over 60 %.", "authors": ["Domen Vreš", "Tjaša Arčon", "Timotej Petrič", "Dario Vajda", "Marko Robnik-Šikonja", "Iztok Lebar Bajec"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-03-02", "url": "https://arxiv.org/abs/2603.01691", "pdf_url": "https://arxiv.org/pdf/2603.01691v1", "arxiv_id": "2603.01691", "doi": "10.48550/arXiv.2603.01691", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.7632}
{"id": "fa76891b27c573bb84ee4a7a5f133548d5e487aab28969c9776638fd8e2a5e01", "sources": ["arxiv", "semantic_scholar"], "title": "CoPeP: Benchmarking Continual Pretraining for Protein Language Models", "abstract": "Protein language models (pLMs) have recently gained significant attention for their ability to uncover relationships between sequence, structure, and function from evolutionary statistics, thereby accelerating therapeutic drug discovery. These models learn from large protein databases that are continuously updated by the biology community and whose dynamic nature motivates the application of continual learning, not only to keep up with the ever-growing data, but also as an opportunity to take advantage of the temporal meta-information that is created during this process. As a result, we introduce the Continual Pretraining of Protein Language Models (CoPeP) benchmark, a novel benchmark for evaluating continual learning approaches on pLMs. Specifically, we curate a sequence of protein datasets derived from the UniProt Knowledgebase spanning a decade and define metrics to assess pLM performance across 31 protein understanding tasks. We evaluate several methods from the continual learning literature, including replay, unlearning, and plasticity-based methods, some of which have never been applied to models and data of this scale. Our findings reveal that incorporating temporal meta-information improves perplexity by up to 7% even when compared to training on data from all tasks jointly. Moreover, even at scale, several continual learning methods outperform naive continual pretraining. The CoPeP benchmark offers an exciting opportunity to study these methods at scale in an impactful real-world application.", "authors": ["Darshan Patil", "Pranshu Malviya", "Mathieu Reymond", "Quentin Fournier", "Sarath Chandar"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-27", "url": "https://arxiv.org/abs/2603.00253", "pdf_url": "https://arxiv.org/pdf/2603.00253v2", "arxiv_id": "2603.00253", "doi": "10.48550/arXiv.2603.00253", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4904}
{"id": "cd1ae30c83a185e14356aaf886458a66daa9f1d6c5c56030eb41238790c1e750", "sources": ["arxiv", "semantic_scholar"], "title": "Protein Language Models Diverge from Natural Language: Comparative Analysis and Improved Inference", "abstract": "Modern Protein Language Models (PLMs) apply transformer-based model architectures from natural language processing to biological sequences, predicting a variety of protein functions and properties. However, protein language has key differences from natural language, such as a rich functional space despite a vocabulary of only 20 amino acids. These differences motivate research into how transformer-based architectures operate differently in the protein domain and how we can better leverage PLMs to solve protein-related tasks. In this work, we begin by directly comparing how the distribution of information stored across layers of attention heads differs between the protein and natural language domain. Furthermore, we adapt a simple early-exit technique-originally used in the natural language domain to improve efficiency at the cost of performance-to achieve both increased accuracy and substantial efficiency gains in protein non-structural property prediction by allowing the model to automatically select protein representations from the intermediate layers of the PLMs for the specific task and protein at hand. We achieve performance gains ranging from 0.4 to 7.01 percentage points while simultaneously improving efficiency by over 10 percent across models and non-structural prediction tasks. Our work opens up an area of research directly comparing how language models change behavior when moved into the protein domain and advances language modeling in biological domains.", "authors": ["Anna Hart", "Chi Han", "Jeonghwan Kim", "Huimin Zhao", "Heng Ji"], "categories": ["cs.LG", "cs.AI", "cs.CL", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2026-02-24", "url": "https://arxiv.org/abs/2602.20449", "pdf_url": "https://arxiv.org/pdf/2602.20449v1", "arxiv_id": "2602.20449", "doi": "10.48550/arXiv.2602.20449", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.487}
{"id": "fce9ef0f1765578e157b0d25acca84cc9ee92bd3dce842954e8a8c18b188401c", "sources": ["arxiv", "semantic_scholar"], "title": "BeamVLM for Low-altitude Economy: Generative Beam Prediction via Vision-language Models", "abstract": "For low-altitude economy (LAE), fast and accurate beam prediction between high-mobility unmanned aerial vehicles (UAVs) and ground base stations is of paramount importance, which ensures seamless coverage and reliable communications. However, existing deep learning-based beam prediction methods lack high-level semantic understanding of dynamic environments, resulting in poor generalization. On the other hand, the emerging large language model (LLM) based approaches show promise in enhancing generalization, but they typically lack rich environmental perception, thereby failing to capture fine-grained spatial semantics essential for precise beam alignment. To tackle these limitations, we propose in this correspondence a novel end-to-end generative framework for beam prediction, called BeamVLM, which treats beam prediction as a vision question answering task capitalizing on powerful existing vision-language models (VLMs). By projecting raw visual patches directly into the language domain and judiciously designing an instructional prompt, the proposed BeamVLM enables the VLM to jointly reason over UAV trajectories and environmental context. Last, experimental results on real-world datasets demonstrate that the proposed BeamVLM outperforms state-of-the-art methods in prediction accuracy and also exhibits superior generalization for other scenarios such as vehicle-to-infrastructure (V2I) beam prediction.", "authors": ["Chenran Kou", "Changsheng You", "Mingjiang Wu", "Dingzhu Wen", "Zezhong Zhang", "Chengwen Xing"], "categories": ["cs.NI", "cs.IT"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2026-02-23", "url": "https://arxiv.org/abs/2602.19929", "pdf_url": "https://arxiv.org/pdf/2602.19929v1", "arxiv_id": "2602.19929", "doi": "10.48550/arXiv.2602.19929", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4858}
{"id": "39dcd2d9ac944e68a62b34cb8a42d6e302188d247e477387a052da3067cc84c8", "sources": ["arxiv", "semantic_scholar"], "title": "STProtein: predicting spatial protein expression from multi-omics data", "abstract": "The integration of spatial multi-omics data from single tissues is crucial for advancing biological research. However, a significant data imbalance impedes progress: while spatial transcriptomics data is relatively abundant, spatial proteomics data remains scarce due to technical limitations and high costs. To overcome this challenge we propose STProtein, a novel framework leveraging graph neural networks with multi-task learning strategy. STProtein is designed to accurately predict unknown spatial protein expression using more accessible spatial multi-omics data, such as spatial transcriptomics. We believe that STProtein can effectively addresses the scarcity of spatial proteomics, accelerating the integration of spatial multi-omics and potentially catalyzing transformative breakthroughs in life sciences. This tool enables scientists to accelerate discovery by identifying complex and previously hidden spatial patterns of proteins within tissues, uncovering novel relationships between different marker genes, and exploring the biological \"Dark Matter\".", "authors": ["Zhaorui Jiang", "Yingfang Yuan", "Lei Hu", "Wei Pang"], "categories": ["cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-02-05", "url": "https://arxiv.org/abs/2602.05811", "pdf_url": "https://arxiv.org/pdf/2602.05811v1", "arxiv_id": "2602.05811", "doi": "10.48550/arXiv.2602.05811", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/zhaorui-bi/STProtein", "venue": "arXiv.org", "quality_score": 0.719}
{"id": "25dc729932d8178547f234c0a1b91071752207d8855a087cef99fcfa5a038908", "sources": ["arxiv", "semantic_scholar"], "title": "Controlling Repetition in Protein Language Models", "abstract": "Protein language models (PLMs) have enabled advances in structure prediction and de novo protein design, yet they frequently collapse into pathological repetition during generation. Unlike in text, where repetition merely reduces readability, in proteins it undermines structural confidence and functional viability. To unify this problem, we present the first systematic study of repetition in PLMs. We first propose quantitative metrics to characterize motif-level and homopolymer repetition and then demonstrate their negative impact on folding reliability. To address this challenge, we propose UCCS (Utility-Controlled Contrastive Steering), which steers protein generation with a constrained dataset. Instead of naively contrasting high- vs. low-repetition sequences, we construct contrastive sets that maximize differences in repetition while tightly controlling for structural utility. This disentanglement yields steering vectors that specifically target repetition without degrading foldability. Injected at inference, these vectors consistently reduce repetition without retraining or heuristic decoding. Experiments with ESM-3 and ProtGPT2 in CATH, UniRef50, and SCOP show that our method outperforms decoding penalties and other baselines, substantially lowering repetition while preserving AlphaFold confidence scores. Our results establish repetition control as a central challenge for PLMs and highlight dataset-guided steering as a principled approach for reliable protein generation.", "authors": ["Jiahao Zhang", "Zeqing Zhang", "Di Wang", "Lijie Hu"], "categories": ["q-bio.BM", "cs.AI"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2026-01-31", "url": "https://arxiv.org/abs/2602.00782", "pdf_url": "https://arxiv.org/pdf/2602.00782v1", "arxiv_id": "2602.00782", "doi": "10.48550/arXiv.2602.00782", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4595}
{"id": "3b1e983c10ad679f44efe516c25757ed09fc760ae26e3899d43f8d0b605a9300", "sources": ["arxiv", "semantic_scholar"], "title": "CalPro: Prior-Aware Evidential--Conformal Prediction with Structure-Aware Guarantees for Protein Structures", "abstract": "Deep protein structure predictors such as AlphaFold provide confidence estimates (e.g., pLDDT) that are often miscalibrated and degrade under distribution shifts across experimental modalities, temporal changes, and intrinsically disordered regions. We introduce CalPro, a prior-aware evidential-conformal framework for shift-robust uncertainty quantification. CalPro combines (i) a geometric evidential head that outputs Normal-Inverse-Gamma predictive distributions via a graph-based architecture; (ii) a differentiable conformal layer that enables end-to-end training with finite-sample coverage guarantees; and (iii) domain priors (disorder, flexibility) encoded as soft constraints. We derive structure-aware coverage guarantees under distribution shift using PAC-Bayesian bounds over ambiguity sets, and show that CalPro maintains near-nominal coverage while producing tighter intervals than standard conformal methods in regions where priors are informative. Empirically, CalPro exhibits at most 5% coverage degradation across modalities (vs. 15-25% for baselines), reduces calibration error by 30-50%, and improves downstream ligand-docking success by 25%. Beyond proteins, CalPro applies to structured regression tasks in which priors encode local reliability, validated on non-biological benchmarks.", "authors": ["Ibne Farabi Shihab", "Sanjeda Akter", "Anuj Sharma"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2026-01-12", "url": "https://arxiv.org/abs/2601.07201", "pdf_url": "https://arxiv.org/pdf/2601.07201v1", "arxiv_id": "2601.07201", "doi": "10.48550/arXiv.2601.07201", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4377}
{"id": "8c06ab9f0ef554e926895b82f6f23c06c9ccf8d15125591eaf5ba6433931edba", "sources": ["arxiv", "semantic_scholar"], "title": "Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model", "abstract": "Implicit solvent models (ISMs) promise to deliver the accuracy of explicit solvent simulations at a fraction of the computational cost. However, despite decades of development, their accuracy has remained insufficient for many critical applications, particularly for simulating protein folding and the behavior of intrinsically disordered proteins. Developing a transferable, data-driven ISM that overcomes the limitations of traditional analytical formulas remains a central challenge in computational chemistry. Here we address this challenge by introducing a novel strategy that distills the evolutionary information learned by a protein language model, ESM3, into a computationally efficient graph neural network (GNN). We show that this GNN potential, trained on effective energies from ESM3, is robust enough to drive stable, long-timescale molecular dynamics simulations. When combined with a standard electrostatics term, our hybrid model accurately reproduces protein folding free-energy landscapes and predicts the structural ensembles of intrinsically disordered proteins. This approach yields a single, unified model that is transferable across both folded and disordered protein states, resolving a long-standing limitation of conventional ISMs. By successfully distilling evolutionary knowledge into a physical potential, our work delivers a foundational implicit solvent model poised to accelerate the development of predictive, large-scale simulation tools.", "authors": ["Justin Airas", "Bin Zhang"], "categories": ["physics.bio-ph", "physics.chem-ph", "physics.comp-ph"], "fields_of_study": ["Physics", "Medicine"], "published_date": "2026-01-08", "url": "https://arxiv.org/abs/2601.05388", "pdf_url": "https://arxiv.org/pdf/2601.05388v2", "arxiv_id": "2601.05388", "doi": null, "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4331}
{"id": "8108741252aedfc85cdc48b2017260c56b8adc01e8f4b65009708ca6b1b25d1d", "sources": ["arxiv", "semantic_scholar"], "title": "Quantum Simulation of Protein Fragment Electronic Structure Using Moment-based Adaptive Variational Quantum Algorithms", "abstract": "Background: Understanding electronic interactions in protein active sites is fundamental to drug discovery and enzyme engineering, but remains computationally challenging due to exponential scaling of quantum mechanical calculations. Results: We present a quantum-classical hybrid framework for simulating protein fragment electronic structure using variational quantum algorithms. We construct fermionic Hamiltonians from experimentally determined protein structures, map them to qubits via Jordan-Wigner transformation, and optimize ground state energies using the Variational Quantum Eigensolver implemented in pure Python. For a 4-orbital serine protease fragment, we achieve chemical accuracy (< 1.6 mHartree) with 95.3% correlation energy recovery. Systematic analysis reveals three-phase convergence behaviour with exponential decay (α = 0.95), power law optimization (γ = 1.21), and asymptotic approach. Application to SARS-CoV-2 protease inhibition demonstrates predictive accuracy (MAE=0.25 kcal/mol), while cytochrome P450 metabolism predictions achieve 85% site accuracy. Conclusions: This work establishes a pathway for quantum-enhanced biomolecular simulations on near-term quantum hardware, bridging quantum algorithm development with practical biological applications.", "authors": ["Biraja Ghoshal"], "categories": ["q-bio.QM", "cs.ET"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2026-01-02", "url": "https://arxiv.org/abs/2601.00656", "pdf_url": "https://arxiv.org/pdf/2601.00656v1", "arxiv_id": "2601.00656", "doi": "10.48550/arXiv.2601.00656", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4263}
{"id": "3a3f618d9a2d8c4f089f480f98da544cf9129429dee02de1958c9b28294e2f01", "sources": ["arxiv", "semantic_scholar"], "title": "Physio-DPO: Aligning Large Language Models with the Protein Energy Landscape to Eliminate Structural Hallucinations", "abstract": "Large Protein Language Models have shown strong potential for generative protein design, yet they frequently produce structural hallucinations, generating sequences with high linguistic likelihood that fold into thermodynamically unstable conformations. Existing alignment approaches such as Direct Preference Optimization are limited in this setting, as they model preferences as binary labels and ignore the continuous structure of the physical energy landscape. We propose Physio-DPO, a physics informed alignment framework that grounds protein language models in thermodynamic stability. Physio-DPO introduces a magnitude aware objective that scales optimization updates according to the energy gap between native structures and physics perturbed hard negatives. Experiments show that Physio-DPO consistently outperforms strong baselines including SFT, PPO, and standard DPO, reducing self consistency RMSD to 1.28 Å and increasing foldability to 92.8%. Qualitative analysis further demonstrates that Physio-DPO effectively mitigates structural hallucinations by recovering biophysical interactions such as hydrophobic core packing and hydrogen bond networks.", "authors": ["QiWei Meng"], "categories": ["cs.CL", "cs.CE", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2026-01-02", "url": "https://arxiv.org/abs/2601.00647", "pdf_url": "https://arxiv.org/pdf/2601.00647v1", "arxiv_id": "2601.00647", "doi": "10.48550/arXiv.2601.00647", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4263}
{"id": "ef39d241eab949d3efe800528741c9edc69bf639b9982c7d2bc4f769fdcf64a3", "sources": ["arxiv", "semantic_scholar"], "title": "HD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens", "abstract": "Proteins inherently possess a consistent sequence-structure duality. The abundance of protein sequence data, which can be readily represented as discrete tokens, has driven fruitful developments in protein language models (pLMs). A key remaining challenge, however, is how to effectively integrate continuous structural knowledge into pLMs. Current methods often discretize protein structures to accommodate the language modeling framework, which inevitably results in the loss of fine-grained information and limits the performance potential of multimodal pLMs. In this paper, we argue that such concerns can be circumvented: a sequence-based pLM can be extended to incorporate the structure modality through continuous tokens, i.e., high-fidelity protein structure latents that avoid vector quantization. Specifically, we propose a hybrid diffusion protein language model, HD-Prot, which embeds a continuous-valued diffusion head atop a discrete pLM, enabling seamless operation with both discrete and continuous tokens for joint sequence-structure modeling. It captures inter-token dependencies across modalities through a unified absorbing diffusion process, and estimates per-token distributions via categorical prediction for sequences and continuous diffusion for structures. Extensive results demonstrate that HD-Prot achieves competitive performance in unconditional sequence-structure co-generation, motif-scaffolding, protein structure prediction, and inverse folding tasks. Furthermore, our method can perform on par with state-of-the-art multimodal pLMs, despite being developed under limited computational resources (i.e., less than one-tenth the budget for modality extension fine-tuning). It highlights the viability of simultaneously estimating categorical and continuous distributions within a unified language model architecture, offering a promising alternative direction for multimodal pLMs.", "authors": ["Yi Zhou", "Haohao Qu", "Yunqing Liu", "Shanru Lin", "Le Song", "Wenqi Fan"], "categories": ["cs.CE", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-17", "url": "https://arxiv.org/abs/2512.15133", "pdf_url": "https://arxiv.org/pdf/2512.15133v3", "arxiv_id": "2512.15133", "doi": "10.48550/arXiv.2512.15133", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4079}
{"id": "8a43a9c0833ff2879a90ef7a720ceb443aba6543f1531486d21d70e48f00eff3", "sources": ["arxiv", "semantic_scholar"], "title": "Large language models have learned to use language", "abstract": "Acknowledging that large language models have learned to use language can open doors to breakthrough language science. Achieving these breakthroughs may require abandoning some long-held ideas about how language knowledge is evaluated and reckoning with the difficult fact that we have entered a post-Turing test era.", "authors": ["Gary Lupyan"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-13", "url": "https://arxiv.org/abs/2512.12447", "pdf_url": "https://arxiv.org/pdf/2512.12447v1", "arxiv_id": "2512.12447", "doi": "10.48550/arXiv.2512.12447", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4033}
{"id": "3274b8e6361e5f626a23dcdf3d21269828073d7b99bef850e3fd10e198486cf9", "sources": ["arxiv", "semantic_scholar"], "title": "Self Distillation Fine-Tuning of Protein Language Models Improves Versatility in Protein Design", "abstract": "Supervised fine-tuning (SFT) is a standard approach for adapting large language models to specialized domains, yet its application to protein sequence modeling and protein language models (PLMs) remains ad hoc. This is in part because high-quality annotated data are far more difficult to obtain for proteins than for natural language. We present a simple and general recipe for fast SFT of PLMs, designed to improve the fidelity, reliability, and novelty of generated protein sequences. Unlike existing approaches that require costly precompiled experimental datasets for SFT, our method leverages the PLM itself, integrating a lightweight curation pipeline with domain-specific filters to construct high-quality training data. These filters can independently refine a PLM's output and identify candidates for in vitro evaluation; when combined with SFT, they enable PLMs to generate more stable and functional enzymes, while expanding exploration into protein sequence space beyond natural variants. Although our approach is agnostic to both the choice of protein language model (PLM) and the protein system, we demonstrate its effectiveness with a genome-scale PLM (GenSLM) applied to the tryptophan synthase enzyme family. The supervised fine-tuned model generates sequences that are not only more novel but also display improved characteristics across both targeted design constraints and emergent protein property measures.", "authors": ["Amin Tavakoli", "Raswanth Murugan", "Ozan Gokdemir", "Arvind Ramanathan", "Frances Arnold", "Anima Anandkumar"], "categories": ["cs.LG", "cs.CE"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-10", "url": "https://arxiv.org/abs/2512.09329", "pdf_url": "https://arxiv.org/pdf/2512.09329v1", "arxiv_id": "2512.09329", "doi": "10.48550/arXiv.2512.09329", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3999}
{"id": "84809160d7b398c91a840edeca946d2c754922ae674dc128c66faaf21cbac684", "sources": ["arxiv", "semantic_scholar"], "title": "Soft Inductive Bias Approach via Explicit Reasoning Perspectives in Inappropriate Utterance Detection Using Large Language Models", "abstract": "Recent incidents in certain online games and communities, where anonymity is guaranteed, show that unchecked inappropriate remarks frequently escalate into verbal abuse and even criminal behavior, raising significant social concerns. Consequently, there is a growing need for research on techniques that can detect inappropriate utterances within conversational texts to help build a safer communication environment. Although large-scale language models trained on Korean corpora and chain-of-thought reasoning have recently gained attention, research applying these approaches to inappropriate utterance detection remains limited. In this study, we propose a soft inductive bias approach that explicitly defines reasoning perspectives to guide the inference process, thereby promoting rational decision-making and preventing errors that may arise during reasoning. We fine-tune a Korean large language model using the proposed method and conduct both quantitative performance comparisons and qualitative evaluations across different training strategies. Experimental results show that the Kanana-1.5 model achieves an average accuracy of 87.0046, improving by approximately 3.89 percent over standard supervised learning. These findings indicate that the proposed method goes beyond simple knowledge imitation by large language models and enables more precise and consistent judgments through constrained reasoning perspectives, demonstrating its effectiveness for inappropriate utterance detection.", "authors": ["Ju-Young Kim", "Ji-Hong Park", "Se-Yeon Lee", "Sujin Park", "Gun-Woo Kim"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-09", "url": "https://arxiv.org/abs/2512.08480", "pdf_url": "https://arxiv.org/pdf/2512.08480v1", "arxiv_id": "2512.08480", "doi": "10.48550/arXiv.2512.08480", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3987}
{"id": "9ba643e751032fea310460ad18dd169975fd9a5b6f0e49b49795abedebb99662", "sources": ["arxiv", "semantic_scholar"], "title": "Protein Secondary Structure Prediction Using Transformers", "abstract": "Predicting protein secondary structures such as alpha helices, beta sheets, and coils from amino acid sequences is essential for understanding protein function. This work presents a transformer-based model that applies attention mechanisms to protein sequence data to predict structural motifs. A sliding-window data augmentation technique is used on the CB513 dataset to expand the training samples. The transformer shows strong ability to generalize across variable-length sequences while effectively capturing both local and long-range residue interactions.", "authors": ["Manzi Kevin Maxime"], "categories": ["cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-09", "url": "https://arxiv.org/abs/2512.08613", "pdf_url": "https://arxiv.org/pdf/2512.08613v1", "arxiv_id": "2512.08613", "doi": "10.48550/arXiv.2512.08613", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3987}
{"id": "99d189816a72fcfcddc3b3cdacae324df68e34ffef6119675ee59b392a606c82", "sources": ["arxiv", "semantic_scholar"], "title": "Classifying German Language Proficiency Levels Using Large Language Models", "abstract": "Assessing language proficiency is essential for education, as it enables instruction tailored to learners needs. This paper investigates the use of Large Language Models (LLMs) for automatically classifying German texts according to the Common European Framework of Reference for Languages (CEFR) into different proficiency levels. To support robust training and evaluation, we construct a diverse dataset by combining multiple existing CEFR-annotated corpora with synthetic data. We then evaluate prompt-engineering strategies, fine-tuning of a LLaMA-3-8B-Instruct model and a probing-based approach that utilizes the internal neural state of the LLM for classification. Our results show a consistent performance improvement over prior methods, highlighting the potential of LLMs for reliable and scalable CEFR classification.", "authors": ["Elias-Leander Ahlers", "Witold Brunsmann", "Malte Schilling"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-12-06", "url": "https://arxiv.org/abs/2512.06483", "pdf_url": "https://arxiv.org/pdf/2512.06483v1", "arxiv_id": "2512.06483", "doi": "10.1109/FLLM67465.2025.11390912", "citation_count": 1, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2516}
{"id": "edde90edeaeb66075f25b08e388d7a8200913a436424fafe35130994ca6732d5", "sources": ["arxiv", "semantic_scholar"], "title": "Small Language Models Reshape Higher Education: Courses, Textbooks, and Teaching", "abstract": "While large language models (LLMs) have introduced novel paradigms in science and education, their adoption in higher education is constrained by inherent limitations. These include a tendency to produce inaccuracies and high computational requirements, which compromise the strict demands for accurate and reliable knowledge essential in higher education. Small language models (MiniLMs), by contrast, offer distinct advantages in professional education due to their lightweight nature and precise retrieval capabilities. This research takes \"Atmospheric Physics\" as an example. We established a specialized corpus and image repository by gathering over 550,000 full-text PDFs from over 130 international well-respected journals in Earth and environmental science. From this collection, we extracted over 100 million high-quality sentence-level corpus and more than 3 million high-resolution academic images. Using MiniLMs, these resources were organized into a high-dimensional vector library for precise retrieval and efficient utilization of extensive educational content. Consequently, we systematically redesigned the courses, textbooks, and teaching strategies for \"Atmospheric Physics\" based on MiniLMs. The course is designed as a \"interdisciplinary-frontier\" system, breaking down traditional boundaries between atmospheric science, space science, hydrology, and remote sensing. Teaching materials are transformed from static, lagging text formats into a dynamic digital resource library powered by MiniLM. For teaching methods, we have designed a question-based learning pathway. This paradigm promotes a shift from passive knowledge transfer to active cognitive development. Consequently, this MiniLM-driven \"Atmospheric Physics\" course demonstrates a specific avenue for \"AI for education\".", "authors": ["Jian Zhang", "Jia Shao"], "categories": ["physics.ed-ph", "cs.CL"], "fields_of_study": ["Physics", "Computer Science"], "published_date": "2025-12-02", "url": "https://arxiv.org/abs/2512.06001", "pdf_url": "https://arxiv.org/pdf/2512.06001v1", "arxiv_id": "2512.06001", "doi": "10.48550/arXiv.2512.06001", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3907}
{"id": "a0d097ff2267023d9e4988fc24ed75aa95ef7a815bdd28db41101eee7a0dfc6f", "sources": ["arxiv", "semantic_scholar"], "title": "Layer Probing Improves Kinase Functional Prediction with Protein Language Models", "abstract": "Protein language models (PLMs) have transformed sequence-based protein analysis, yet most applications rely only on final-layer embeddings, which may overlook biologically meaningful information encoded in earlier layers. We systematically evaluate all 33 layers of ESM-2 for kinase functional prediction using both unsupervised clustering and supervised classification. We show that mid-to-late transformer layers (layers 20-33) outperform the final layer by 32 percent in unsupervised Adjusted Rand Index and improve homology-aware supervised accuracy to 75.7 percent. Domain-level extraction, calibrated probability estimates, and a reproducible benchmarking pipeline further strengthen reliability. Our results demonstrate that transformer depth contains functionally distinct biological signals and that principled layer selection significantly improves kinase function prediction.", "authors": ["Ajit Kumar", "IndraPrakash Jha"], "categories": ["q-bio.QM", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-11-29", "url": "https://arxiv.org/abs/2512.00376", "pdf_url": "https://arxiv.org/pdf/2512.00376v1", "arxiv_id": "2512.00376", "doi": "10.48550/arXiv.2512.00376", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3873}
{"id": "a99edac5fb61c22f532891c8c445ed6b3de89af4abe31d2f6d1e5946f58699c1", "sources": ["arxiv", "semantic_scholar"], "title": "Language-conditioned world model improves policy generalization by reading environmental descriptions", "abstract": "To interact effectively with humans in the real world, it is important for agents to understand language that describes the dynamics of the environment--that is, how the environment behaves--rather than just task instructions specifying \"what to do\". Understanding this dynamics-descriptive language is important for human-agent interaction and agent behavior. Recent work address this problem using a model-based approach: language is incorporated into a world model, which is then used to learn a behavior policy. However, these existing methods either do not demonstrate policy generalization to unseen games or rely on limiting assumptions. For instance, assuming that the latency induced by inference-time planning is tolerable for the target task or expert demonstrations are available. Expanding on this line of research, we focus on improving policy generalization from a language-conditioned world model while dropping these assumptions. We propose a model-based reinforcement learning approach, where a language-conditioned world model is trained through interaction with the environment, and a policy is learned from this model--without planning or expert demonstrations. Our method proposes Language-aware Encoder for Dreamer World Model (LED-WM) built on top of DreamerV3. LED-WM features an observation encoder that uses an attention mechanism to explicitly ground language descriptions to entities in the observation. We show that policies trained with LED-WM generalize more effectively to unseen games described by novel dynamics and language compared to other baselines in several settings in two environments: MESSENGER and MESSENGER-WM.To highlight how the policy can leverage the trained world model before real-world deployment, we demonstrate the policy can be improved through fine-tuning on synthetic test trajectories generated by the world model.", "authors": ["Anh Nguyen", "Stefan Lee"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-28", "url": "https://arxiv.org/abs/2511.22904", "pdf_url": "https://arxiv.org/pdf/2511.22904v1", "arxiv_id": "2511.22904", "doi": "10.48550/arXiv.2511.22904", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3861}
{"id": "1d2eefd3f45c2512327996cda188ad850678166a12210efbdc9fa5ea932f8663", "sources": ["arxiv", "semantic_scholar"], "title": "Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validation", "abstract": "Designing proteins de novo with tailored structural, physicochemical, and functional properties remains a grand challenge in biotechnology, medicine, and materials science, due to the vastness of sequence space and the complex coupling between sequence, structure, and function. Current state-of-the-art generative methods, such as protein language models (PLMs) and diffusion-based architectures, often require extensive fine-tuning, task-specific data, or model reconfiguration to support objective-directed design, thereby limiting their flexibility and scalability. To overcome these limitations, we present a decentralized, agent-based framework inspired by swarm intelligence for de novo protein design. In this approach, multiple large language model (LLM) agents operate in parallel, each assigned to a specific residue position. These agents iteratively propose context-aware mutations by integrating design objectives, local neighborhood interactions, and memory and feedback from previous iterations. This position-wise, decentralized coordination enables emergent design of diverse, well-defined sequences without reliance on motif scaffolds or multiple sequence alignments, validated with experiments on proteins with alpha helix and coil structures. Through analyses of residue conservation, structure-based metrics, and sequence convergence and embeddings, we demonstrate that the framework exhibits emergent behaviors and effective navigation of the protein fitness landscape. Our method achieves efficient, objective-directed designs within a few GPU-hours and operates entirely without fine-tuning or specialized training, offering a generalizable and adaptable solution for protein design. Beyond proteins, the approach lays the groundwork for collective LLM-driven design across biomolecular systems and other scientific discovery tasks.", "authors": ["Fiona Y. Wang", "Di Sheng Lee", "David L. Kaplan", "Markus J. Buehler"], "categories": ["cs.AI", "cond-mat.mes-hall", "cond-mat.soft", "cs.CL", "cs.LG"], "fields_of_study": ["Computer Science", "Physics"], "published_date": "2025-11-27", "url": "https://arxiv.org/abs/2511.22311", "pdf_url": "https://arxiv.org/pdf/2511.22311v1", "arxiv_id": "2511.22311", "doi": "10.48550/arXiv.2511.22311", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.385}
{"id": "8a89f3b0e767124b3ecac378cd663d2380ea5d550ae836716db1baf52997f70a", "sources": ["arxiv", "semantic_scholar"], "title": "DeepPNI: Language- and graph-based model for mutation-driven protein-nucleic acid energetics", "abstract": "The interaction between proteins and nucleic acids is crucial for processes that sustain cellular function, including DNA maintenance and the regulation of gene expression and translation. Amino acid mutations in protein-nucleic acid complexes often lead to vital diseases. Experimental techniques have their own specific limitations in predicting mutational effects in protein-nucleic acid complexes. In this study, we compiled a large dataset of 1951 mutations including both protein-DNA and protein-RNA complexes and integrated structural and sequential features to build a deep learning-based regression model named DeepPNI. This model estimates mutation-induced binding free energy changes in protein-nucleic acid complexes. The structural features are encoded via edge-aware RGCN and the sequential features are extracted using protein language model ESM-2. We have achieved a high average Pearson correlation coefficient (PCC) of 0.76 in the large dataset via five-fold cross-validation. Consistent performance across individual dataset of protein-DNA, protein-RNA complexes, and different experimental temperature split dataset make the model generalizable. Our model showed good performance in complex-based five-fold cross-validation, which proved its robustness. In addition, DeepPNI outperformed in external dataset validation, and comparison with existing tools", "authors": ["Somnath Mondal", "Tinkal Mondal", "Soumajit Pramanik", "Rukmankesh Mehra"], "categories": ["q-bio.BM", "cs.AI"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2025-11-27", "url": "https://arxiv.org/abs/2511.22239", "pdf_url": "https://arxiv.org/pdf/2511.22239v1", "arxiv_id": "2511.22239", "doi": "10.48550/arXiv.2511.22239", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.385}
{"id": "b5353a619eaea8460535a73e12d9d3852e5dd4eb66907753d18382b152f074cc", "sources": ["arxiv", "semantic_scholar"], "title": "Protein Secondary Structure Prediction Using 3D Graphs and Relation-Aware Message Passing Transformers", "abstract": "In this study, we tackle the challenging task of predicting secondary structures from protein primary sequences, a pivotal initial stride towards predicting tertiary structures, while yielding crucial insights into protein activity, relationships, and functions. Existing methods often utilize extensive sets of unlabeled amino acid sequences. However, these approaches neither explicitly capture nor harness the accessible protein 3D structural data, which is recognized as a decisive factor in dictating protein functions. To address this, we utilize protein residue graphs and introduce various forms of sequential or structural connections to capture enhanced spatial information. We adeptly combine Graph Neural Networks (GNNs) and Language Models (LMs), specifically utilizing a pre-trained transformer-based protein language model to encode amino acid sequences and employing message-passing mechanisms like GCN and R-GCN to capture geometric characteristics of protein structures. Employing convolution within a specific node's nearby region, including relations, we stack multiple convolutional layers to efficiently learn combined insights from the protein's spatial graph, revealing intricate interconnections and dependencies in its structural arrangement. To assess our model's performance, we employed the training dataset provided by NetSurfP-2.0, which outlines secondary structure in 3-and 8-states. Extensive experiments show that our proposed model, SSRGNet surpasses the baseline on f1-scores.", "authors": ["Disha Varshney", "Samarth Garg", "Sarthak Tyagi", "Deeksha Varshney", "Nayan Deep", "Asif Ekbal"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-17", "url": "https://arxiv.org/abs/2511.13685", "pdf_url": "https://arxiv.org/pdf/2511.13685v1", "arxiv_id": "2511.13685", "doi": "10.48550/arXiv.2511.13685", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3735}
{"id": "859d10bcf2c0d311d4ca6c85f4ab9d3caf30455d644e046df07579d451de2c74", "sources": ["arxiv", "semantic_scholar"], "title": "Studies with impossible languages falsify LMs as models of human language", "abstract": "According to Futrell and Mahowald [arXiv:2501.17047], both infants and language models (LMs) find attested languages easier to learn than impossible languages that have unnatural structures. We review the literature and show that LMs often learn attested and many impossible languages equally well. Difficult to learn impossible languages are simply more complex (or random). LMs are missing human inductive biases that support language acquisition.", "authors": ["Jeffrey S. Bowers", "Jeff Mitchell"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-14", "url": "https://arxiv.org/abs/2511.11389", "pdf_url": "https://arxiv.org/pdf/2511.11389v1", "arxiv_id": "2511.11389", "doi": "10.48550/arXiv.2511.11389", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3701}
{"id": "f90693cfcb34bb06f6da918410e6071af0a85d07f8c013ffed0acd13a591e1dc", "sources": ["arxiv", "semantic_scholar"], "title": "Boosting In-Silicon Directed Evolution with Fine-Tuned Protein Language Model and Tree Search", "abstract": "Protein evolution through amino acid mutations is a cornerstone of life sciences. Recent advances in protein language models have shown rich evolutionary patterns, offering unprecedented potential for in-silicon directed evolution. However, existing directed evolution methods largely rely on heuristic evolution strategies and have yet to efficiently integrate the transformative protein language models with advanced optimization techniques, such as reinforcement learning, to adaptively learn superior evolution policies. To bridge this gap, we propose AlphaDE, a novel framework that evolves protein sequences by harnessing the innovative paradigms of large language models, such as fine-tuning and test-time inference. First, AlphaDE fine-tunes pretrained protein language models using masked language modeling on homologous protein sequences to activate the evolutionary plausibility of the interested protein family. Second, AlphaDE introduces test-time inference based on Monte Carlo tree search, which effectively evolves proteins with evolutionary guidance from the fine-tuned protein language model. Extensive benchmark experiments show that AlphaDE remarkably outperforms previous state-of-the-art methods even with few-shot fine-tuning. A case study further demonstrates that AlphaDE supports condensing the protein sequence space of avGFP through computational evolution.", "authors": ["Yaodong Yang", "Yang Wang", "Jinpeng Li", "Pei Guo", "Da Han", "Guangyong Chen", "Pheng-Ann Heng"], "categories": ["cs.AI", "cs.CE"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-13", "url": "https://arxiv.org/abs/2511.09900", "pdf_url": "https://arxiv.org/pdf/2511.09900v4", "arxiv_id": "2511.09900", "doi": "10.48550/arXiv.2511.09900", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.369}
{"id": "db887845bcd21311022f915d4f4cb72543394233d12ba77db512a8983536728d", "sources": ["arxiv", "semantic_scholar"], "title": "From Static Structures to Ensembles: Studying and Harnessing Protein Structure Tokenization", "abstract": "Protein structure tokenization converts 3D structures into discrete or vectorized representations, enabling the integration of structural and sequence data. Despite many recent works on structure tokenization, the properties of the underlying discrete representations are not well understood. In this work, we first demonstrate that the successful utilization of structural tokens in a language model for structure prediction depends on using rich, pre-trained sequence embeddings to bridge the semantic gap between the sequence and structural \"language\". The analysis of the structural vocabulary itself then reveals significant semantic redundancy, where multiple distinct tokens correspond to nearly identical local geometries, acting as \"structural synonyms\". This redundancy, rather than being a flaw, can be exploited with a simple \"synonym swap\" strategy to generate diverse conformational ensembles by perturbing a predicted structure with its structural synonyms. This computationally lightweight method accurately recapitulates protein flexibility, performing competitively with state-of-the-art models. Our study provides fundamental insights into the nature of discrete protein structure representations and introduces a powerful, near-instantaneous method for modeling protein dynamics. Source code is available in https://github.com/IDEA-XL/TokenMD.", "authors": ["Zijing Liu", "Bin Feng", "He Cao", "Yu Li"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-13", "url": "https://arxiv.org/abs/2511.10056", "pdf_url": "https://arxiv.org/pdf/2511.10056v1", "arxiv_id": "2511.10056", "doi": "10.48550/arXiv.2511.10056", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/IDEA-XL/TokenMD", "venue": "arXiv.org", "quality_score": 0.5702}
{"id": "fb4597c9e9b5aabf4aadfd5fe87bb078c02f418eb222ac34b819275b6da1526e", "sources": ["arxiv", "semantic_scholar"], "title": "Joint Design of Protein Surface and Structure Using a Diffusion Bridge Model", "abstract": "Protein-protein interactions (PPIs) are governed by surface complementarity and hydrophobic interactions at protein interfaces. However, designing diverse and physically realistic protein structure and surfaces that precisely complement target receptors remains a significant challenge in computational protein design. In this work, we introduce PepBridge, a novel framework for the joint design of protein surface and structure that seamlessly integrates receptor surface geometry and biochemical properties. Starting with a receptor surface represented as a 3D point cloud, PepBridge generates complete protein structures through a multi-step process. First, it employs denoising diffusion bridge models (DDBMs) to map receptor surfaces to ligand surfaces. Next, a multi-model diffusion model predicts the corresponding structure, while Shape-Frame Matching Networks ensure alignment between surface geometry and backbone architecture. This integrated approach facilitates surface complementarity, conformational stability, and chemical feasibility. Extensive validation across diverse protein design scenarios demonstrates PepBridge's efficacy in generating structurally viable proteins, representing a significant advancement in the joint design of top-down protein structure.", "authors": ["Guanlue Li", "Xufeng Zhao", "Fang Wu", "Sören Laue"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-11-08", "url": "https://arxiv.org/abs/2511.16675", "pdf_url": "https://arxiv.org/pdf/2511.16675v1", "arxiv_id": "2511.16675", "doi": "10.48550/arXiv.2511.16675", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3632}
{"id": "3ae6593ec242f09e125cff955f93079e9f444726bd1bbd13887809491bd31d73", "sources": ["arxiv", "semantic_scholar"], "title": "Quantifying the Role of OpenFold Components in Protein Structure Prediction", "abstract": "Models such as AlphaFold2 and OpenFold have transformed protein structure prediction, yet their inner workings remain poorly understood. We present a methodology to systematically evaluate the contribution of individual OpenFold components to structure prediction accuracy. We identify several components that are critical for most proteins, while others vary in importance across proteins. We further show that the contribution of several components is correlated with protein length. These findings provide insight into how OpenFold achieves accurate predictions and highlight directions for interpreting protein prediction networks more broadly.", "authors": ["Tyler L. Hayes", "Giri P. Krishnan"], "categories": ["q-bio.BM", "cs.AI"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2025-11-06", "url": "https://arxiv.org/abs/2511.14781", "pdf_url": "https://arxiv.org/pdf/2511.14781v1", "arxiv_id": "2511.14781", "doi": "10.48550/arXiv.2511.14781", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3609}
{"id": "196d7f0b69a25eb0d0446bc422d71c79a3a9a9a39a01cb2c7361217e9b974e66", "sources": ["arxiv", "semantic_scholar"], "title": "GeoPep: A geometry-aware masked language model for protein-peptide binding site prediction", "abstract": "Multimodal approaches that integrate protein structure and sequence have achieved remarkable success in protein-protein interface prediction. However, extending these methods to protein-peptide interactions remains challenging due to the inherent conformational flexibility of peptides and the limited availability of structural data that hinder direct training of structure-aware models. To address these limitations, we introduce GeoPep, a novel framework for peptide binding site prediction that leverages transfer learning from ESM3, a multimodal protein foundation model. GeoPep fine-tunes ESM3's rich pre-learned representations from protein-protein binding to address the limited availability of protein-peptide binding data. The fine-tuned model is further integrated with a parameter-efficient neural network architecture capable of learning complex patterns from sparse data. Furthermore, the model is trained using distance-based loss functions that exploit 3D structural information to enhance binding site prediction. Comprehensive evaluations demonstrate that GeoPep significantly outperforms existing methods in protein-peptide binding site prediction by effectively capturing sparse and heterogeneous binding patterns.", "authors": ["Dian Chen", "Yunkai Chen", "Tong Lin", "Sijie Chen", "Xiaolin Cheng"], "categories": ["eess.SP", "cs.LG"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-10-30", "url": "https://arxiv.org/abs/2510.27040", "pdf_url": "https://arxiv.org/pdf/2510.27040v1", "arxiv_id": "2510.27040", "doi": "10.48550/arXiv.2510.27040", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3529}
{"id": "4f5c67c8776219add5efccfd1dd935c1f2d5cc687012b88b01ecadc3093d86be", "sources": ["arxiv", "semantic_scholar"], "title": "Precision Design of Cyclic Peptides using AlphaFold", "abstract": "This independent research investigates methods to improve the precision of cyclic peptide generation targeting the HIV gp120 trimer using AlphaFold. The study explores proximity-based hotspot mapping at the CD4 binding site, centroid distance penalization, generative loss tuning, and custom loss function development. These enhancements produced cyclic peptides that closely resemble the binding conformation of the CD4 attachment inhibitor BMS-818251. The proposed methodology demonstrates improved structural control and precision in cyclic peptide generation, advancing the applicability of AlphaFold in structure-based drug discovery.", "authors": ["Cheuk Sau Au"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2025-10-15", "url": "https://arxiv.org/abs/2510.13127", "pdf_url": "https://arxiv.org/pdf/2510.13127v1", "arxiv_id": "2510.13127", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2136}
{"id": "9f4b6fd0003622d2b0f41321ed3835c8a74e1588375112fe263338aa82471af1", "sources": ["arxiv", "semantic_scholar"], "title": "Protein as a Second Language for LLMs", "abstract": "Deciphering the function of unseen protein sequences is a fundamental challenge with broad scientific impact, yet most existing methods depend on task-specific adapters or large-scale supervised fine-tuning. We introduce the \"Protein-as-Second-Language\" framework, which reformulates amino-acid sequences as sentences in a novel symbolic language that large language models can interpret through contextual exemplars. Our approach adaptively constructs sequence-question-answer triples that reveal functional cues in a zero-shot setting, without any further training. To support this process, we curate a bilingual corpus of 79,926 protein-QA instances spanning attribute prediction, descriptive understanding, and extended reasoning. Empirically, our method delivers consistent gains across diverse open-source LLMs and GPT-4, achieving up to 17.2% ROUGE-L improvement (average +7%) and even surpassing fine-tuned protein-specific language models. These results highlight that generic LLMs, when guided with protein-as-language cues, can outperform domain-specialized models, offering a scalable pathway for protein understanding in foundation models.", "authors": ["Xinhui Chen", "Zuchao Li", "Mengqi Gao", "Yufeng Zhang", "Chak Tou Leong", "Haoyang Li", "Jiaqi Chen"], "categories": ["cs.LG", "cs.AI", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-10-13", "url": "https://arxiv.org/abs/2510.11188", "pdf_url": "https://arxiv.org/pdf/2510.11188v1", "arxiv_id": "2510.11188", "doi": "10.48550/arXiv.2510.11188", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.5153}
{"id": "cfaabc6209ae2cfb268efc15bbadb592d452e9c2e47c2e08bf452883fabc0bac", "sources": ["arxiv", "semantic_scholar"], "title": "A Hybrid Quantum-AI Framework for Protein Structure Prediction on NISQ Devices", "abstract": "Variational quantum algorithms provide a direct, physics-based approach to protein structure prediction, but their accuracy is limited by the coarse resolution of the energy landscapes generated on current noisy devices. We propose a hybrid framework that combines quantum computation with deep learning, formulating structure prediction as a problem of energy fusion. Candidate conformations are obtained through the Variational Quantum Eigensolver (VQE) executed on IBM's 127-qubit superconducting processor, which defines a global yet low-resolution quantum energy surface. To refine these basins, secondary structure probabilities and dihedral angle distributions predicted by the NSP3 neural network are incorporated as statistical potentials. These additional terms sharpen the valleys of the quantum landscape, resulting in a fused energy function that enhances effective resolution and better distinguishes native-like structures. Evaluation on 375 conformations from 75 protein fragments shows consistent improvements over AlphaFold3, ColabFold, and quantum-only predictions, achieving a mean RMSD of 4.9 Å with statistical significance (p < 0.001). The findings demonstrate that energy fusion offers a systematic method for combining data-driven models with quantum algorithms, improving the practical applicability of near-term quantum computing to molecular and structural biology.", "authors": ["Yuqi Zhang", "Yuxin Yang", "Feixiong Chen", "Cheng-Chang Lu", "Nima Saeidi", "Samuel L. Volchenboum", "Junhan Zhao", "Siwei Chen", "Weiwen Jiang", "Qiang Guan"], "categories": ["cs.ET"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-07", "url": "https://arxiv.org/abs/2510.06413", "pdf_url": "https://arxiv.org/pdf/2510.06413v1", "arxiv_id": "2510.06413", "doi": "10.48550/arXiv.2510.06413", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3266}
{"id": "bd93db712752904771ffc9457b5979fcaf5d6841220c1b7e758c14eea6542406", "sources": ["arxiv", "semantic_scholar"], "title": "Attending on Multilevel Structure of Proteins enables Accurate Prediction of Cold-Start Drug-Target Interactions", "abstract": "Cold-start drug-target interaction (DTI) prediction focuses on interaction between novel drugs and proteins. Previous methods typically learn transferable interaction patterns between structures of drug and proteins to tackle it. However, insight from proteomics suggest that protein have multi-level structures and they all influence the DTI. Existing works usually represent protein with only primary structures, limiting their ability to capture interactions involving higher-level structures. Inspired by this insight, we propose ColdDTI, a framework attending on protein multi-level structure for cold-start DTI prediction. We employ hierarchical attention mechanism to mine interaction between multi-level protein structures (from primary to quaternary) and drug structures at both local and global granularities. Then, we leverage mined interactions to fuse structure representations of different levels for final prediction. Our design captures biologically transferable priors, avoiding the risk of overfitting caused by excessive reliance on representation learning. Experiments on benchmark datasets demonstrate that ColdDTI consistently outperforms previous methods in cold-start settings.", "authors": ["Ziying Zhang", "Yaqing Wang", "Yuxuan Sun", "Min Ye", "Quanming Yao"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-05", "url": "https://arxiv.org/abs/2510.04126", "pdf_url": "https://arxiv.org/pdf/2510.04126v1", "arxiv_id": "2510.04126", "doi": "10.48550/arXiv.2510.04126", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3243}
{"id": "997e330af66c5a44c97b0a05f0f23917a51a2ac6948203afda211d53201b5e66", "sources": ["arxiv", "semantic_scholar"], "title": "Model Merging to Maintain Language-Only Performance in Developmentally Plausible Multimodal Models", "abstract": "State-of-the-art vision-and-language models consist of many parameters and learn from enormous datasets, surpassing the amounts of linguistic data that children are exposed to as they acquire a language. This paper presents our approach to the multimodal track of the BabyLM challenge addressing this discrepancy. We develop language-only and multimodal models in low-resource settings using developmentally plausible datasets, with our multimodal models outperforming previous BabyLM baselines. One finding in the multimodal language model literature is that these models tend to underperform in \\textit{language-only} tasks. Therefore, we focus on maintaining language-only abilities in multimodal models. To this end, we experiment with \\textit{model merging}, where we fuse the parameters of multimodal models with those of language-only models using weighted linear interpolation. Our results corroborate the findings that multimodal models underperform in language-only benchmarks that focus on grammar, and model merging with text-only models can help alleviate this problem to some extent, while maintaining multimodal performance.", "authors": ["Ece Takmaz", "Lisa Bylinina", "Jakub Dotlacil"], "categories": ["cs.CL", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-10-02", "url": "https://arxiv.org/abs/2510.01845", "pdf_url": "https://arxiv.org/pdf/2510.01845v1", "arxiv_id": "2510.01845", "doi": "10.48550/arXiv.2510.01845", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2042}
{"id": "8aac8fc547a50e6f6816a05a13d9322940af5e1daf5af4043c434a5e9b506b40", "sources": ["arxiv", "semantic_scholar"], "title": "Type-Less yet Type-Aware Inductive Link Prediction with Pretrained Language Models", "abstract": "Inductive link prediction is emerging as a key paradigm for real-world knowledge graphs (KGs), where new entities frequently appear and models must generalize to them without retraining. Predicting links in a KG faces the challenge of guessing previously unseen entities by leveraging generalizable node features such as subgraph structure, type annotations, and ontological constraints. However, explicit type information is often lacking or incomplete. Even when available, type information in most KGs is often coarse-grained, sparse, and prone to errors due to human annotation. In this work, we explore the potential of pre-trained language models (PLMs) to enrich node representations with implicit type signals. We introduce TyleR, a Type-less yet type-awaRe approach for subgraph-based inductive link prediction that leverages PLMs for semantic enrichment. Experiments on standard benchmarks demonstrate that TyleR outperforms state-of-the-art baselines in scenarios with scarce type annotations and sparse graph connectivity. To ensure reproducibility, we share our code at https://github.com/sisinflab/tyler .", "authors": ["Alessandro De Bellis", "Salvatore Bufi", "Giovanni Servedio", "Vito Walter Anelli", "Tommaso Di Noia", "Eugenio Di Sciascio"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-09-30", "url": "https://arxiv.org/abs/2509.26224", "pdf_url": "https://arxiv.org/pdf/2509.26224v1", "arxiv_id": "2509.26224", "doi": "10.48550/arXiv.2509.26224", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/sisinflab/tyler", "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.4923}
{"id": "c68474d09149161b5097adb4aae09f1a6303eed62ff697027b51f0ce86025856", "sources": ["arxiv", "semantic_scholar"], "title": "LAMP-PRo: Label-aware Attention for Multi-label Prediction of DNA- and RNA-binding Proteins using Protein Language Models", "abstract": "Identifying DNA- (DBPs) and RNA-binding proteins (RBPs) is crucial for the understanding of cell function, molecular interactions as well as regulatory functions. Owing to their high similarity, most of the existing approaches face challenges in differentiating between DBPs and RBPs leading to high cross-prediction errors. Moreover, identifying proteins which bind to both DNA and RNA (DRBPs) is also quite a challenging task. In this regard, we propose a novel framework viz. LAMP-PRo which is based on pre-trained protein language model (PLM), attention mechanisms and multi-label learning to mitigate these issues. First, pre-trained PLM such ESM-2 is used for embedding the protein sequences followed by convolutional neural network (CNN). Subsequently multi-head self-attention mechanism is applied for the contextual information while label-aware attention is used to compute class-specific representations by attending to the sequence in a way that is tailored to each label (DBP, RBP and non-NABP) in a multi-label setup. We have also included a novel cross-label attention mechanism to explicitly capture dependencies between DNA- and RNA-binding proteins, enabling more accurate prediction of DRBP. Finally, a linear layer followed by a sigmoid function are used for the final prediction. Extensive experiments are carried out to compare LAMP-PRo with the existing methods wherein the proposed model shows consistent competent performance. Furthermore, we also provide visualization to showcase model interpretability, highlighting which parts of the sequence are most relevant for a predicted label. The original datasets are available at http://bliulab.net/iDRBP\\_MMC and the codes are available at https://github.com/NimishaGhosh/LAMP-PRo.", "authors": ["Nimisha Ghosh", "Dheeran Sankaran", "Rahul Balakrishnan Adhi", "Sharath S", "Amrut Anand"], "categories": ["q-bio.QM", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-09-29", "url": "https://arxiv.org/abs/2509.24262", "pdf_url": "https://arxiv.org/pdf/2509.24262v2", "arxiv_id": "2509.24262", "doi": "10.48550/arXiv.2509.24262", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/NimishaGhosh/LAMP-PRo", "venue": "arXiv.org", "quality_score": 0.4905}
{"id": "60930b7601b2044af93819ee3ae0a1b7afd4c36d360c05d42914fcc934902aa8", "sources": ["arxiv", "semantic_scholar"], "title": "Twin Peaks: Dual-Head Architecture for Structure-Free Prediction of Protein-Protein Binding Affinity and Mutation Effects", "abstract": "We present a novel dual-head deep learning architecture for protein-protein interaction modeling that enables simultaneous prediction of binding affinity ($ΔG$) and mutation-induced affinity changes ($ΔΔG$) using only protein sequence information. Our approach offers a significant advancement over existing methods by employing specialized prediction heads that operate on a shared representation network, allowing direct and optimized prediction of both values. To ensure robust generalization, we integrated complementary datasets from SKEMPI v2 and PDBbind with a rigorous protein domain-based splitting strategy that prevents information leakage between training and validation sets. Our architecture combines transformer-based encoders with a novel cross-attention mechanism that processes paired protein sequences directly, without requiring any structural information. The network embeds input sequences using ESM3 representations, then employs a learnable sliced window embedding layer to manage variable-length sequences efficiently. A multi-layer transformer encoder with bidirectional self-attention captures intra-protein patterns, while cross-attention layers enable explicit modeling of interactions between protein pairs. This shared representation network feeds into separate $ΔG$ and $ΔΔG$ prediction heads, allowing task-specific optimization while leveraging common features. The model achieves $ΔΔG$ validation of Pearson correlation at 0.485, while maintaining strong $ΔG$ predictions (Pearson: 0.638). While existing approaches require protein structure data and binding interface information, our model eliminates these constraints. This provides a critical advantage for the numerous proteins with unknown structures or those challenging to crystallize, such as viral and intrinsically disordered proteins.", "authors": ["Supantha Dey", "Ratul Chowdhury"], "categories": ["q-bio.QM"], "fields_of_study": ["Biology"], "published_date": "2025-09-26", "url": "https://arxiv.org/abs/2509.22950", "pdf_url": "https://arxiv.org/pdf/2509.22950v1", "arxiv_id": "2509.22950", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1998}
{"id": "2bc1a481d3045b1f8b702408de30814fdca3d3fa1d01dbb18b974d3564addc64", "sources": ["arxiv", "semantic_scholar"], "title": "Efficient Quantum Protein Structure Prediction with Problem-Agnostic Ansatzes", "abstract": "Accurately predicting protein structures from amino acid sequences remains a fundamental challenge in computational biology, with profound implications for understanding biological functions and enabling structure-based drug discovery. Quantum computing approaches based on coarse-grained lattice models combined with variational algorithms have been proposed as an initial step towards predicting protein structures using quantum computers. In this work, we introduce a more efficient quantum protein structure prediction workflow that bypasses the need for explicit Hamiltonian construction by employing a problem-agnostic ansatz. The ansatz is trained to minimize an energy-based cost function that can be efficiently computed on classical computers, eliminating the need for ancillary qubits and reducing circuit depth compared to previous Hamiltonian-based methods. This enables a more scalable approach for larger proteins and facilitates the inclusion of higher-order interactions, previously hard to achieve in quantum approaches. We validate our method by benchmarking a hardware-efficient ansatz on a large set of proteins with up to 26 amino acids, modeled on the tetrahedral, body-centered cubic, and face-centered cubic lattices, incorporating up to second-nearest-neighbor interactions. We assess the performance on both a noise-free simulator and the ibm_kingston quantum computer using a set of distinct metrics to probe different aspects of the prediction quality. These experiments push the boundaries of quantum methods for protein structure prediction, targeting sequences that are longer than those typically addressed in prior studies. Overall, the results highlight the scalability and versatility of our approach, while also identifying key areas for improvement to inform future algorithm development and hardware advancements.", "authors": ["Hanna Linn", "Rui-Hao Li", "Alexander Holden", "Abdullah Ash Saki", "Frank DiFilippo", "Tomas Radivoyevitch", "Daniel Blankenberg", "Laura García-Álvarez", "Göran Johansson"], "categories": ["quant-ph"], "fields_of_study": ["Physics"], "published_date": "2025-09-22", "url": "https://arxiv.org/abs/2509.18263", "pdf_url": "https://arxiv.org/pdf/2509.18263v1", "arxiv_id": "2509.18263", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1969}
{"id": "2ea56703821928365e5f4eac2439d5e2942c319b75b2a594a2e7de9a80b27a4d", "sources": ["arxiv", "semantic_scholar"], "title": "From Prediction to Simulation: AlphaFold 3 as a Differentiable Framework for Structural Biology", "abstract": "AlphaFold 3 represents a transformative advancement in computational biology, enhancing protein structure prediction through novel multi-scale transformer architectures, biologically informed cross-attention mechanisms, and geometry-aware optimization strategies. These innovations dramatically improve predictive accuracy and generalization across diverse protein families, surpassing previous methods. Crucially, AlphaFold 3 embodies a paradigm shift toward differentiable simulation, bridging traditional static structural modeling with dynamic molecular simulations. By reframing protein folding predictions as a differentiable process, AlphaFold 3 serves as a foundational framework for integrating deep learning with physics-based molecular", "authors": ["Alireza Abbaszadeh", "Armita Shahlaee"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-08-25", "url": "https://arxiv.org/abs/2508.18446", "pdf_url": "https://arxiv.org/pdf/2508.18446v1", "arxiv_id": "2508.18446", "doi": "10.48550/arXiv.2508.18446", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2773}
{"id": "e022f1918a4154619461042e4a679001df53ea10cd5e186a91a4c4c279d51ba0", "sources": ["arxiv", "semantic_scholar"], "title": "Deep Learning Model for Amyloidogenicity Prediction using a Pre-trained Protein LLM", "abstract": "The prediction of amyloidogenicity in peptides and proteins remains a focal point of ongoing bioinformatics. The crucial step in this field is to apply advanced computational methodologies. Many recent approaches to predicting amyloidogenicity within proteins are highly based on evolutionary motifs and the individual properties of amino acids. It is becoming increasingly evident that the sequence information-based features show high predictive performance. Consequently, our study evaluated the contextual features of protein sequences obtained from a pretrained protein large language model leveraging bidirectional LSTM and GRU to predict amyloidogenic regions in peptide and protein sequences. Our method achieved an accuracy of 84.5% on 10-fold cross-validation and an accuracy of 83% in the test dataset. Our results demonstrate competitive performance, highlighting the potential of LLMs in enhancing the accuracy of amyloid prediction.", "authors": ["Zohra Yagoub", "Hafida Bouziane"], "categories": ["cs.LG", "cs.AI", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-08-18", "url": "https://arxiv.org/abs/2508.12575", "pdf_url": "https://arxiv.org/pdf/2508.12575v1", "arxiv_id": "2508.12575", "doi": "10.2991/978-94-6463-805-9_22", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2693}
{"id": "7dece0cfbb92ef34733e5a16ae907e3ec336efaff4fadf467989600c0c44684c", "sources": ["arxiv", "semantic_scholar"], "title": "Driving Accurate Allergen Prediction with Protein Language Models and Generalization-Focused Evaluation", "abstract": "Allergens, typically proteins capable of triggering adverse immune responses, represent a significant public health challenge. To accurately identify allergen proteins, we introduce Applm (Allergen Prediction with Protein Language Models), a computational framework that leverages the 100-billion parameter xTrimoPGLM protein language model. We show that Applm consistently outperforms seven state-of-the-art methods in a diverse set of tasks that closely resemble difficult real-world scenarios. These include identifying novel allergens that lack similar examples in the training set, differentiating between allergens and non-allergens among homologs with high sequence similarity, and assessing functional consequences of mutations that create few changes to the protein sequences. Our analysis confirms that xTrimoPGLM, originally trained on one trillion tokens to capture general protein sequence characteristics, is crucial for Applm's performance by detecting important differences among protein sequences. In addition to providing Applm as open-source software, we also provide our carefully curated benchmark datasets to facilitate future research.", "authors": ["Brian Shing-Hei Wong", "Joshua Mincheol Kim", "Sin-Hang Fung", "Qing Xiong", "Kelvin Fu-Kiu Ao", "Junkang Wei", "Ran Wang", "Dan Michelle Wang", "Jingying Zhou", "Bo Feng", "Alfred Sze-Lok Cheng", "Kevin Y. Yip", "Stephen Kwok-Wing Tsui", "Qin Cao"], "categories": ["cs.LG", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-08-14", "url": "https://arxiv.org/abs/2508.10541", "pdf_url": "https://arxiv.org/pdf/2508.10541v1", "arxiv_id": "2508.10541", "doi": "10.48550/arXiv.2508.10541", "citation_count": 0, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4091}
{"id": "290803a475eeed7c71953073b94d51203d2339516da2e71e81a90fe36e9abf07", "sources": ["arxiv", "semantic_scholar"], "title": "Energy-Based Models for Predicting Mutational Effects on Proteins", "abstract": "Predicting changes in binding free energy ($ΔΔG$) is a vital task in protein engineering and protein-protein interaction (PPI) engineering for drug discovery. Previous works have observed a high correlation between $ΔΔG$ and entropy, using probabilities of biologically important objects such as side chain angles and residue identities to estimate $ΔΔG$. However, estimating the full conformational distribution of a protein complex is generally considered intractable. In this work, we propose a new approach to $ΔΔG$ prediction that avoids this issue by instead leveraging energy-based models for estimating the probability of a complex's conformation. Specifically, we novelly decompose $ΔΔG$ into a sequence-based component estimated by an inverse folding model and a structure-based component estimated by an energy model. This decomposition is made tractable by assuming equilibrium between the bound and unbound states, allowing us to simplify the estimation of degeneracies associated with each state. Unlike previous deep learning-based methods, our method incorporates an energy-based physical inductive bias by connecting the often-used sequence log-odds ratio-based approach to $ΔΔG$ prediction with a new $ΔΔE$ term grounded in statistical mechanics. We demonstrate superiority over existing state-of-the-art structure and sequence-based deep learning methods in $ΔΔG$ prediction and antibody optimization against SARS-CoV-2.", "authors": ["Patrick Soga", "Zhenyu Lei", "Yinhan He", "Camille Bilodeau", "Jundong Li"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-14", "url": "https://arxiv.org/abs/2508.10629", "pdf_url": "https://arxiv.org/pdf/2508.10629v1", "arxiv_id": "2508.10629", "doi": "10.1145/3711896.3736931", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Knowledge Discovery and Data Mining", "quality_score": 0.2647}
{"id": "e4b968583cd5dac10956364486238675088de6b7ce1db2a8194760db6512cef2", "sources": ["arxiv", "semantic_scholar"], "title": "Not Yet AlphaFold for the Mind: Evaluating Centaur as a Synthetic Participant", "abstract": "Simulators have revolutionized scientific practice across the natural sciences. By generating data that reliably approximate real-world phenomena, they enable scientists to accelerate hypothesis testing and optimize experimental designs. This is perhaps best illustrated by AlphaFold, a Nobel-prize winning simulator in chemistry that predicts protein structures from amino acid sequences, enabling rapid prototyping of molecular interactions, drug targets, and protein functions. In the behavioral sciences, a reliable participant simulator - a system capable of producing human-like behavior across cognitive tasks - would represent a similarly transformative advance. Recently, Binz et al. introduced Centaur, a large language model (LLM) fine-tuned on human data from 160 experiments, proposing its use not only as a model of cognition but also as a participant simulator for \"in silico prototyping of experimental studies\", e.g., to advance automated cognitive science. Here, we review the core criteria for a participant simulator and assess how well Centaur meets them. Although Centaur demonstrates strong predictive accuracy, its generative behavior - a critical criterion for a participant simulator - systematically diverges from human data. This suggests that, while Centaur is a significant step toward predicting human behavior, it does not yet meet the standards of a reliable participant simulator or an accurate model of cognition.", "authors": ["Sabrina Namazova", "Alessandra Brondetta", "Younes Strittmatter", "Matthew Nassar", "Sebastian Musslick"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-08-11", "url": "https://arxiv.org/abs/2508.07887", "pdf_url": "https://arxiv.org/pdf/2508.07887v1", "arxiv_id": "2508.07887", "doi": "10.48550/arXiv.2508.07887", "citation_count": 2, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2612}
{"id": "6bcbf04dbaa3032ba85de972916bafeace4d1a2244148b2740390bf53e49725b", "sources": ["arxiv", "semantic_scholar"], "title": "Scaling and Data Saturation in Protein Language Models", "abstract": "Data in biology is redundant, noisy, and sparse. How does the type and scale of available data impact model performance? In this work, we specifically investigate how protein language models (pLMs) scale with increasing pretraining data. We investigate this relationship by measuring the performance of protein function prediction on a suite of pLMs pretrained on yearly snapshots of UniRef100 from 2011 to 2024. We find no evidence of model saturation on this task: performance improves--but not monotonically--with added data, and this trend differs between unsupervised and supervised experiments. Using a well-characterized Beta-Lactamase protein from E. coli, we find that unsupervised model predictions get better year-over-year, though they do not yet consistently perform better than the supervised baseline. Our results underscore the need for targeted data acquisition and deeper study of data scaling in protein modeling. All training, inference, analysis, and visualization code is available at: https://github.com/Align-to-Innovate/data-saturation-and-scaling.", "authors": ["Aviv Spinner", "Erika DeBenedictis", "Corey M. Hudson"], "categories": ["q-bio.QM"], "fields_of_study": ["Biology"], "published_date": "2025-07-29", "url": "https://arxiv.org/abs/2507.22210", "pdf_url": "https://arxiv.org/pdf/2507.22210v1", "arxiv_id": "2507.22210", "doi": null, "citation_count": 4, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Align-to-Innovate/data-saturation-and-scaling", "venue": null, "quality_score": 0.2911}
{"id": "a4bc9146f7a0426d9bce76a3696c3b193afce96a171c0a45f8d21791cbc16aeb", "sources": ["arxiv", "semantic_scholar"], "title": "A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations", "abstract": "Objectives: With accurate estimates of expected safety results, clinical trials could be better designed and monitored. We evaluated methods for predicting serious adverse event (SAE) results in clinical trials using information only from their registrations prior to the trial. Material and Methods: We analyzed 22,107 two-arm parallel interventional clinical trials from ClinicalTrials.gov with structured summary results. Two prediction models were developed: a classifier predicting whether a greater proportion of participants in an experimental arm would have SAEs (area under the receiver operating characteristic curve; AUC) compared to the control arm, and a regression model to predict the proportion of participants with SAEs in the control arms (root mean squared error; RMSE). A transfer learning approach using pretrained language models (e.g., ClinicalT5, BioBERT) was used for feature extraction, combined with a downstream model for prediction. To maintain semantic representation in long trial texts exceeding localized language model input limits, a sliding window method was developed for embedding extraction. Results: The best model (ClinicalT5+Transformer+MLP) had 77.6% AUC when predicting which trial arm had a higher proportion of SAEs. When predicting SAE proportion in the control arm, the same model achieved RMSE of 18.6%. The sliding window approach consistently outperformed direct comparisons. Across 12 classifiers, the average absolute AUC increase was 2.00%, and absolute RMSE reduction was 1.58% across 12 regressors. Discussion: Summary results data from ClinicalTrials.gov remains underutilized. Predicted results of publicly reported trials provides an opportunity to identify discrepancies between expected and reported safety results.", "authors": ["Qixuan Hu", "Xumou Zhang", "Jinman Kim", "Florence Bourgeois", "Adam G. Dunn"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-21", "url": "https://arxiv.org/abs/2507.22919", "pdf_url": "https://arxiv.org/pdf/2507.22919v2", "arxiv_id": "2507.22919", "doi": "10.48550/arXiv.2507.22919", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2372}
{"id": "dff613cd5afdfb5b33cc648ae472a565c46ecde7d7a36e62bcb42e8b00757bde", "sources": ["arxiv", "semantic_scholar"], "title": "Continued domain-specific pre-training of protein language models for pMHC-I binding prediction", "abstract": "Predicting peptide--major histocompatibility complex I (pMHC-I) binding affinity remains challenging due to extreme allelic diversity ($\\sim$30,000 HLA alleles), severe data scarcity for most alleles, and noisy experimental measurements. Current methods particularly struggle with underrepresented alleles and quantitative binding prediction. We test whether domain-specific continued pre-training of protein language models is beneficial for their application to pMHC-I binding affinity prediction. Starting from ESM Cambrian (300M parameters), we perform masked-language modeling (MLM)-based continued pre-training on HLA-associated peptides (epitopes), testing two input formats: epitope sequences alone versus epitopes concatenated with HLA heavy chain sequences. We then fine-tune for functional IC$_{50}$ binding affinity prediction using only high-quality quantitative data, avoiding mass spectrometry biases that are inherited by existing methods.", "authors": ["Sergio E. Mares", "Ariel Espinoza Weinberger", "Nilah M. Ioannidis"], "categories": ["q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-07-16", "url": "https://arxiv.org/abs/2507.13077", "pdf_url": "https://arxiv.org/pdf/2507.13077v1", "arxiv_id": "2507.13077", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1473}
{"id": "615a089e8e1750031a63923f8b0581717f0d58295045bf2313c3d7b7766f3413", "sources": ["arxiv", "semantic_scholar"], "title": "Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins", "abstract": "We introduce Ibex, a pan-immunoglobulin structure prediction model that achieves state-of-the-art accuracy in modeling the variable domains of antibodies, nanobodies, and T-cell receptors. Unlike previous approaches, Ibex explicitly distinguishes between bound and unbound protein conformations by training on labeled apo and holo structural pairs, enabling accurate prediction of both states at inference time. Using a comprehensive private dataset of high-resolution antibody structures, we demonstrate superior out-of-distribution performance compared to existing specialized and general protein structure prediction tools. Ibex combines the accuracy of cutting-edge models with significantly reduced computational requirements, providing a robust foundation for accelerating large molecule design and therapeutic development.", "authors": ["Frédéric A. Dreyer", "Jan Ludwiczak", "Karolis Martinkus", "Brennan Abanades", "Robert G. Alberstein", "Pan Kessel", "Pranav Rao", "Jae Hyeon Lee", "Richard Bonneau", "Andrew M. Watkins", "Franziska Seeger"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Medicine", "Biology", "Computer Science"], "published_date": "2025-07-11", "url": "https://arxiv.org/abs/2507.09054", "pdf_url": "https://arxiv.org/pdf/2507.09054v1", "arxiv_id": "2507.09054", "doi": "10.1080/19420862.2025.2602217", "citation_count": 6, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/prescient-design/ibex", "venue": "mAbs", "quality_score": 0.3489}
{"id": "85c60dba527fa4258ea2bfbeafd52499e3f6b46e5c68f5f2cb1560f8639e1815", "sources": ["arxiv", "semantic_scholar"], "title": "PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs", "abstract": "Deep learning-based computational methods have achieved promising results in predicting protein-protein interactions (PPIs). However, existing benchmarks predominantly focus on isolated pairwise evaluations, overlooking a model's capability to reconstruct biologically meaningful PPI networks, which is crucial for biology research. To address this gap, we introduce PRING, the first comprehensive benchmark that evaluates protein-protein interaction prediction from a graph-level perspective. PRING curates a high-quality, multi-species PPI network dataset comprising 21,484 proteins and 186,818 interactions, with well-designed strategies to address both data redundancy and leakage. Building on this golden-standard dataset, we establish two complementary evaluation paradigms: (1) topology-oriented tasks, which assess intra and cross-species PPI network construction, and (2) function-oriented tasks, including protein complex pathway prediction, GO module analysis, and essential protein justification. These evaluations not only reflect the model's capability to understand the network topology but also facilitate protein function annotation, biological module detection, and even disease mechanism analysis. Extensive experiments on four representative model categories, consisting of sequence similarity-based, naive sequence-based, protein language model-based, and structure-based approaches, demonstrate that current PPI models have potential limitations in recovering both structural and functional properties of PPI networks, highlighting the gap in supporting real-world biological applications. We believe PRING provides a reliable platform to guide the development of more effective PPI prediction models for the community. The dataset and source code of PRING are available at https://github.com/SophieSarceau/PRING.", "authors": ["Xinzhe Zheng", "Hao Du", "Fanding Xu", "Jinzhe Li", "Zhiyuan Liu", "Wenkang Wang", "Tao Chen", "Wanli Ouyang", "Stan Z. Li", "Yan Lu", "Nanqing Dong", "Yang Zhang"], "categories": ["cs.LG", "cs.AI", "q-bio.BM", "q-bio.MN"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-07-07", "url": "https://arxiv.org/abs/2507.05101", "pdf_url": "https://arxiv.org/pdf/2507.05101v2", "arxiv_id": "2507.05101", "doi": "10.48550/arXiv.2507.05101", "citation_count": 3, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/SophieSarceau/PRING", "venue": "arXiv.org", "quality_score": 0.3418}
{"id": "35416db0adec377947ad37adff664fc4425409a9b9b9c59febe15c247c6a8dee", "sources": ["arxiv", "semantic_scholar"], "title": "ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction", "abstract": "This paper presents ElliottAgents, a multi-agent system leveraging natural language processing (NLP) and large language models (LLMs) to analyze complex stock market data. The system combines AI-driven analysis with the Elliott Wave Principle to generate human-comprehensible predictions and explanations. A key feature is the natural language dialogue between agents, enabling collaborative analysis refinement. The LLM-enhanced architecture facilitates advanced language understanding, reasoning, and autonomous decision-making. Experiments demonstrate the system's effectiveness in pattern recognition and generating natural language descriptions of market trends. ElliottAgents contributes to NLP applications in specialized domains, showcasing how AI-driven dialogue systems can enhance collaborative analysis in data-intensive fields. This research bridges the gap between complex financial data and human understanding, addressing the need for interpretable and adaptive prediction systems in finance.", "authors": ["Jarosław A. Chudziak", "Michał Wawer"], "categories": ["cs.CE"], "fields_of_study": ["Computer Science"], "published_date": "2025-07-04", "url": "https://arxiv.org/abs/2507.03435", "pdf_url": "https://arxiv.org/pdf/2507.03435v1", "arxiv_id": "2507.03435", "doi": "10.48550/arXiv.2507.03435", "citation_count": 13, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Pacific Asia Conference on Language, Information and Computation", "quality_score": 0.2865}
{"id": "3f560b9c9b5d5696bdd8ef45f40c21973f45bc05886dd0a5b1b53c387d3b358d", "sources": ["arxiv", "semantic_scholar"], "title": "DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment", "abstract": "We introduce DeSTA2.5-Audio, a general-purpose Large Audio Language Model (LALM) designed for robust auditory perception and instruction-following. Recent LALMs augment Large Language Models (LLMs) with auditory capabilities by training on large-scale audio-instruction datasets. However, existing LALMs have often suffered from the catastrophic forgetting of the LLM's original abilities. Therefore, balancing knowledge retention and audio perception has become a critical challenge. To address this, we revisit the data construction pipeline and propose a self-generated cross-modal alignment strategy in which the backbone LLM generates its own training targets, named DeSTA. This approach aims at preserving the LLM's native language proficiency thereby enabling zero-shot generalization without task-specific tuning. We construct DeSTA-AQA5M, a large-scale, task-agnostic dataset containing 5 million training samples derived from 7,000 hours of audio spanning 50 diverse datasets, including speech, environmental sounds, and music. DeSTA2.5-Audio achieves state-of-the-art or competitive performance across a wide range of audio-language benchmarks, including Dynamic-SUPERB, MMAU, SAKURA, Speech-IFEval, and VoiceBench. Comprehensive comparative studies demonstrate that our self-generated strategy outperforms existing training strategies. Our findings underscore the importance of carefully designed data construction in LALM development and offer practical insights for building robust, general-purpose LALMs.", "authors": ["Ke-Han Lu", "Zhehuai Chen", "Szu-Wei Fu", "Chao-Han Huck Yang", "Sung-Feng Huang", "Chih-Kai Yang", "Chee-En Yu", "Chun-Wei Chen", "Wei-Chih Chen", "Chien-yu Huang", "Yi-Cheng Lin", "Yu-Xiang Lin", "Chi-An Fu", "Chun-Yi Kuan", "Wenze Ren", "Xuanjun Chen", "Wei-Ping Huang", "En-Pei Hu", "Tzu-Quan Lin", "Yuan-Kuei Wu", "Kuan-Po Huang", "Hsiao-Ying Huang", "Huang-Cheng Chou", "Kai-Wei Chang", "Cheng-Han Chiang", "Boris Ginsburg", "Yu-Chiang Frank Wang", "Hung-yi Lee"], "categories": ["eess.AS", "cs.CL", "cs.SD"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-07-03", "url": "https://arxiv.org/abs/2507.02768", "pdf_url": "https://arxiv.org/pdf/2507.02768v2", "arxiv_id": "2507.02768", "doi": "10.1109/TASLPRO.2026.3675792", "citation_count": 50, "influential_citation_count": 4, "has_code": true, "code_url": "https://github.com/kehanlu/DeSTA2.5-Audio", "venue": "IEEE Transactions on Audio, Speech, and Language Processing", "quality_score": 0.4269}
{"id": "837045204085ddf966e85102120ce95abc72efd910dd2e71936a2eba0e8aa5f9", "sources": ["arxiv", "semantic_scholar"], "title": "Steering Protein Language Models", "abstract": "Protein Language Models (PLMs), pre-trained on extensive evolutionary data from natural proteins, have emerged as indispensable tools for protein design. While powerful, PLMs often struggle to produce proteins with precisely specified functionalities or properties due to inherent challenges in controlling their outputs. In this work, we investigate the potential of Activation Steering, a technique originally developed for controlling text generation in Large Language Models (LLMs), to direct PLMs toward generating protein sequences with targeted properties. We propose a simple yet effective method that employs activation editing to steer PLM outputs, and extend this approach to protein optimization through a novel editing site identification module. Through comprehensive experiments on lysozyme-like sequence generation and optimization, we demonstrate that our methods can be seamlessly integrated into both auto-encoding and autoregressive PLMs without requiring additional training. These results highlight a promising direction for precise protein engineering using foundation models.", "authors": ["Long-Kai Huang", "Rongyi Zhu", "Bing He", "Jianhua Yao"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-07-01", "url": "https://arxiv.org/abs/2509.07983", "pdf_url": "https://arxiv.org/pdf/2509.07983v2", "arxiv_id": "2509.07983", "doi": "10.48550/arXiv.2509.07983", "citation_count": 5, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "International Conference on Machine Learning", "quality_score": 0.2386}
{"id": "992599a4c51365c71a416b8dffabf0fb43df0e7ad045cc9da447222ebe05dfa3", "sources": ["arxiv", "semantic_scholar"], "title": "Self-Organizing Language", "abstract": "We introduce a novel paradigm of emergent local memory. It is a continuous-learning completely-parallel content-addressable memory encoding global order. It demonstrates how local constraints on uncoordinated learning can produce topologically protected memories realizing emergent symbolic order. It is therefore a neuro-symbolic bridge. It further has the ability to produce human language without data, by exploiting its own self-organizing dynamics. It teaches us that words arise as a side-effect of emergent symbolic order, and that human language patterns at all structural levels reflect a universal mechanism of word formation (which is subregular). This work answers essential questions about the existence \\& origin of all the human language data.", "authors": ["P. Myles Eugenio", "Anthony Beavers"], "categories": ["cs.CL", "cs.AI", "cs.LG", "q-bio.NC"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-06-29", "url": "https://arxiv.org/abs/2506.23293", "pdf_url": "https://arxiv.org/pdf/2506.23293v2", "arxiv_id": "2506.23293", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1349}
{"id": "bf2b044f029bcd29e9063502a72385ef42bc5cf61b03375789252548e1837e8d", "sources": ["arxiv", "semantic_scholar"], "title": "Toward the Explainability of Protein Language Models", "abstract": "Protein language models (pLMs) excel in a variety of tasks that range from structure prediction to the design of functional enzymes. However, these models operate as black boxes, and their underlying working principles remain unclear. Here, we survey emerging applications of explainable artificial intelligence (XAI) to pLMs and describe the potential of XAI in protein research. We divide the workflow of protein AI modeling into four information contexts: (i) training sequences, (ii) input prompt, (iii) model architecture, and (iv) input-output pairs. For each, we describe existing methods and applications of XAI. Additionally, from published studies we distil five (potential) roles that XAI can play in protein research: Evaluator, Multitasker, Engineer, Coach, and Teacher, with the Evaluator role being the only one widely adopted so far. These roles aim to help both protein scientists and model developers understand the possibilities and limitations of implementing XAI for predictive and generative tasks. While our analysis focuses on pLMs, both this categorization and roles are broadly applicable to any other model architectures. We conclude by highlighting critical areas of application for the future, including risks related to security, trustworthiness, and bias, and we call for community benchmarks, open-source tooling, domain-specific visualizations, and wet-lab characterization to advance the interpretability of protein AI.", "authors": ["Andrea Hunklinger", "Noelia Ferruz"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2025-06-24", "url": "https://arxiv.org/abs/2506.19532", "pdf_url": "https://arxiv.org/pdf/2506.19532v4", "arxiv_id": "2506.19532", "doi": null, "citation_count": 4, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": null, "quality_score": 0.2437}
{"id": "1c0af367aabedb2a5100ab0033b2b6aeb96bc90576c2e3bece2c20ab078b17db", "sources": ["arxiv", "semantic_scholar"], "title": "From Data to Knowledge: Evaluating How Efficiently Language Models Learn Facts", "abstract": "Sample efficiency is a crucial property of language models with practical implications for training efficiency. In real-world text, information follows a long-tailed distribution. Yet, we expect models to learn and recall frequent and infrequent facts. Sample-efficient models are better equipped to handle this challenge of learning and retaining rare information without requiring excessive exposure. This study analyzes multiple models of varying architectures and sizes, all trained on the same pre-training data. By annotating relational facts with their frequencies in the training corpus, we examine how model performance varies with fact frequency. Our findings show that most models perform similarly on high-frequency facts but differ notably on low-frequency facts. This analysis provides new insights into the relationship between model architecture, size, and factual learning efficiency.", "authors": ["Daniel Christoph", "Max Ploner", "Patrick Haller", "Alan Akbik"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-20", "url": "https://arxiv.org/abs/2506.16912", "pdf_url": "https://arxiv.org/pdf/2506.16912v1", "arxiv_id": "2506.16912", "doi": "10.48550/arXiv.2506.16912", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1283}
{"id": "01e833fed03679295d74a2882911ff3d0bf08674ae9161c795494815377140bf", "sources": ["arxiv", "semantic_scholar"], "title": "PL-Guard: Benchmarking Language Model Safety for Polish", "abstract": "Despite increasing efforts to ensure the safety of large language models (LLMs), most existing safety assessments and moderation tools remain heavily biased toward English and other high-resource languages, leaving majority of global languages underexamined. To address this gap, we introduce a manually annotated benchmark dataset for language model safety classification in Polish. We also create adversarially perturbed variants of these samples designed to challenge model robustness. We conduct a series of experiments to evaluate LLM-based and classifier-based models of varying sizes and architectures. Specifically, we fine-tune three models: Llama-Guard-3-8B, a HerBERT-based classifier (a Polish BERT derivative), and PLLuM, a Polish-adapted Llama-8B model. We train these models using different combinations of annotated data and evaluate their performance, comparing it against publicly available guard models. Results demonstrate that the HerBERT-based classifier achieves the highest overall performance, particularly under adversarial conditions.", "authors": ["Aleksandra Krasnodębska", "Karolina Seweryn", "Szymon Łukasik", "Wojciech Kusa"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-19", "url": "https://arxiv.org/abs/2506.16322", "pdf_url": "https://arxiv.org/pdf/2506.16322v1", "arxiv_id": "2506.16322", "doi": "10.48550/arXiv.2506.16322", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1276}
{"id": "7b8ee46486dc765aaba8be750a85138fb4c60b5c22eff352a3cd190d5dc50a04", "sources": ["arxiv", "semantic_scholar"], "title": "Can structural correspondences ground real world representational content in Large Language Models?", "abstract": "Large Language Models (LLMs) such as GPT-4 produce compelling responses to a wide range of prompts. But their representational capacities are uncertain. Many LLMs have no direct contact with extra-linguistic reality: their inputs, outputs and training data consist solely of text, raising the questions (1) can LLMs represent anything and (2) if so, what? In this paper, I explore what it would take to answer these questions according to a structural-correspondence based account of representation, and make an initial survey of this evidence. I argue that the mere existence of structural correspondences between LLMs and worldly entities is insufficient to ground representation of those entities. However, if these structural correspondences play an appropriate role - they are exploited in a way that explains successful task performance - then they could ground real world contents. This requires overcoming a challenge: the text-boundedness of LLMs appears, on the face of it, to prevent them engaging in the right sorts of tasks.", "authors": ["Iwan Williams"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-19", "url": "https://arxiv.org/abs/2506.16370", "pdf_url": "https://arxiv.org/pdf/2506.16370v1", "arxiv_id": "2506.16370", "doi": "10.1111/mila.70018", "citation_count": 6, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Mind & Language (2026)", "quality_score": 0.2113}
{"id": "75a55fff656c471766778a9e496b285ba5ba3581e9e1cab37cd1ff9bb027f2f0", "sources": ["arxiv", "semantic_scholar"], "title": "DISPROTBENCH: Uncovering the Functional Limits of Protein Structure Prediction Models in Intrinsically Disordered Regions", "abstract": "Intrinsically disordered regions (IDRs) play central roles in cellular function, yet remain poorly evaluated by existing protein structure prediction benchmarks. Current evaluations largely focus on well-folded domains, overlooking three fundamental challenges in realistic biological settings: the structural complexity of proteins, the resulting low availability of reliable ground truth, and prediction uncertainty that can propagate into high-risk downstream failures, such as in drug discovery, protein-protein interaction modeling, and functional annotation. We present DisProtBench, an IDR-centric benchmark that explicitly incorporates prediction uncertainty into the evaluation of protein structure prediction models (PSPMs). To address structural complexity and ground-truth scarcity, we curate and unify a large-scale, multi-modal dataset spanning disease-relevant IDRs, GPCR-ligand interactions, and multimeric protein complexes. To assess predictive uncertainty, we introduce Functional Uncertainty Sensitivity (FUS), a novel prediction uncertainty-stratified metric that quantifies downstream task performance under prediction uncertainty. Using this benchmark, we conduct a systematic evaluation of state-of-the-art PSPMs and reveal clear, task-dependent failure modes. Protein-protein interaction prediction degrades sharply in IDRs, while structure-based drug discovery remains comparatively robust. These effects are largely invisible to standard global accuracy metrics, which overestimate functional reliability under prediction uncertainty. We have open-sourced our benchmark and the codebase at https://github.com/Susan571/DisProtBench.", "authors": ["Xinyue Zeng", "Tuo Wang", "Adithya Kulkarni", "Alexander Lu", "Alexandra Ni", "Phoebe Xing", "Junhan Zhao", "Siwei Chen", "Dawei Zhou"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2025-06-18", "url": "https://arxiv.org/abs/2507.02883", "pdf_url": "https://arxiv.org/pdf/2507.02883v2", "arxiv_id": "2507.02883", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Susan571/DisProtBench", "venue": null, "quality_score": 0.2356}
{"id": "aedb9150be631477545f2e7296c2361e0785321ca04414fac3e3b31ad0a3f244", "sources": ["arxiv", "semantic_scholar"], "title": "InstructPro: Natural Language Guided Ligand-Binding Protein Design", "abstract": "The de novo design of ligand-binding proteins with tailored functions is essential for advancing biotechnology and molecular medicine, yet existing AI approaches are limited by scarce protein-ligand complex data. To circumvent this data bottleneck, we leverage the abundant natural language descriptions characterizing protein-ligand interactions. Here, we introduce InstructPro, a family of generative models that design proteins following the guidance of natural language instructions and ligand formulas. InstructPro produces protein sequences consistent with specified function descriptions and ligand targets. To enable training and evaluation, we develop InstructProBench, a large-scale dataset of 9.6 million (function description, ligand, protein) triples. We train two model variants -- InstructPro-1B and InstructPro-3B -- that substantially outperform strong baselines. InstructPro-1B achieves an AlphaFold3 ipTM of 0.918 and a binding affinity of -8.764 on seen ligands, while maintaining robust performance in a zero-shot setting with scores of 0.869 and -6.713, respectively. These results are accompanied by novelty scores of 70.1% and 68.8%, underscoring the model's ability to generalize beyond the training set. Furthermore, the model yields a superior binding free energy of -20.9 kcal/mol and an average of 5.82 intermolecular hydrogen bonds, validating its proficiency in designing high-affinity ligand-binding proteins. Notably, scaling to InstructPro-3B further improves the zero-shot ipTM to 0.882, binding affinity to -6.797, and binding free energy to -25.8 kcal/mol, demonstrating clear performance gains associated with increased model capacity. These findings highlight the power of natural language-guided generative models to mitigate the data bottlenecks in traditional structure-based methods, significantly broadening the scope of de novo protein design.", "authors": ["Zhenqiao Song", "Ramith Hettiarachchi", "Chuan Li", "Jianwen Xie", "Lei Li"], "categories": ["cs.LG", "cs.CE", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-11", "url": "https://arxiv.org/abs/2506.09332", "pdf_url": "https://arxiv.org/pdf/2506.09332v3", "arxiv_id": "2506.09332", "doi": null, "citation_count": 4, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "24ec0895e13954f36f7dc4f6657cc93c8536513335b13c531818dbc89383a157", "sources": ["arxiv", "semantic_scholar"], "title": "BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models", "abstract": "We present BioLangFusion, a simple approach for integrating pre-trained DNA, mRNA, and protein language models into unified molecular representations. Motivated by the central dogma of molecular biology (information flow from gene to transcript to protein), we align per-modality embeddings at the biologically meaningful codon level (three nucleotides encoding one amino acid) to ensure direct cross-modal correspondence. BioLangFusion studies three standard fusion techniques: (i) codon-level embedding concatenation, (ii) entropy-regularized attention pooling inspired by multiple-instance learning, and (iii) cross-modal multi-head attention -- each technique providing a different inductive bias for combining modality-specific signals. These methods require no additional pre-training or modification of the base models, allowing straightforward integration with existing sequence-based foundation models. Across five molecular property prediction tasks, BioLangFusion outperforms strong unimodal baselines, showing that even simple fusion of pre-trained models can capture complementary multi-omic information with minimal overhead.", "authors": ["Amina Mollaysa", "Artem Moskale", "Pushpak Pati", "Tommaso Mansi", "Mangal Prakash", "Rui Liao"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-10", "url": "https://arxiv.org/abs/2506.08936", "pdf_url": "https://arxiv.org/pdf/2506.08936v1", "arxiv_id": "2506.08936", "doi": "10.48550/arXiv.2506.08936", "citation_count": 4, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1902}
{"id": "9421819fef23fac9c08cb493a8b23e745d222e0dbeb894e66f36a485fdc7eaf2", "sources": ["arxiv", "semantic_scholar"], "title": "AlphaFold Database Debiasing for Robust Inverse Folding", "abstract": "The AlphaFold Protein Structure Database (AFDB) offers unparalleled structural coverage at near-experimental accuracy, positioning it as a valuable resource for data-driven protein design. However, its direct use in training deep models that are sensitive to fine-grained atomic geometry, such as inverse folding, exposes a critical limitation. Comparative analysis of structural feature distributions reveals that AFDB structures exhibit distinct statistical regularities, reflecting a systematic geometric bias that deviates from the conformational diversity found in experimentally determined structures from the Protein Data Bank (PDB). While AFDB structures are cleaner and more idealized, PDB structures capture the intrinsic variability and physical realism essential for generalization in downstream tasks. To address this discrepancy, we introduce a Debiasing Structure AutoEncoder (DeSAE) that learns to reconstruct native-like conformations from intentionally corrupted backbone geometries. By training the model to recover plausible structural states, DeSAE implicitly captures a more robust and natural structural manifold. At inference, applying DeSAE to AFDB structures produces debiased structures that significantly improve inverse folding performance across multiple benchmarks. This work highlights the critical impact of subtle systematic biases in predicted structures and presents a principled framework for debiasing, significantly boosting the performance of structure-based learning tasks like inverse folding.", "authors": ["Cheng Tan", "Zhenxiao Cao", "Zhangyang Gao", "Siyuan Li", "Yufei Huang", "Stan Z. Li"], "categories": ["cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-06-10", "url": "https://arxiv.org/abs/2506.08365", "pdf_url": "https://arxiv.org/pdf/2506.08365v1", "arxiv_id": "2506.08365", "doi": "10.48550/arXiv.2506.08365", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1902}
{"id": "b6f9da9b8836d99fefd98883d1732d2e02cc5ef600038430c9cd640a94a337c8", "sources": ["arxiv", "semantic_scholar"], "title": "Into the Unknown: From Structure to Disorder in Protein Function Prediction", "abstract": "Intrinsically disordered regions (IDRs) account for one-third of the human proteome and play essential biological roles. However, predicting the functions of IDRs remains a major challenge due to their lack of stable structures, rapid sequence evolution, and context-dependent behavior. Many predictors of protein function neglect or underperform on IDRs. Recent advances in computational biology and machine learning, including protein language models, alignment-free approaches, and IDR-specific methods, have revealed conserved bulk features and local motifs within IDRs that are linked to function. This review highlights emerging computational methods that map the sequence-function relationship in IDRs, outlines critical challenges in IDR function annotation, and proposes a community-driven framework to accelerate interpretable functional predictions for IDRs.", "authors": ["Đesika Kolarić", "Chi Fung Willis Chow", "Rita Zi Zhu", "Agnes Toth-Petroczy", "T. Reid Alderson", "Iva Pritišanac"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2025-06-06", "url": "https://arxiv.org/abs/2506.06004", "pdf_url": "https://arxiv.org/pdf/2506.06004v2", "arxiv_id": "2506.06004", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1181}
{"id": "177a284d804e1f6fd8193aa1754044be2fe8d467b2ef2b396b9ce295df9570d1", "sources": ["arxiv", "semantic_scholar"], "title": "Multiscale guidance of protein structure prediction with heterogeneous cryo-EM data", "abstract": "Protein structure prediction models are now capable of generating accurate 3D structural hypotheses from sequence alone. However, they routinely fail to capture the conformational diversity of dynamic biomolecular complexes, often requiring heuristic MSA subsampling approaches for generating alternative states. In parallel, cryo-electron microscopy (cryo-EM) has emerged as a powerful tool for imaging near-native structural heterogeneity, but is challenged by arduous pipelines to transform raw experimental data into atomic models. Here, we bridge the gap between these modalities, combining cryo-EM density maps with the rich sequence and biophysical priors learned by protein structure prediction models. Our method, CryoBoltz, guides the sampling trajectory of a pretrained biomolecular structure prediction model using both global and local structural constraints derived from density maps, driving predictions towards conformational states consistent with the experimental data. We demonstrate that this flexible yet powerful inference-time approach allows us to build atomic models into heterogeneous cryo-EM maps across a variety of dynamic biomolecular systems including transporters and antibodies. Code is available at https://github.com/ml-struct-bio/cryoboltz .", "authors": ["Rishwanth Raghu", "Axel Levy", "Gordon Wetzstein", "Ellen D. Zhong"], "categories": ["cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-06-04", "url": "https://arxiv.org/abs/2506.04490", "pdf_url": "https://arxiv.org/pdf/2506.04490v2", "arxiv_id": "2506.04490", "doi": null, "citation_count": 10, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/ml-struct-bio/cryoboltz", "venue": null, "quality_score": 0.2603}
{"id": "541b8d667afef65e6b76a169bafb9914c98341ee055cde8e5622457859c6a733", "sources": ["arxiv", "semantic_scholar"], "title": "Trajectory Prediction Meets Large Language Models: A Survey", "abstract": "Recent advances in large language models (LLMs) have sparked growing interest in integrating language-driven techniques into trajectory prediction. By leveraging their semantic and reasoning capabilities, LLMs are reshaping how autonomous systems perceive, model, and predict trajectories. This survey provides a comprehensive overview of this emerging field, categorizing recent work into five directions: (1) Trajectory prediction via language modeling paradigms, (2) Direct trajectory prediction with pretrained language models, (3) Language-guided scene understanding for trajectory prediction, (4) Language-driven data generation for trajectory prediction, (5) Language-based reasoning and interpretability for trajectory prediction. For each, we analyze representative methods, highlight core design choices, and identify open challenges. This survey bridges natural language processing and trajectory prediction, offering a unified perspective on how language can enrich trajectory prediction.", "authors": ["Yi Xu", "Ruining Yang", "Yitian Zhang", "Jianglin Lu", "Mingyuan Zhang", "Yizhou Wang", "Lili Su", "Yun Fu"], "categories": ["cs.CL", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2025-06-03", "url": "https://arxiv.org/abs/2506.03408", "pdf_url": "https://arxiv.org/pdf/2506.03408v2", "arxiv_id": "2506.03408", "doi": "10.48550/arXiv.2506.03408", "citation_count": 15, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers", "venue": "arXiv.org", "quality_score": 0.301}
{"id": "d2584d7ddd76ec6b7043b482c64f9367d8ea70d3ba478faa9cb3446c0be966bb", "sources": ["arxiv", "semantic_scholar"], "title": "Protein Language Model Zero-Shot Fitness Predictions are Improved by Inference-only Dropout", "abstract": "Protein Language Models (PLMs) such as ESM2 have been shown to be capable of zero-shot prediction of critical scalar properties of proteins (fitness). In this work, we show that injecting a dropout layer at inference time between a PLM's featurizer/embedding layer and its transformer, and averaging its output akin to Monte-Carlo dropout increases zero-shot performance on a subset of the ProteinGym dataset. This is the case even when the model was not trained with dropouts to begin with, and does not require retraining or finetuning of the PLM. A dropout of 0.1 seems performant across all models.", "authors": ["Aditya Ravuri", "Neil D. Lawrence"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-31", "url": "https://arxiv.org/abs/2506.14793", "pdf_url": "https://arxiv.org/pdf/2506.14793v1", "arxiv_id": "2506.14793", "doi": "10.48550/arXiv.2506.14793", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1788}
{"id": "2e6bfe02b9c252b7da6e6a04262ddef85921fd4a3f74ec224c7399df50c2b55f", "sources": ["arxiv", "semantic_scholar"], "title": "Aligning Proteins and Language: A Foundation Model for Protein Retrieval", "abstract": "This paper aims to retrieve proteins with similar structures and semantics from large-scale protein dataset, facilitating the functional interpretation of protein structures derived by structural determination methods like cryo-Electron Microscopy (cryo-EM). Motivated by the recent progress of vision-language models (VLMs), we propose a CLIP-style framework for aligning 3D protein structures with functional annotations using contrastive learning. For model training, we propose a large-scale dataset of approximately 200,000 protein-caption pairs with rich functional descriptors. We evaluate our model in both in-domain and more challenging cross-database retrieval on Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB) dataset, respectively. In both cases, our approach demonstrates promising zero-shot retrieval performance, highlighting the potential of multimodal foundation models for structure-function understanding in protein biology.", "authors": ["Qifeng Wu", "Zhengzhe Liu", "Han Zhu", "Yizhou Zhao", "Daisuke Kihara", "Min Xu"], "categories": ["q-bio.BM", "cs.AI", "cs.CE", "cs.CV", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-05-27", "url": "https://arxiv.org/abs/2506.08023", "pdf_url": "https://arxiv.org/pdf/2506.08023v1", "arxiv_id": "2506.08023", "doi": "10.48550/arXiv.2506.08023", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1742}
{"id": "add8b8512e20d5ecc2e9f9bbbc2767b3acb548b225aad58f3d9fa66911e73d79", "sources": ["arxiv", "semantic_scholar"], "title": "AlphaFold's Bayesian Roots in Probability Kinematics", "abstract": "The seminal breakthrough of AlphaFold in protein structure prediction relied on a learned potential energy function parameterized by deep models, in contrast to its successors AlphaFold2 and AlphaFold3, which lack an explicit probabilistic interpretation. While AlphaFold's potential was originally justified by heuristic analogy to physical potentials of mean force, we show that it can instead be understood as a principled instance of probability kinematics (PK), also known as Jeffrey conditioning, a generalization of Bayesian updating. This reinterpretation reveals that AlphaFold is a generalized Bayesian model that explicitly defines a posterior distribution over structures, providing a deeper explanation of its success and a foundation for future model design. To demonstrate this framework with precision, we introduce a tractable synthetic model in which an angular random walk prior is updated with distance-based evidence via PK, directly mirroring AlphaFold's mechanism. This setting allows us to explore the probabilistic foundations of AlphaFold in a clear and interpretable way. Our work connects a landmark in protein structure prediction to a broader class of compositional deep generative models and points to new opportunities for principled probabilistic approaches.", "authors": ["Thomas Hamelryck", "Kanti V. Mardia"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-26", "url": "https://arxiv.org/abs/2505.19763", "pdf_url": "https://arxiv.org/pdf/2505.19763v3", "arxiv_id": "2505.19763", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1193}
{"id": "f90c949ade15ba2f4c9e0bd5e1fdeb023da3cf4dd97ba383fdfa8e6c1a128b9d", "sources": ["arxiv", "semantic_scholar"], "title": "From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data", "abstract": "Audio-aware large language models (ALLMs) have recently made great strides in understanding and processing audio inputs. These models are typically adapted from text-based large language models (LLMs) through additional training on audio-related tasks. This adaptation process presents two major limitations. First, ALLMs often suffer from catastrophic forgetting, where crucial textual capabilities like instruction-following are lost after training on audio data. In some cases, models may even hallucinate sounds that are not present in the input audio, raising concerns about reliability. Second, achieving cross-modal alignment between audio and language typically relies on large collections of task-specific question-answer pairs for instruction tuning, making it resource-intensive. To address these issues, previous works have leveraged the backbone LLMs to synthesize general-purpose, caption-style alignment data. In this paper, we propose a data generation framework that produces contrastive-like training data, designed to enhance ALLMs' ability to differentiate between present and absent sounds. We further extend our approach to multi-audio scenarios, enabling the model to either explain differences between audio inputs or produce unified captions that describe all inputs, thereby enhancing audio-language alignment. We refer to the entire ALLM training framework as bootstrapping audio-language alignment via synthetic data generation from backbone LLMs (BALSa). Experimental results indicate that our method effectively mitigates audio hallucinations while reliably maintaining strong performance on audio understanding and reasoning benchmarks, as well as instruction-following skills. Moreover, incorporating multi-audio training further enhances the model's comprehension and reasoning capabilities. Overall, BALSa offers an efficient and scalable approach to developing ALLMs.", "authors": ["Chun-Yi Kuan", "Hung-yi Lee"], "categories": ["eess.AS", "cs.AI", "cs.CL", "cs.LG", "cs.SD"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2025-05-26", "url": "https://arxiv.org/abs/2505.20166", "pdf_url": "https://arxiv.org/pdf/2505.20166v3", "arxiv_id": "2505.20166", "doi": "10.1109/TASLPRO.2025.3626233", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Transactions on Audio, Speech, and Language Processing", "quality_score": 0.1747}
{"id": "fc09c3be373f4e44bae269a7a4096faf762b965be76abf58376cdb758f052856", "sources": ["arxiv", "semantic_scholar"], "title": "Prot2Token: A Unified Framework for Protein Modeling via Next-Token Prediction", "abstract": "The diverse nature of protein prediction tasks has traditionally necessitated specialized models, hindering the development of broadly applicable and computationally efficient Protein Language Models (PLMs). In this work, we introduce Prot2Token, a unified framework that overcomes these challenges by converting a wide spectrum of protein-related predictions-from sequence-level properties and residue-specific attributes to complex inter-protein interactions-into a standardized next-token prediction format. At its core, Prot2Token employs an autoregressive decoder, conditioned on embeddings from pre-trained protein encoders and guided by learnable task tokens, to perform diverse predictions. This architecture uniquely facilitates multi-task learning, enabling general-purpose decoders to generalize across five distinct categories. We present extensive experimental validation across a variety of benchmarks, demonstrating Prot2Token's predictive power in different types of protein-prediction tasks. In 3D structure prediction, Prot2Token delivers substantial speedups (up to 1000x faster than AlphaFold2 with MSA on the same hardware) while, across other numerous tasks, matching or surpassing specialized methods. Beyond that, we introduce an auxiliary self-supervised decoder pre-training approach to improve spatially sensitive task performance. Prot2Token thus offers a step towards standardizing biological prediction into a generative interface, promising to accelerate biological discovery and the development of novel therapeutics. The code is available at https://github.com/mahdip72/prot2token .", "authors": ["Mahdi Pourmirzaei", "Farzaneh Esmaili", "Salhuldin Alqarghuli", "Mohammadreza Pourmirzaei", "Ye Han", "Kai Chen", "Mohsen Rezaei", "Duolin Wang", "Dong Xu"], "categories": ["cs.LG", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-05-26", "url": "https://arxiv.org/abs/2505.20589", "pdf_url": "https://arxiv.org/pdf/2505.20589v2", "arxiv_id": "2505.20589", "doi": "10.48550/arXiv.2505.20589", "citation_count": 4, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/mahdip72/prot2token", "venue": "arXiv.org", "quality_score": 0.2674}
{"id": "c3a06ff816af0f5fd2eb043768765ca7388db572603b0eb914ce8b4ddd548e6d", "sources": ["arxiv", "semantic_scholar"], "title": "Rethinking Text-based Protein Understanding: Retrieval or LLM?", "abstract": "In recent years, protein-text models have gained significant attention for their potential in protein generation and understanding. Current approaches focus on integrating protein-related knowledge into large language models through continued pretraining and multi-modal alignment, enabling simultaneous comprehension of textual descriptions and protein sequences. Through a thorough analysis of existing model architectures and text-based protein understanding benchmarks, we identify significant data leakage issues present in current benchmarks. Moreover, conventional metrics derived from natural language processing fail to accurately assess the model's performance in this domain. To address these limitations, we reorganize existing datasets and introduce a novel evaluation framework based on biological entities. Motivated by our observation, we propose a retrieval-enhanced method, which significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios. Our code and data can be seen at https://github.com/IDEA-XL/RAPM.", "authors": ["Juntong Wu", "Zijing Liu", "He Cao", "Hao Li", "Bin Feng", "Zishan Shu", "Ke Yu", "Li Yuan", "Yu Li"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-26", "url": "https://arxiv.org/abs/2505.20354", "pdf_url": "https://arxiv.org/pdf/2505.20354v4", "arxiv_id": "2505.20354", "doi": "10.48550/arXiv.2505.20354", "citation_count": 7, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/IDEA-XL/RAPM", "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.2674}
{"id": "45c8f864b22bddd63e8ed7a00371b6995e9b2360a5d6c2b608e5a7365bdb3ed1", "sources": ["arxiv", "semantic_scholar"], "title": "Structure-Aligned Protein Language Model", "abstract": "Protein language models (pLMs) pre-trained on vast protein sequence databases excel at various downstream tasks but often lack the structural knowledge essential for some biological applications. To address this, we introduce a method to enrich pLMs with structural knowledge by leveraging pre-trained protein graph neural networks (pGNNs). First, a latent-level contrastive learning task aligns residue representations from pLMs with those from pGNNs across multiple proteins, injecting inter-protein structural information. Additionally, a physical-level task integrates intra-protein information by training pLMs to predict structure tokens. Together, the proposed dual-task framework effectively incorporates both inter- and intra-protein structural knowledge into pLMs. Given the variability in the quality of protein structures in PDB, we further introduce a residue loss selection module that uses a small model trained on high-quality structures to select reliable yet challenging residue losses for the pLM to learn. Applying our structure alignment method as a simple, lightweight post-training step to the state-of-the-art ESM2 and AMPLIFY yields notable performance gains. These improvements are consistent across a wide range of tasks, including substantial gains in deep mutational scanning (DMS) fitness prediction and a 59% increase in P@L for ESM2 650M contact prediction on CASP16. Furthermore, we demonstrate that these performance gains are robust, scaling with model sizes from 8M to 650M and extending to different downstream tasks.", "authors": ["Can Chen", "David Heurtel-Depeiges", "Robert M. Vernon", "Christopher James Langmead", "Yoshua Bengio", "Quentin Fournier"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-22", "url": "https://arxiv.org/abs/2505.16896", "pdf_url": "https://arxiv.org/pdf/2505.16896v2", "arxiv_id": "2505.16896", "doi": "10.48550/arXiv.2505.16896", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1684}
{"id": "e43f7c77f398ceb34627e66c526781424035739e1b0ea58edced7a5e7ea62a43", "sources": ["arxiv", "semantic_scholar"], "title": "CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models", "abstract": "Large language models (LLMs) have shown remarkable ability in various language tasks, especially with their emergent in-context learning capability. Extending LLMs to incorporate visual inputs, large vision-language models (LVLMs) have shown impressive performance in tasks such as recognition and visual question answering (VQA). Despite increasing interest in the utility of LLMs in causal reasoning tasks such as causal discovery and counterfactual reasoning, there has been relatively little work showcasing the abilities of LVLMs on visual causal reasoning tasks. We take this opportunity to formally introduce a comprehensive causal reasoning benchmark for multi-modal in-context learning from LVLMs. Our CausalVLBench encompasses three representative tasks: causal structure inference, intervention target prediction, and counterfactual prediction. We evaluate the ability of state-of-the-art open-source LVLMs on our causal reasoning tasks across three causal representation learning datasets and demonstrate their fundamental strengths and weaknesses. We hope that our benchmark elucidates the drawbacks of existing vision-language models and motivates new directions and paradigms in improving the visual causal reasoning abilities of LVLMs.", "authors": ["Aneesh Komanduri", "Karuna Bhaila", "Xintao Wu"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-21", "url": "https://arxiv.org/abs/2506.11034", "pdf_url": "https://arxiv.org/pdf/2506.11034v2", "arxiv_id": "2506.11034", "doi": "10.48550/arXiv.2506.11034", "citation_count": 4, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.2585}
{"id": "70eba8961a5bce19dd3b371e53cf8e362f805d33f3c8ec16326dfd6359a5d05b", "sources": ["arxiv", "semantic_scholar"], "title": "Modeling and prediction of mutation fitness on protein functionality with structural information using high-dimensional Potts model", "abstract": "Quantifying the effects of amino acid mutations in proteins presents a significant challenge due to the vast combinations of residue sites and amino acid types, making experimental approaches costly and time-consuming. The Potts model has been used to address this challenge, with parameters capturing evolutionary dependency between residue sites within a protein family. However, existing methods often use the mean-field approximation to reduce computational demands, which lacks provable guarantees and overlooks critical structural information for assessing mutation effects. We propose a new framework for analyzing protein sequences using the Potts model with node-wise high-dimensional multinomial regression. Our method identifies key residue interactions and important amino acids, quantifying mutation effects through evolutionary energy derived from model parameters. It encourages sparsity in both site-wise and amino acid-wise dependencies through element-wise and group sparsity. We have established, for the first time to our knowledge, the $\\ell_2$ convergence rate for estimated parameters in the high-dimensional Potts model using sparse group Lasso, matching the existing minimax lower bound for high-dimensional linear models with a sparse group structure, up to a factor depending only on the multinomial nature of the Potts model. This theoretical guarantee enables accurate quantification of estimated energy changes. Additionally, we incorporate structural data into our model by applying penalty weights across site pairs. Our method outperforms others in predicting mutation fitness, as demonstrated by comparisons with high-throughput mutagenesis experiments across 12 protein families.", "authors": ["Bingying Dai", "Yinan Lin", "Kejue Jia", "Zhao Ren", "Wen Zhou"], "categories": ["stat.ME"], "fields_of_study": ["Mathematics"], "published_date": "2025-05-20", "url": "https://arxiv.org/abs/2505.14958", "pdf_url": "https://arxiv.org/pdf/2505.14958v1", "arxiv_id": "2505.14958", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1057}
{"id": "b4f1f40ac7272d1fd17b698fe4612591ab417a616b32ec1851b903e1c36dd0ea", "sources": ["arxiv", "semantic_scholar"], "title": "DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design", "abstract": "Inverse Protein Folding (IPF) is a critical subtask in the field of protein design, aiming to engineer amino acid sequences capable of folding correctly into a specified three-dimensional (3D) conformation. Although substantial progress has been achieved in recent years, existing methods generally rely on either backbone coordinates or molecular surface features alone, which restricts their ability to fully capture the complex chemical and geometric constraints necessary for precise sequence prediction. To address this limitation, we present DS-ProGen, a dual-structure deep language model for functional protein design, which integrates both backbone geometry and surface-level representations. By incorporating backbone coordinates as well as surface chemical and geometric descriptors into a next-amino-acid prediction paradigm, DS-ProGen is able to generate functionally relevant and structurally stable sequences while satisfying both global and local conformational constraints. On the PRIDE dataset, DS-ProGen attains the current state-of-the-art recovery rate of 61.47%, demonstrating the synergistic advantage of multi-modal structural encoding in protein design. Furthermore, DS-ProGen excels in predicting interactions with a variety of biological partners, including ligands, ions, and RNA, confirming its robust functional retention capabilities.", "authors": ["Yanting Li", "Jiyue Jiang", "Zikang Wang", "Ziqian Lin", "Dongchen He", "Yuheng Shan", "Yanruisheng Shao", "Jiayi Li", "Xiangyu Shi", "Jiuming Wang", "Yanyu Chen", "Yimin Fan", "Han Li", "Yu Li"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-18", "url": "https://arxiv.org/abs/2505.12511", "pdf_url": "https://arxiv.org/pdf/2505.12511v1", "arxiv_id": "2505.12511", "doi": "10.48550/arXiv.2505.12511", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "837c900713503c342909a2496f3460510a79040724d12115c9b6b73d0b594224", "sources": ["arxiv", "semantic_scholar"], "title": "mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Molecules", "abstract": "Despite their ability to understand chemical knowledge, large language models (LLMs) remain limited in their capacity to propose novel molecules with desired functions (e.g., drug-like properties). In addition, the molecules that LLMs propose can often be challenging to make, and are almost never compatible with automated synthesis approaches. To better enable the discovery of functional small molecules, LLMs need to learn a new molecular language that is more effective in predicting properties and inherently synced with automated synthesis technology. Current molecule LLMs are limited by representing molecules based on atoms. In this paper, we argue that just like tokenizing texts into meaning-bearing (sub-)word tokens instead of characters, molecules should be tokenized at the level of functional building blocks, i.e., parts of molecules that bring unique functions and serve as effective building blocks for real-world automated laboratory synthesis. This motivates us to propose mCLM, a modular Chemical-Language Model that comprises a bilingual language model that understands both natural language descriptions of functions and molecular blocks. mCLM front-loads synthesizability considerations while improving the predicted functions of molecules in a principled manner. Experiments on FDA-approved drugs showed that mCLM is capable of significantly improving chemical functions. mCLM, with only 3B parameters, also achieves improvements in synthetic accessibility relative to 7 other leading generative AI methods including GPT-5. When tested on 122 out-of-distribution medicines using only building blocks/tokens that are compatible with automated modular synthesis, mCLM outperforms all baselines in property scores and synthetic accessibility. mCLM can also reason on multiple functions and iteratively self-improve to rescue drug candidates that failed late in clinical trials (\"fallen angels\").", "authors": ["Carl Edwards", "Chi Han", "Gawon Lee", "Thao Nguyen", "Sara Szymkuć", "Chetan Kumar Prasad", "Bowen Jin", "Jiawei Han", "Ying Diao", "Ge Liu", "Hao Peng", "Bartosz A. Grzybowski", "Martin D. Burke", "Heng Ji"], "categories": ["cs.AI", "cs.CL", "cs.LG", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-05-18", "url": "https://arxiv.org/abs/2505.12565", "pdf_url": "https://arxiv.org/pdf/2505.12565v3", "arxiv_id": "2505.12565", "doi": null, "citation_count": 3, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/blender-nlp/mCLM", "venue": null, "quality_score": 0.1936}
{"id": "7b2dd90cd28ccae9661abea23df5c16eede0bda264c9f056c1b7bd2df7ae5146", "sources": ["arxiv", "semantic_scholar"], "title": "Retrospex: Language Agent Meets Offline Reinforcement Learning Critic", "abstract": "Large Language Models (LLMs) possess extensive knowledge and commonsense reasoning capabilities, making them valuable for creating powerful agents. However, existing LLM agent frameworks have not fully utilized past experiences for improvement. This work introduces a new LLM-based agent framework called Retrospex, which addresses this challenge by analyzing past experiences in depth. Unlike previous approaches, Retrospex does not directly integrate experiences into the LLM's context. Instead, it combines the LLM's action likelihood with action values estimated by a Reinforcement Learning (RL) Critic, which is trained on past experiences through an offline ''retrospection'' process. Additionally, Retrospex employs a dynamic action rescoring mechanism that increases the importance of experience-based values for tasks that require more interaction with the environment. We evaluate Retrospex in ScienceWorld, ALFWorld and Webshop environments, demonstrating its advantages over strong, contemporary baselines.", "authors": ["Yufei Xiang", "Yiqun Shen", "Yeqin Zhang", "Cam-Tu Nguyen"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-17", "url": "https://arxiv.org/abs/2505.11807", "pdf_url": "https://arxiv.org/pdf/2505.11807v2", "arxiv_id": "2505.11807", "doi": "10.18653/v1/2024.emnlp-main.268", "citation_count": 10, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.2603}
{"id": "e1b1e22412cee9a8413633521fbc8924a00879cae51fd11404dee29ec89e416c", "sources": ["arxiv", "semantic_scholar"], "title": "Prot2Text-V2: Protein Function Prediction with Multimodal Contrastive Alignment", "abstract": "Predicting protein function from sequence is a central challenge in computational biology. While existing methods rely heavily on structured ontologies or similarity-based techniques, they often lack the flexibility to express structure-free functional descriptions and novel biological functions. In this work, we introduce Prot2Text-V2, a novel multimodal sequence-to-text model that generates free-form natural language descriptions of protein function directly from amino acid sequences. Our method combines a protein language model as a sequence encoder (ESM-3B) and a decoder-only language model (LLaMA-3.1-8B-Instruct) through a lightweight nonlinear modality projector. A key innovation is our Hybrid Sequence-level Contrastive Alignment Learning (H-SCALE), which improves cross-modal learning by matching mean- and std-pooled protein embeddings with text representations via contrastive loss. After the alignment phase, we apply instruction-based fine-tuning using LoRA on the decoder to teach the model how to generate accurate protein function descriptions conditioned on the protein sequence. We train Prot2Text-V2 on about 250K curated entries from SwissProt and evaluate it under low-homology conditions, where test sequences have low similarity with training samples. Prot2Text-V2 consistently outperforms traditional and LLM-based baselines across various metrics.", "authors": ["Xiao Fei", "Michail Chatzianastasis", "Sarah Almeida Carneiro", "Hadi Abdine", "Lawrence P. Petalidis", "Michalis Vazirgiannis"], "categories": ["cs.CE"], "fields_of_study": ["Computer Science"], "published_date": "2025-05-16", "url": "https://arxiv.org/abs/2505.11194", "pdf_url": "https://arxiv.org/pdf/2505.11194v3", "arxiv_id": "2505.11194", "doi": "10.48550/arXiv.2505.11194", "citation_count": 9, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.25}
{"id": "27c49389010ae89a0d9358f1a30f7dee6c74f0893247838e9b585bbe6ce043c9", "sources": ["arxiv", "semantic_scholar"], "title": "PSBench: a large-scale benchmark for estimating the accuracy of protein complex structural models", "abstract": "Predicting protein complex structures is essential for protein function analysis, protein design, and drug discovery. While AI methods like AlphaFold can predict accurate structural models for many protein complexes, reliably estimating the quality of these predicted models (estimation of model accuracy, or EMA) for model ranking and selection remains a major challenge. A key barrier to developing effective machine learning-based EMA methods is the lack of large, diverse, and well-annotated datasets for training and evaluation. To address this gap, we introduce PSBench, a benchmark suite comprising four large-scale, labeled datasets generated during the 15th and 16th community-wide Critical Assessment of Protein Structure Prediction (CASP15 and CASP16). PSBench includes over one million structural models covering a wide range of protein sequence lengths, complex stoichiometries, functional classes, and modeling difficulties. Each model is annotated with multiple complementary quality scores at the global, local, and interface levels. PSBench also provides multiple evaluation metrics and baseline EMA methods to facilitate rigorous comparisons. To demonstrate PSBench's utility, we trained and evaluated GATE, a graph transformer-based EMA method, on the CASP15 data. GATE was blindly tested in CASP16 (2024), where it ranked among the top-performing EMA methods. These results highlight PSBench as a valuable resource for advancing EMA research in protein complex modeling. PSBench is publicly available at: https://github.com/BioinfoMachineLearning/PSBench.", "authors": ["Pawan Neupane", "Jian Liu", "Jianlin Cheng"], "categories": ["q-bio.BM", "cs.AI", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2025-05-13", "url": "https://arxiv.org/abs/2505.22674", "pdf_url": "https://arxiv.org/pdf/2505.22674v1", "arxiv_id": "2505.22674", "doi": "10.48550/arXiv.2505.22674", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/BioinfoMachineLearning/PSBench", "venue": "arXiv.org", "quality_score": 0.2444}
{"id": "2cdebf922251b59288caffdb14c04ce3766d093edb67b5d8c93dd5f967c3e151", "sources": ["arxiv", "semantic_scholar"], "title": "Protein FID: Improved Evaluation of Protein Structure Generative Models", "abstract": "Protein structure generative models have seen a recent surge of interest, but meaningfully evaluating them computationally is an active area of research. While current metrics have driven useful progress, they do not capture how well models sample the design space represented by the training data. We argue for a protein Frechet Inception Distance (FID) metric to supplement current evaluations with a measure of distributional similarity in a semantically meaningful latent space. Our FID behaves desirably under protein structure perturbations and correctly recapitulates similarities between protein samples: it correlates with optimal transport distances and recovers FoldSeek clusters and the CATH hierarchy. Evaluating current protein structure generative models with FID shows that they fall short of modeling the distribution of PDB proteins.", "authors": ["Felix Faltings", "Hannes Stark", "Tommi Jaakkola", "Regina Barzilay"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology", "Computer Science", "Medicine"], "published_date": "2025-05-12", "url": "https://arxiv.org/abs/2505.08041", "pdf_url": "https://arxiv.org/pdf/2505.08041v3", "arxiv_id": "2505.08041", "doi": "10.1093/bioinformatics/btag156", "citation_count": 4, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "636824137d738d7ae15462c9e0dd66a83ebc33e7bfefa785d71b8644117cfd5f", "sources": ["arxiv", "semantic_scholar"], "title": "LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization", "abstract": "Recent advances in Protein Structure Prediction Models (PPMs), such as AlphaFold2 and ESMFold, have revolutionized computational biology by achieving unprecedented accuracy in predicting three-dimensional protein folding structures. However, these models face significant scalability challenges, particularly when processing proteins with long amino acid sequences (e.g., sequence length > 1,000). The primary bottleneck that arises from the exponential growth in activation sizes is driven by the unique data structure in PPM, which introduces an additional dimension that leads to substantial memory and computational demands. These limitations have hindered the effective scaling of PPM for real-world applications, such as analyzing large proteins or complex multimers with critical biological and pharmaceutical relevance. In this paper, we present LightNobel, the first hardware-software co-designed accelerator developed to overcome scalability limitations on the sequence length in PPM. At the software level, we propose Token-wise Adaptive Activation Quantization (AAQ), which leverages unique token-wise characteristics, such as distogram patterns in PPM activations, to enable fine-grained quantization techniques without compromising accuracy. At the hardware level, LightNobel integrates the multi-precision reconfigurable matrix processing unit (RMPU) and versatile vector processing unit (VVPU) to enable the efficient execution of AAQ. Through these innovations, LightNobel achieves up to 8.44x, 8.41x speedup and 37.29x, 43.35x higher power efficiency over the latest NVIDIA A100 and H100 GPUs, respectively, while maintaining negligible accuracy loss. It also reduces the peak memory requirement up to 120.05x in PPM, enabling scalable processing for proteins with long sequences.", "authors": ["Seunghee Han", "Soongyu Choi", "Joo-Young Kim"], "categories": ["cs.AR", "cs.AI", "cs.ET", "cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-05-09", "url": "https://arxiv.org/abs/2505.05893", "pdf_url": "https://arxiv.org/pdf/2505.05893v1", "arxiv_id": "2505.05893", "doi": "10.1145/3695053.3731006", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Symposium on Computer Architecture", "quality_score": 0.1535}
{"id": "963314eb269d2e2345cc092199d74b33dedf6d3c4c887720ce0cda7a84e163de", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring zero-shot structure-based protein fitness prediction", "abstract": "The ability to make zero-shot predictions about the fitness consequences of protein sequence changes with pre-trained machine learning models enables many practical applications. Such models can be applied for downstream tasks like genetic variant interpretation and protein engineering without additional labeled data. The advent of capable protein structure prediction tools has led to the availability of orders of magnitude more precomputed predicted structures, giving rise to powerful structure-based fitness prediction models. Through our experiments, we assess several modeling choices for structure-based models and their effects on downstream fitness prediction. Zero-shot fitness prediction models can struggle to assess the fitness landscape within disordered regions of proteins, those that lack a fixed 3D structure. We confirm the importance of matching protein structures to fitness assays and find that predicted structures for disordered regions can be misleading and affect predictive performance. Lastly, we evaluate an additional structure-based model on the ProteinGym substitution benchmark and show that simple multi-modal ensembles are strong baselines.", "authors": ["Arnav Sharma", "Anthony Gitter"], "categories": ["q-bio.QM", "cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-04-23", "url": "https://arxiv.org/abs/2504.16886", "pdf_url": "https://arxiv.org/pdf/2504.16886v1", "arxiv_id": "2504.16886", "doi": "10.48550/arXiv.2504.16886", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "afb2322ea7aa59d0c4f288dfa2b5fb493ab895304f81da54185830317f4f52af", "sources": ["arxiv", "semantic_scholar"], "title": "Enhancing TCR-Peptide Interaction Prediction with Pretrained Language Models and Molecular Representations", "abstract": "Understanding the binding specificity between T-cell receptors (TCRs) and peptide-major histocompatibility complexes (pMHCs) is central to immunotherapy and vaccine development. However, current predictive models struggle with generalization, especially in data-scarce settings and when faced with novel epitopes. We present LANTERN (Large lAnguage model-powered TCR-Enhanced Recognition Network), a deep learning framework that combines large-scale protein language models with chemical representations of peptides. By encoding TCR \\b{eta}-chain sequences using ESM-1b and transforming peptide sequences into SMILES strings processed by MolFormer, LANTERN captures rich biological and chemical features critical for TCR-peptide recognition. Through extensive benchmarking against existing models such as ChemBERTa, TITAN, and NetTCR, LANTERN demonstrates superior performance, particularly in zero-shot and few-shot learning scenarios. Our model also benefits from a robust negative sampling strategy and shows significant clustering improvements via embedding analysis. These results highlight the potential of LANTERN to advance TCR-pMHC binding prediction and support the development of personalized immunotherapies.", "authors": ["Cong Qi", "Hanzhang Fang", "Siqi jiang", "Tianxing Hu", "Zhi Wei"], "categories": ["q-bio.QM", "cs.CL", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-04-22", "url": "https://arxiv.org/abs/2505.01433", "pdf_url": "https://arxiv.org/pdf/2505.01433v2", "arxiv_id": "2505.01433", "doi": "10.48550/arXiv.2505.01433", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "75270270ab32be2b14b2ff55d3516cf071c37a467a2ab403066bf3438e31726f", "sources": ["arxiv", "semantic_scholar"], "title": "Hierarchical protein backbone generation with latent and structure diffusion", "abstract": "We propose a hierarchical protein backbone generative model that separates coarse and fine-grained details. Our approach called LSD consists of two stages: sampling latents which are decoded into a contact map then sampling atomic coordinates conditioned on the contact map. LSD allows new ways to control protein generation towards desirable properties while scaling to large datasets. In particular, the AlphaFold DataBase (AFDB) is appealing due as its diverse structure topologies but suffers from poor designability. We train LSD on AFDB and show latent diffusion guidance towards AlphaFold2 Predicted Alignment Error and long range contacts can explicitly balance designability, diversity, and noveltys in the generated samples. Our results are competitive with structure diffusion models and outperforms prior latent diffusion models.", "authors": ["Jason Yim", "Marouane Jaakik", "Ge Liu", "Jacob Gershon", "Karsten Kreis", "David Baker", "Regina Barzilay", "Tommi Jaakkola"], "categories": ["q-bio.QM"], "fields_of_study": ["Biology"], "published_date": "2025-04-12", "url": "https://arxiv.org/abs/2504.09374", "pdf_url": "https://arxiv.org/pdf/2504.09374v1", "arxiv_id": "2504.09374", "doi": null, "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "8dc4b056fbf6a1bbda231b1dc003278466b3ab62865895f7e996e7f222d9786d", "sources": ["arxiv", "semantic_scholar"], "title": "Mass Balance Approximation of Unfolding Improves Potential-Like Methods for Protein Stability Predictions", "abstract": "The prediction of protein stability changes following single-point mutations plays a pivotal role in computational biology, particularly in areas like drug discovery, enzyme reengineering, and genetic disease analysis. Although deep-learning strategies have pushed the field forward, their use in standard workflows remains limited due to resource demands. Conversely, potential-like methods are fast, intuitive, and efficient. Yet, these typically estimate Gibbs free energy shifts without considering the free-energy variations in the unfolded protein state, an omission that may breach mass balance and diminish accuracy. This study shows that incorporating a mass-balance correction (MBC) to account for the unfolded state significantly enhances these methods. While many machine learning models partially model this balance, our analysis suggests that a refined representation of the unfolded state may improve the predictive performance.", "authors": ["Ivan Rossi", "Guido Barducci", "Tiziana Sanavia", "Paola Turina", "Emidio Capriotti", "Piero Fariselli"], "categories": ["q-bio.QM", "cs.LG", "physics.bio-ph"], "fields_of_study": ["Biology", "Computer Science", "Physics", "Medicine"], "published_date": "2025-04-09", "url": "https://arxiv.org/abs/2504.06806", "pdf_url": "https://arxiv.org/pdf/2504.06806v1", "arxiv_id": "2504.06806", "doi": "10.1002/pro.70134", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Protein Science", "quality_score": 0.1192}
{"id": "cf49baa7e01ca56e39ecab9e8c4683189ad5647e6b551ac4b3e5f51fa7481ced", "sources": ["arxiv", "semantic_scholar"], "title": "Prot42: a Novel Family of Protein Language Models for Target-aware Protein Binder Generation", "abstract": "Unlocking the next generation of biotechnology and therapeutic innovation demands overcoming the inherent complexity and resource-intensity of conventional protein engineering methods. Recent GenAI-powered computational techniques often rely on the availability of the target protein's 3D structures and specific binding sites to generate high-affinity binders, constraints exhibited by models such as AlphaProteo and RFdiffusion. In this work, we explore the use of Protein Language Models (pLMs) for high-affinity binder generation. We introduce Prot42, a novel family of Protein Language Models (pLMs) pretrained on vast amounts of unlabeled protein sequences. By capturing deep evolutionary, structural, and functional insights through an advanced auto-regressive, decoder-only architecture inspired by breakthroughs in natural language processing, Prot42 dramatically expands the capabilities of computational protein design based on language only. Remarkably, our models handle sequences up to 8,192 amino acids, significantly surpassing standard limitations and enabling precise modeling of large proteins and complex multi-domain sequences. Demonstrating powerful practical applications, Prot42 excels in generating high-affinity protein binders and sequence-specific DNA-binding proteins. Our innovative models are publicly available, offering the scientific community an efficient and precise computational toolkit for rapid protein engineering.", "authors": ["Mohammad Amaan Sayeed", "Engin Tekin", "Maryam Nadeem", "Nancy A. ElNaker", "Aahan Singh", "Natalia Vassilieva", "Boulbaba Ben Amor"], "categories": ["q-bio.BM", "cs.AI", "cs.CL", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-04-06", "url": "https://arxiv.org/abs/2504.04453", "pdf_url": "https://arxiv.org/pdf/2504.04453v2", "arxiv_id": "2504.04453", "doi": "10.48550/arXiv.2504.04453", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "a566ce5dae25c97202c197812ccae9b125ad068153ce9c3796f70307a2cb94c9", "sources": ["arxiv", "semantic_scholar"], "title": "Redefining technology for indigenous languages", "abstract": "In this paper, we offer an overview of indigenous languages, identifying the causes of their devaluation and the need for legislation on language rights. We review the technologies used to revitalize these languages, finding that when they come from outside, they often have the opposite effect to what they seek; however, when developed from within communities, they become powerful instruments of expression. We propose that the inclusion of Indigenous knowledge in large language models (LLMs) will enrich the technological landscape, but must be done in a participatory environment that encourages the exchange of knowledge.", "authors": ["Silvia Fernandez-Sabido", "Laura Peniche-Sabido"], "categories": ["cs.CY", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-04-02", "url": "https://arxiv.org/abs/2504.01522", "pdf_url": "https://arxiv.org/pdf/2504.01522v1", "arxiv_id": "2504.01522", "doi": "10.48550/arXiv.2504.01522", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "03f8a851f506dedca933fb4e1b20645ea38a071300e5ac23e7c47c55efd07ff2", "sources": ["arxiv", "semantic_scholar"], "title": "Deep Learning-Driven Protein Structure Prediction and Design: Key Model Developments by Nobel Laureates and Multi-Domain Applications", "abstract": "This systematic review outlines pivotal advancements in deep learning-driven protein structure prediction and design, focusing on four core models-AlphaFold, RoseTTAFold, RFDiffusion, and ProteinMPNN-developed by 2024 Nobel Laureates in Chemistry: David Baker, Demis Hassabis, and John Jumper. We analyze their technological iterations and collaborative design paradigms, emphasizing breakthroughs in atomic-level structural accuracy, functional protein engineering, and multi-component biomolecular interaction modeling. Key innovations include AlphaFold3's diffusion-based framework for unified biomolecular prediction, RoseTTAFold's three-track architecture integrating sequence and spatial constraints, RFDiffusion's denoising diffusion for de novo protein generation, and ProteinMPNN's inverse folding for sequence-structure co-optimization. Despite transformative progress in applications such as binder design, nanomaterials, and enzyme engineering, challenges persist in dynamic conformational sampling, multimodal data integration, and generalization to non-canonical targets. We propose future directions, including hybrid physics-AI frameworks and multimodal learning, to bridge gaps between computational design and functional validation in cellular environments.", "authors": ["Wanqing Yang", "Yanwei Wang", "Yang Wang"], "categories": ["physics.bio-ph"], "fields_of_study": ["Physics"], "published_date": "2025-04-02", "url": "https://arxiv.org/abs/2504.01490", "pdf_url": "https://arxiv.org/pdf/2504.01490v1", "arxiv_id": "2504.01490", "doi": "10.1063/5.0273394", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Biophysical Reviews", "quality_score": 0.1505}
{"id": "d9dda0d443c7e8c9b1fd997dd66d9fc005c470f956501cccbc1ab88d0c3ff2ff", "sources": ["arxiv", "semantic_scholar"], "title": "Whisper-LM: Improving ASR Models with Language Models for Low-Resource Languages", "abstract": "Automatic speech recognition systems have undoubtedly advanced with the integration of multilingual and multitask models such as Whisper, which have shown a promising ability to understand and process speech across a wide range of languages. Despite their robustness, these models often fall short in handling the linguistic distinctions of minority languages. This study addresses this gap by integrating traditional and novel language models with fine-tuned Whisper models to raise their performance in less commonly studied languages. Through rigorous fine-tuning and evaluation across multiple datasets, we demonstrate substantial improvements in word error rate, particularly in low-resource scenarios. Our approach not only does take advantage of the extensive data Whisper was pre-trained on, but also complements its linguistic adaptability by incorporating language models. We obtained improvements up to 51% for in-distribution datasets and up to 34% for out-of-distribution sentences using statistical language models, while large language models provided moderate but consistently robust improvement across diverse linguistic contexts. The findings reveal that, while the integration reliably benefits all model sizes, the extent of improvement varies, highlighting the importance of optimized language model parameters. Finally, we emphasize the importance of selecting appropriate evaluation parameters when reporting the results using transformer-based ASR models. In summary, this research clears the way for more inclusive ASR technologies that perform better across languages by enriching their linguistic knowledge. For further implementation details of this study, the technical documentation and source code are available at http://www.github.com/hitz-zentroa/whisper-lm.", "authors": ["Xabier de Zuazo", "Eva Navas", "Ibon Saratxaga", "Inma Hernáez Rioja"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-30", "url": "https://arxiv.org/abs/2503.23542", "pdf_url": "https://arxiv.org/pdf/2503.23542v1", "arxiv_id": "2503.23542", "doi": "10.48550/arXiv.2503.23542", "citation_count": 7, "influential_citation_count": 1, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "2c472c1f645ae9baaf258fcc8b6eb9a6704e65ff6d17c8d26451f35fa9426f53", "sources": ["arxiv", "semantic_scholar"], "title": "MSNGO: multi-species protein function annotation based on 3D protein structure and network propagation", "abstract": "Motivation: In recent years, protein function prediction has broken through the bottleneck of sequence features, significantly improving prediction accuracy using high-precision protein structures predicted by AlphaFold2. While single-species protein function prediction methods have achieved remarkable success, multi-species protein function prediction methods are still in the stage of using PPI networks and sequence features. Providing effective cross-species label propagation for species with sparse protein annotations remains a challenging issue. To address this problem, we propose the MSNGO model, which integrates structural features and network propagation methods. Our validation shows that using structural features can significantly improve the accuracy of multi-species protein function prediction. Results: We employ graph representation learning techniques to extract amino acid representations from protein structure contact maps and train a structural model using a graph convolution pooling module to derive protein-level structural features. After incorporating the sequence features from ESM-2, we apply a network propagation algorithm to aggregate information and update node representations within a heterogeneous network. The results demonstrate that MSNGO outperforms previous multi-species protein function prediction methods that rely on sequence features and PPI networks. Availability: https://github.com/blingbell/MSNGO.", "authors": ["Beibei Wang", "Boyue Cui", "Shiqu Chen", "Xuan Wang", "Yadong Wang", "Junyi Li"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2025-03-29", "url": "https://arxiv.org/abs/2503.23014", "pdf_url": "https://arxiv.org/pdf/2503.23014v1", "arxiv_id": "2503.23014", "doi": "10.1093/bioinformatics/btaf285", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/blingbell/MSNGO", "venue": null, "quality_score": 0.1259}
{"id": "821ee1cc8c1434c0619135bd61b4214eefca6e79fc953982a09c82e1b173ebe5", "sources": ["arxiv", "semantic_scholar"], "title": "How do language models learn facts? Dynamics, curricula and hallucinations", "abstract": "Large language models accumulate vast knowledge during pre-training, yet the dynamics governing this acquisition remain poorly understood. This work investigates the learning dynamics of language models on a synthetic factual recall task, uncovering three key findings: First, language models learn in three phases, exhibiting a performance plateau before acquiring precise factual knowledge. Mechanistically, this plateau coincides with the formation of attention-based circuits that support recall. Second, the training data distribution significantly impacts learning dynamics, as imbalanced distributions lead to shorter plateaus. Finally, hallucinations emerge simultaneously with knowledge, and integrating new knowledge into the model through fine-tuning is challenging, as it quickly corrupts its existing parametric memories. Our results emphasize the importance of data distribution in knowledge acquisition and suggest novel data scheduling strategies to accelerate neural network training.", "authors": ["Nicolas Zucchet", "Jörg Bornschein", "Stephanie Chan", "Andrew Lampinen", "Razvan Pascanu", "Soham De"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-27", "url": "https://arxiv.org/abs/2503.21676", "pdf_url": "https://arxiv.org/pdf/2503.21676v2", "arxiv_id": "2503.21676", "doi": "10.48550/arXiv.2503.21676", "citation_count": 28, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3656}
{"id": "46e883a42cd3cdb7d791ac0bd1deebc82bf982ca0ed7d7c85179075a058e2e8f", "sources": ["arxiv", "semantic_scholar"], "title": "Untangling the Influence of Typology, Data and Model Architecture on Ranking Transfer Languages for Cross-Lingual POS Tagging", "abstract": "Cross-lingual transfer learning is an invaluable tool for overcoming data scarcity, yet selecting a suitable transfer language remains a challenge. The precise roles of linguistic typology, training data, and model architecture in transfer language choice are not fully understood. We take a holistic approach, examining how both dataset-specific and fine-grained typological features influence transfer language selection for part-of-speech tagging, considering two different sources for morphosyntactic features. While previous work examines these dynamics in the context of bilingual biLSTMS, we extend our analysis to a more modern transfer learning pipeline: zero-shot prediction with pretrained multilingual models. We train a series of transfer language ranking systems and examine how different feature inputs influence ranker performance across architectures. Word overlap, type-token ratio, and genealogical distance emerge as top features across all architectures. Our findings reveal that a combination of typological and dataset-dependent features leads to the best rankings, and that good performance can be obtained with either feature group on its own.", "authors": ["Enora Rice", "Ali Marashian", "Hannah Haynie", "Katharina von der Wense", "Alexis Palmer"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-25", "url": "https://arxiv.org/abs/2503.19979", "pdf_url": "https://arxiv.org/pdf/2503.19979v1", "arxiv_id": "2503.19979", "doi": "10.48550/arXiv.2503.19979", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2258}
{"id": "7b708d8632039afac42213ec7243fceb22274a35056a7e71111d31960962e839", "sources": ["arxiv", "semantic_scholar"], "title": "An Energy-Adaptive Elastic Equivariant Transformer Framework for Protein Structure Representation", "abstract": "Structure-informed protein representation learning is essential for effective protein function annotation and \\textit{de novo} design. However, the presence of inherent noise in both crystal and AlphaFold-predicted structures poses significant challenges for existing methods in learning robust protein representations. To address these issues, we propose a novel equivariant Transformer-State Space Model(SSM) hybrid framework, termed $E^3$former, designed for efficient protein representation. Our approach uses energy function-based receptive fields to construct proximity graphs and incorporates an equivariant high-tensor-elastic selective SSM within the transformer architecture. These components enable the model to adapt to complex atom interactions and extract geometric features with higher signal-to-noise ratios. Empirical results demonstrate that our model outperforms existing methods in structure-intensive tasks, such as inverse folding and binding site prediction, particularly when using predicted structures, owing to its enhanced tolerance to data deviation and noise. Our approach offers a novel perspective for conducting biological function research and drug discovery using noisy protein structure data.", "authors": ["Zhongyue Zhang", "Runze Ma", "Yanjie Huang", "Shuangjia Zheng"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2025-03-21", "url": "https://arxiv.org/abs/2503.16996", "pdf_url": "https://arxiv.org/pdf/2503.16996v2", "arxiv_id": "2503.16996", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.062}
{"id": "59f4bbaec903b04997379e7c6962630592cadb7609ec8f637c98c9cdca814ba2", "sources": ["arxiv", "semantic_scholar"], "title": "VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning", "abstract": "Natural language processing (NLP) has significantly influenced scientific domains beyond human language, including protein engineering, where pre-trained protein language models (PLMs) have demonstrated remarkable success. However, interdisciplinary adoption remains limited due to challenges in data collection, task benchmarking, and application. This work presents VenusFactory, a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs. VenusFactory supports both computer science and biology communities with choices of both a command-line execution and a Gradio-based no-code interface, integrating $40+$ protein-related datasets and $40+$ popular PLMs. All implementations are open-sourced on https://github.com/tyang816/VenusFactory.", "authors": ["Yang Tan", "Chen Liu", "Jingyuan Gao", "Banghao Wu", "Mingchen Li", "Ruilin Wang", "Lingrong Zhang", "Huiqun Yu", "Guisheng Fan", "Liang Hong", "Bingxin Zhou"], "categories": ["cs.CL", "cs.AI", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-03-19", "url": "https://arxiv.org/abs/2503.15438", "pdf_url": "https://arxiv.org/pdf/2503.15438v1", "arxiv_id": "2503.15438", "doi": "10.48550/arXiv.2503.15438", "citation_count": 6, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/tyang816/VenusFactory", "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "3ae3f6a8ba9678a64763fe7a464165efc46ea6963da15dad7541f6b2b6f2c495", "sources": ["arxiv", "semantic_scholar"], "title": "Advanced Deep Learning Methods for Protein Structure Prediction and Design", "abstract": "After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to protein structure prediction and design. It begins by examining recent innovations in prediction architectures, with detailed discussions on improvements such as diffusion based frameworks and novel pairwise attention modules. The text analyses key components including structure generation, evaluation metrics, multiple sequence alignment processing, and network architecture, thereby illustrating the current state of the art in computational protein modelling. Subsequent chapters focus on practical applications, presenting case studies that range from individual protein predictions to complex biomolecular interactions. Strategies for enhancing prediction accuracy and integrating deep learning techniques with experimental validation are thoroughly explored. The later sections review the industry landscape of protein design, highlighting the transformative role of artificial intelligence in biotechnology and discussing emerging market trends and future challenges. Supplementary appendices provide essential resources such as databases and open source tools, making this volume a valuable reference for researchers and students.", "authors": ["Yichao Zhang", "Ningyuan Deng", "Xinyuan Song", "Ziqian Bi", "Tianyang Wang", "Zheyu Yao", "Keyu Chen", "Ming Li", "Qian Niu", "Junyu Liu", "Benji Peng", "Sen Zhang", "Ming Liu", "Li Zhang", "Xuanhe Pan", "Jinlang Wang", "Pohsun Feng", "Yizhu Wen", "Lawrence KQ Yan", "Hongming Tseng", "Yan Zhong", "Yunze Wang", "Ziyuan Qin", "Bowen Jing", "Junjie Yang", "Jun Zhou", "Chia Xin Liang", "Junhao Song"], "categories": ["q-bio.BM", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-03-14", "url": "https://arxiv.org/abs/2503.13522", "pdf_url": "https://arxiv.org/pdf/2503.13522v3", "arxiv_id": "2503.13522", "doi": "10.48550/arXiv.2503.13522", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1381}
{"id": "19c8074f67488059ca22bb2e779f4ef7effcc8b0d3b7258dcbbd97f6e94c326e", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Interpretable Protein Structure Prediction with Sparse Autoencoders", "abstract": "Protein language models have revolutionized structure prediction, but their nonlinear nature obscures how sequence representations inform structure prediction. While sparse autoencoders (SAEs) offer a path to interpretability here by learning linear representations in high-dimensional space, their application has been limited to smaller protein language models unable to perform structure prediction. In this work, we make two key advances: (1) we scale SAEs to ESM2-3B, the base model for ESMFold, enabling mechanistic interpretability of protein structure prediction for the first time, and (2) we adapt Matryoshka SAEs for protein language models, which learn hierarchically organized features by forcing nested groups of latents to reconstruct inputs independently. We demonstrate that our Matryoshka SAEs achieve comparable or better performance than standard architectures. Through comprehensive evaluations, we show that SAEs trained on ESM2-3B significantly outperform those trained on smaller models for both biological concept discovery and contact map prediction. Finally, we present an initial case study demonstrating how our approach enables targeted steering of ESMFold predictions, increasing structure solvent accessibility while fixing the input sequence. To facilitate further investigation by the broader community, we open-source our code, dataset, pretrained models https://github.com/johnyang101/reticular-sae , and visualizer https://sae.reticular.ai .", "authors": ["Nithin Parsan", "David J. Yang", "John J. Yang"], "categories": ["q-bio.BM", "cs.AI", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2025-03-11", "url": "https://arxiv.org/abs/2503.08764", "pdf_url": "https://arxiv.org/pdf/2503.08764v1", "arxiv_id": "2503.08764", "doi": "10.48550/arXiv.2503.08764", "citation_count": 15, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/johnyang101/reticular-sae", "venue": "arXiv.org", "quality_score": 0.301}
{"id": "3d3148436e5150410021c3e184caafa9fefe69fe0c965813f99217391bf5e7ed", "sources": ["arxiv", "semantic_scholar"], "title": "ProtTeX: Structure-In-Context Reasoning and Editing of Proteins with Large Language Models", "abstract": "Large language models have made remarkable progress in the field of molecular science, particularly in understanding and generating functional small molecules. This success is largely attributed to the effectiveness of molecular tokenization strategies. In protein science, the amino acid sequence serves as the sole tokenizer for LLMs. However, many fundamental challenges in protein science are inherently structure-dependent. The absence of structure-aware tokens significantly limits the capabilities of LLMs for comprehensive biomolecular comprehension and multimodal generation. To address these challenges, we introduce a novel framework, ProtTeX, which tokenizes the protein sequences, structures, and textual information into a unified discrete space. This innovative approach enables joint training of the LLM exclusively through the Next-Token Prediction paradigm, facilitating multimodal protein reasoning and generation. ProtTeX enables general LLMs to perceive and process protein structures through sequential text input, leverage structural information as intermediate reasoning components, and generate or manipulate structures via sequential text output. Experiments demonstrate that our model achieves significant improvements in protein function prediction, outperforming the state-of-the-art domain expert model with a twofold increase in accuracy. Our framework enables high-quality conformational generation and customizable protein design. For the first time, we demonstrate that by adopting the standard training and inference pipelines from the LLM domain, ProtTeX empowers decoder-only LLMs to effectively address diverse spectrum of protein-related tasks.", "authors": ["Zicheng Ma", "Chuanliu Fan", "Zhicong Wang", "Zhenyu Chen", "Xiaohan Lin", "Yanheng Li", "Shihao Feng", "Jun Zhang", "Ziqiang Cao", "Yi Qin Gao"], "categories": ["q-bio.BM", "cs.AI"], "fields_of_study": ["Biology", "Computer Science", "Medicine"], "published_date": "2025-03-11", "url": "https://arxiv.org/abs/2503.08179", "pdf_url": "https://arxiv.org/pdf/2503.08179v3", "arxiv_id": "2503.08179", "doi": "10.48550/arXiv.2503.08179", "citation_count": 10, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Journal of Chemical Information and Modeling", "quality_score": 0.2603}
{"id": "af6b3da96db957f1cc1265a17d0210b40420b126272cf9bc88edbdf32599fd9f", "sources": ["arxiv", "semantic_scholar"], "title": "Gender Encoding Patterns in Pretrained Language Model Representations", "abstract": "Gender bias in pretrained language models (PLMs) poses significant social and ethical challenges. Despite growing awareness, there is a lack of comprehensive investigation into how different models internally represent and propagate such biases. This study adopts an information-theoretic approach to analyze how gender biases are encoded within various encoder-based architectures. We focus on three key aspects: identifying how models encode gender information and biases, examining the impact of bias mitigation techniques and fine-tuning on the encoded biases and their effectiveness, and exploring how model design differences influence the encoding of biases. Through rigorous and systematic investigation, our findings reveal a consistent pattern of gender encoding across diverse models. Surprisingly, debiasing techniques often exhibit limited efficacy, sometimes inadvertently increasing the encoded bias in internal representations while reducing bias in model output distributions. This highlights a disconnect between mitigating bias in output distributions and addressing its internal representations. This work provides valuable guidance for advancing bias mitigation strategies and fostering the development of more equitable language models.", "authors": ["Mahdi Zakizadeh", "Mohammad Taher Pilehvar"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-09", "url": "https://arxiv.org/abs/2503.06734", "pdf_url": "https://arxiv.org/pdf/2503.06734v1", "arxiv_id": "2503.06734", "doi": "10.48550/arXiv.2503.06734", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1193}
{"id": "48d57fb56749f8d7d348ccefc4445bf2303b6182678776f09bd86f4951deaa38", "sources": ["arxiv", "semantic_scholar"], "title": "From Language to Cognition: How LLMs Outgrow the Human Language Network", "abstract": "Large language models (LLMs) exhibit remarkable similarity to neural activity in the human language network. However, the key properties of language shaping brain-like representations, and their evolution during training as a function of different tasks remain unclear. We here benchmark 34 training checkpoints spanning 300B tokens across 8 different model sizes to analyze how brain alignment relates to linguistic competence. Specifically, we find that brain alignment tracks the development of formal linguistic competence -- i.e., knowledge of linguistic rules -- more closely than functional linguistic competence. While functional competence, which involves world knowledge and reasoning, continues to develop throughout training, its relationship with brain alignment is weaker, suggesting that the human language network primarily encodes formal linguistic structure rather than broader cognitive functions. We further show that model size is not a reliable predictor of brain alignment when controlling for feature size and find that the correlation between next-word prediction, behavioral alignment and brain alignment fades once models surpass human language proficiency. Finally, using the largest set of rigorous neural language benchmarks to date, we show that language brain alignment benchmarks remain unsaturated, highlighting opportunities for improving future models. Taken together, our findings suggest that the human language network is best modeled by formal, rather than functional, aspects of language.", "authors": ["Badr AlKhamissi", "Greta Tuckute", "Yingtian Tang", "Taha Binhuraib", "Antoine Bosselut", "Martin Schrimpf"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2025-03-03", "url": "https://arxiv.org/abs/2503.01830", "pdf_url": "https://arxiv.org/pdf/2503.01830v2", "arxiv_id": "2503.01830", "doi": "10.48550/arXiv.2503.01830", "citation_count": 29, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.3693}
{"id": "dc937d0a4a60d2a361e8c99f76ba5189942868d2bcbe1c0787a63ca623416e48", "sources": ["arxiv", "semantic_scholar"], "title": "A Model-Centric Review of Deep Learning for Protein Design", "abstract": "Deep learning has transformed protein design, enabling accurate structure prediction, sequence optimization, and de novo protein generation. Advances in single-chain protein structure prediction via AlphaFold2, RoseTTAFold, ESMFold, and others have achieved near-experimental accuracy, inspiring successive work extended to biomolecular complexes via AlphaFold Multimer, RoseTTAFold All-Atom, AlphaFold 3, Chai-1, Boltz-1 and others. Generative models such as ProtGPT2, ProteinMPNN, and RFdiffusion have enabled sequence and backbone design beyond natural evolution-based limitations. More recently, joint sequence-structure co-design models, including ESM3, have integrated both modalities into a unified framework, resulting in improved designability. Despite these advances, challenges still exist pertaining to modeling sequence-structure-function relationships and ensuring robust generalization beyond the regions of protein space spanned by the training data. Future advances will likely focus on joint sequence-structure-function co-design frameworks that are able to model the fitness landscape more effectively than models that treat these modalities independently. Current capabilities, coupled with the dizzying rate of progress, suggest that the field will soon enable rapid, rational design of proteins with tailored structures and functions that transcend the limitations imposed by natural evolution. In this review, we discuss the current capabilities of deep learning methods for protein design, focusing on some of the most revolutionary and capable models with respect to their functionality and the applications that they enable, leading up to the current challenges of the field and the optimal path forward.", "authors": ["Gregory W. Kyro", "Tianyin Qiu", "Victor S. Batista"], "categories": ["cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-02-26", "url": "https://arxiv.org/abs/2502.19173", "pdf_url": "https://arxiv.org/pdf/2502.19173v1", "arxiv_id": "2502.19173", "doi": "10.48550/arXiv.2502.19173", "citation_count": 12, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2785}
{"id": "70dfd15729b117583b73dad0dd580ae0bb543c2d623a4a54320969cec05a512d", "sources": ["arxiv", "semantic_scholar"], "title": "Leveraging Retrieval-Augmented Generation and Large Language Models to Predict SERCA-Binding Protein Fragments from Cardiac Proteomics Data", "abstract": "Large language models (LLMs) have shown promise in various natural language processing tasks, including their application to proteomics data to classify protein fragments. In this study, we curated a limited mass spectrometry dataset with 1000s of protein fragments, consisting of proteins that appear to be attached to the endoplasmic reticulum in cardiac cells, of which a fraction was cloned and characterized for their impact on SERCA, an ER calcium pump. With this limited dataset, we sought to determine whether LLMs could correctly predict whether a new protein fragment could bind SERCA, based only on its sequence and a few biophysical characteristics, such as hydrophobicity, determined from that sequence. To do so, we generated random sequences based on cloned fragments, embedded the fragments into a retrieval augmented generation (RAG) database to group them by similarity, then fine-tuned large language model (LLM) prompts to predict whether a novel sequence could bind SERCA. We benchmarked this approach using multiple open-source LLMs, namely the Meta/llama series, and embedding functions commonly available on the Huggingface repository. We then assessed the generalizability of this approach in classifying novel protein fragments from mass spectrometry that were not initially cloned for functional characterization. By further tuning the prompt to account for motifs, such as ER retention sequences, we improved the classification accuracy by and identified several proteins predicted to localize to the endoplasmic reticulum and bind SERCA, including Ribosomal Protein L2 and selenoprotein S. Although our results were based on proteomics data from cardiac cells, our approach demonstrates the potential of LLMs in identifying novel protein interactions and functions with very limited proteomic data.", "authors": ["Taylor A Phillips", "Alejandro W. Huskey", "Patrick T. Huskey", "Seth L. Robia", "Peter M. Kekenes-Huskey"], "categories": ["q-bio.QM"], "fields_of_study": ["Biology"], "published_date": "2025-02-26", "url": "https://arxiv.org/abs/2502.19574", "pdf_url": "https://arxiv.org/pdf/2502.19574v1", "arxiv_id": "2502.19574", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": null, "quality_score": 0.084}
{"id": "5d813b5a99a83d09a8b8d3effc7cb7748c20890f0a3ad61a59fce3f71ca789b0", "sources": ["arxiv", "semantic_scholar"], "title": "Characterizing the Conformational States of G Protein Coupled Receptors Generated with AlphaFold", "abstract": "G-Protein Coupled Receptors (GPCRs) are integral to numerous physiological processes and are the target of approximately one-third of FDA-approved therapeutics. Despite their significance, only a limited subset of GPCRs has been successfully targeted, primarily due to challenges in accurately modeling their structures. AlphaFold, a state-of-the-art deep learning model, has demonstrated remarkable capability in predicting protein structures with high accuracy. This study conducts an evaluation of AlphaFold performance in predicting GPCR structures and their conformational states by comparing its predictions to experimentally determined structures using metrics such as average deformation between alpha carbon atoms and the Helix 3 - Helix 6 (H3-H6) distance. Our analysis reveals that both AlphaFold 2 (AF2) and AlphaFold 3 (AF3) produce more accurate predictions for GPCRs in inactive conformations, with lower activity levels correlating with smaller deformations. Conversely, higher activity levels are associated with increased variability in AlphaFold performance due to difficulties with accurately predicting conformational changes upon GPCR activation and ligand binding. Additionally, AlphaFold performance varies across different GPCR classes, influenced by the availability and quality of training data as well as the structural complexity and diversity of the receptors. These findings demonstrate the potential of AlphaFold in advancing drug discovery efforts, while also highlighting the necessity for continued refinement to enhance predictive accuracy for active conformations.", "authors": ["Garima Chib", "Parisa Mollaei", "Amir Barati Farimani"], "categories": ["q-bio.QM", "q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2025-02-24", "url": "https://arxiv.org/abs/2502.17628", "pdf_url": "https://arxiv.org/pdf/2502.17628v1", "arxiv_id": "2502.17628", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0437}
{"id": "b9ff0a826e0448f586a091edd2a03eed75009cc0bb3434384fca57ab279a9545", "sources": ["arxiv", "semantic_scholar"], "title": "Child vs. machine language learning: Can the logical structure of human language unleash LLMs?", "abstract": "We argue that human language learning proceeds in a manner that is different in nature from current approaches to training LLMs, predicting a difference in learning biases. We then present evidence from German plural formation by LLMs that confirm our hypothesis that even very powerful implementations produce results that miss aspects of the logic inherent to language that humans have no problem with. We conclude that attention to the different structures of human language and artificial neural networks is likely to be an avenue to improve LLM performance.", "authors": ["Uli Sauerland", "Celia Matthaei", "Felix Salfner"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-02-24", "url": "https://arxiv.org/abs/2502.17304", "pdf_url": "https://arxiv.org/pdf/2502.17304v1", "arxiv_id": "2502.17304", "doi": "10.48550/arXiv.2502.17304", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0688}
{"id": "3dbcaca3b663964b2875a938c8fb7a7fd322666f34bf7e758bf43ffc45fdedfe", "sources": ["arxiv", "semantic_scholar"], "title": "Integrating protein sequence embeddings with structure via graph-based deep learning for single-residue property prediction", "abstract": "Understanding the intertwined contributions of amino acid sequence and spatial structure is essential to explain protein behaviour. Here, we introduce INFUSSE (Integrated Network Framework Unifying Structure and Sequence Embeddings), a deep learning framework for the prediction of single-residue properties that combines fine-tuning of sequence embeddings derived from a Large Language Model with the inclusion of graph-based representations of protein structures via a diffusive Graph Convolutional Network. To illustrate the benefits of jointly leveraging sequence and structure, we apply INFUSSE to the prediction of B-factors in antibodies, a residue property that reflects the local flexibility shaped by biochemical and structural constraints in these highly variable and dynamic proteins. Using a dataset of 1510 antibody and antibody-antigen complexes from the database SAbDab, we show that INFUSSE improves performance over current machine learning (ML) methods based on sequence or structure alone, and allows for the systematic disentanglement of sequence and structure contributions to the performance. Our results show that adding structural information via geometric graphs enhances predictions especially for intrinsically disordered regions, protein-protein interaction sites, and highly variable amino acid positions -- all key structural features for antibody function which are not well captured by purely sequence-based ML descriptions.", "authors": ["Kevin Michalewicz", "Mauricio Barahona", "Barbara Bravi"], "categories": ["q-bio.QM"], "fields_of_study": ["Biology"], "published_date": "2025-02-24", "url": "https://arxiv.org/abs/2502.17294", "pdf_url": "https://arxiv.org/pdf/2502.17294v2", "arxiv_id": "2502.17294", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0437}
{"id": "57eb5718bd228be3ae7fe6d939adc909a22da46c9679fb5e436307041341ca43", "sources": ["arxiv", "semantic_scholar"], "title": "Protein Large Language Models: A Comprehensive Survey", "abstract": "Protein-specific large language models (Protein LLMs) are revolutionizing protein science by enabling more efficient protein structure prediction, function annotation, and design. While existing surveys focus on specific aspects or applications, this work provides the first comprehensive overview of Protein LLMs, covering their architectures, training datasets, evaluation metrics, and diverse applications. Through a systematic analysis of over 100 articles, we propose a structured taxonomy of state-of-the-art Protein LLMs, analyze how they leverage large-scale protein sequence data for improved accuracy, and explore their potential in advancing protein engineering and biomedical research. Additionally, we discuss key challenges and future directions, positioning Protein LLMs as essential tools for scientific discovery in protein science. Resources are maintained at https://github.com/Yijia-Xiao/Protein-LLM-Survey.", "authors": ["Yijia Xiao", "Wanjia Zhao", "Junkai Zhang", "Yiqiao Jin", "Han Zhang", "Zhicheng Ren", "Renliang Sun", "Haixin Wang", "Guancheng Wan", "Pan Lu", "Xiao Luo", "Yu Zhang", "James Zou", "Yizhou Sun", "Wei Wang"], "categories": ["q-bio.BM", "cs.AI", "cs.CE", "cs.CL", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2025-02-21", "url": "https://arxiv.org/abs/2502.17504", "pdf_url": "https://arxiv.org/pdf/2502.17504v2", "arxiv_id": "2502.17504", "doi": "10.48550/arXiv.2502.17504", "citation_count": 40, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/Yijia-Xiao/Protein-LLM-Survey", "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.4032}
{"id": "47af00d253c454e73ea820d1c675775dd8d636666e4301d3ffd42a403be0e5f8", "sources": ["arxiv", "semantic_scholar"], "title": "Interpreting and Steering Protein Language Models through Sparse Autoencoders", "abstract": "The rapid advancements in transformer-based language models have revolutionized natural language processing, yet understanding the internal mechanisms of these models remains a significant challenge. This paper explores the application of sparse autoencoders (SAE) to interpret the internal representations of protein language models, specifically focusing on the ESM-2 8M parameter model. By performing a statistical analysis on each latent component's relevance to distinct protein annotations, we identify potential interpretations linked to various protein characteristics, including transmembrane regions, binding sites, and specialized motifs. We then leverage these insights to guide sequence generation, shortlisting the relevant latent components that can steer the model towards desired targets such as zinc finger domains. This work contributes to the emerging field of mechanistic interpretability in biological sequence models, offering new perspectives on model steering for sequence design.", "authors": ["Edith Natalia Villegas Garcia", "Alessio Ansuini"], "categories": ["cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-02-13", "url": "https://arxiv.org/abs/2502.09135", "pdf_url": "https://arxiv.org/pdf/2502.09135v1", "arxiv_id": "2502.09135", "doi": "10.48550/arXiv.2502.09135", "citation_count": 18, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3197}
{"id": "303e165c21d27a6a599a70ab6c36e4557e3b68d86517fa79ac2c0d6d7d1ac78d", "sources": ["arxiv", "semantic_scholar"], "title": "Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis", "abstract": "Identification of protein-protein interactions (PPIs) helps derive cellular mechanistic understanding, particularly in the context of complex conditions such as neurodegenerative disorders, metabolic syndromes, and cancer. Large Language Models (LLMs) have demonstrated remarkable potential in predicting protein structures and interactions via automated mining of vast biomedical literature; yet their inherent uncertainty remains a key challenge for deriving reproducible findings, critical for biomedical applications. In this study, we present an uncertainty-aware adaptation of LLMs for PPI analysis, leveraging fine-tuned LLaMA-3 and BioMedGPT models. To enhance prediction reliability, we integrate LoRA ensembles and Bayesian LoRA models for uncertainty quantification (UQ), ensuring confidence-calibrated insights into protein behavior. Our approach achieves competitive performance in PPI identification across diverse disease contexts while addressing model uncertainty, thereby enhancing trustworthiness and reproducibility in computational biology. These findings underscore the potential of uncertainty-aware LLM adaptation for advancing precision medicine and biomedical research.", "authors": ["Sanket Jantre", "Tianle Wang", "Gilchan Park", "Kriti Chopra", "Nicholas Jeon", "Xiaoning Qian", "Nathan M. Urban", "Byung-Jun Yoon"], "categories": ["cs.LG", "cs.AI", "cs.CL", "stat.AP", "stat.ML"], "fields_of_study": ["Medicine", "Computer Science", "Mathematics"], "published_date": "2025-02-10", "url": "https://arxiv.org/abs/2502.06173", "pdf_url": "https://arxiv.org/pdf/2502.06173v2", "arxiv_id": "2502.06173", "doi": "10.1109/EMBC58623.2025.11253873", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Annual International Conference of the IEEE Engineering in Medicine and Biology Society", "quality_score": 0.0753}
{"id": "f6d8325db398dd74e2aaab8e9293ccd99b25345344d529d358a9ec3b23e0b94c", "sources": ["arxiv", "semantic_scholar"], "title": "PyMOLfold: Interactive Protein and Ligand Structure Prediction in PyMOL", "abstract": "PyMOLfold is a flexible and open-source plugin designed to seamlessly integrate AI-based protein structure prediction and visualization within the widely used PyMOL molecular graphics system. By leveraging state-of-the-art protein folding models such as ESM3, Boltz-1, and Chai-1, PyMOLfold allows researchers to directly predict protein tertiary structures from amino acid sequences without requiring external tools or complex workflows. Furthermore, with certain models, users can provide a SMILES string of a ligand and have the small molecule placed in the protein structure. This unique capability bridges the gap between computational folding and structural visualization, enabling users to input a primary sequence, perform a folding prediction, and immediately explore the resulting 3D structure within the same intuitive platform.", "authors": ["Colby T. Ford", "Samee Ullah", "Dinler Amaral Antunes", "Tarsis Gesteira Ferreira"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2025-02-01", "url": "https://arxiv.org/abs/2502.00508", "pdf_url": "https://arxiv.org/pdf/2502.00508v1", "arxiv_id": "2502.00508", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/colbyford/PyMolfold", "venue": null, "quality_score": 0.1193}
{"id": "4a4d7b6fd4fc3a9eab506e5a36c5e0b4c2118d693b368b31118bd64a232403ba", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring Large Protein Language Models in Constrained Evaluation Scenarios within the FLIP Benchmark", "abstract": "In this study, we expand upon the FLIP benchmark-designed for evaluating protein fitness prediction models in small, specialized prediction tasks-by assessing the performance of state-of-the-art large protein language models, including ESM-2 and SaProt on the FLIP dataset. Unlike larger, more diverse benchmarks such as ProteinGym, which cover a broad spectrum of tasks, FLIP focuses on constrained settings where data availability is limited. This makes it an ideal framework to evaluate model performance in scenarios with scarce task-specific data. We investigate whether recent advances in protein language models lead to significant improvements in such settings. Our findings provide valuable insights into the performance of large-scale models in specialized protein prediction tasks.", "authors": ["Manuel F. Mollon", "Joaquin Gonzalez-Rodriguez", "Alicia Lozano-Diez", "Daniel Ramos", "Doroteo T. Toledano"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2025-01-30", "url": "https://arxiv.org/abs/2501.18223", "pdf_url": "https://arxiv.org/pdf/2501.18223v1", "arxiv_id": "2501.18223", "doi": "10.48550/arXiv.2501.18223", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0401}
{"id": "b0e5d7d993174bcfb1cebdeb9d1689de3e86842c878846de1a6d539b68217f7e", "sources": ["arxiv", "semantic_scholar"], "title": "Computational Protein Science in the Era of Large Language Models (LLMs)", "abstract": "Considering the significance of proteins, computational protein science has always been a critical scientific field, dedicated to revealing knowledge and developing applications within the protein sequence-structure-function paradigm. In the last few decades, Artificial Intelligence (AI) has made significant impacts in computational protein science, leading to notable successes in specific protein modeling tasks. However, those previous AI models still meet limitations, such as the difficulty in comprehending the semantics of protein sequences, and the inability to generalize across a wide range of protein modeling tasks. Recently, LLMs have emerged as a milestone in AI due to their unprecedented language processing & generalization capability. They can promote comprehensive progress in fields rather than solving individual tasks. As a result, researchers have actively introduced LLM techniques in computational protein science, developing protein Language Models (pLMs) that skillfully grasp the foundational knowledge of proteins and can be effectively generalized to solve a diversity of sequence-structure-function reasoning problems. While witnessing prosperous developments, it's necessary to present a systematic overview of computational protein science empowered by LLM techniques. First, we summarize existing pLMs into categories based on their mastered protein knowledge, i.e., underlying sequence patterns, explicit structural and functional information, and external scientific languages. Second, we introduce the utilization and adaptation of pLMs, highlighting their remarkable achievements in promoting protein structure prediction, protein function prediction, and protein design studies. Then, we describe the practical application of pLMs in antibody design, enzyme design, and drug discovery. Finally, we specifically discuss the promising future directions in this fast-growing field.", "authors": ["Wenqi Fan", "Yi Zhou", "Shijie Wang", "Yuyao Yan", "Hui Liu", "Qian Zhao", "Le Song", "Qing Li"], "categories": ["cs.CE", "cs.CL", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2025-01-17", "url": "https://arxiv.org/abs/2501.10282", "pdf_url": "https://arxiv.org/pdf/2501.10282v2", "arxiv_id": "2501.10282", "doi": "10.48550/arXiv.2501.10282", "citation_count": 16, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3076}
{"id": "4dd4bff106bdb6c57f8d6b5142d6a41fc694c5af63bf5ff70a147a795192300f", "sources": ["arxiv", "semantic_scholar"], "title": "Protein Structure Prediction in the 3D HP Model Using Deep Reinforcement Learning", "abstract": "We address protein structure prediction in the 3D Hydrophobic-Polar lattice model through two novel deep learning architectures. For proteins under 36 residues, our hybrid reservoir-based model combines fixed random projections with trainable deep layers, achieving optimal conformations with 25% fewer training episodes. For longer sequences, we employ a long short-term memory network with multi-headed attention, matching best-known energy values. Both architectures leverage a stabilized Deep Q-Learning framework with experience replay and target networks, demonstrating consistent achievement of optimal conformations while significantly improving training efficiency compared to existing methods.", "authors": ["Giovanny Espitia", "Yui Tik Pang", "James C. Gumbart"], "categories": ["cs.LG", "cs.AI", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-12-29", "url": "https://arxiv.org/abs/2412.20329", "pdf_url": "https://arxiv.org/pdf/2412.20329v1", "arxiv_id": "2412.20329", "doi": "10.48550/arXiv.2412.20329", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "5714b0fb38088e67cd504ff5a0cda8334e9b1c6a168a92e2eab53f6a88292473", "sources": ["arxiv", "semantic_scholar"], "title": "PLD-Tree: Persistent Laplacian Decision Tree for Protein-Protein Binding Free Energy Prediction", "abstract": "Recent advances in topology-based modeling have accelerated progress in physical modeling and molecular studies, including applications to protein-ligand binding affinity. In this work, we introduce the Persistent Laplacian Decision Tree (PLD-Tree), a novel method designed to address the challenging task of predicting protein-protein interaction (PPI) affinities. PLD-Tree focuses on protein chains at binding interfaces and employs the persistent Laplacian to capture topological invariants reflecting critical inter-protein interactions. These topological descriptors, derived from persistent homology, are further enhanced by incorporating evolutionary scale modeling (ESM) from a large language model to integrate sequence-based information. We validate PLD-Tree on two benchmark datasets-PDBbind V2020 and SKEMPI v2 demonstrating a correlation coefficient ($R_p$) of 0.83 under the sophisticated leave-out-protein-out cross-validation. Notably, our approach outperforms all reported state-of-the-art methods on these datasets. These results underscore the power of integrating machine learning techniques with topology-based descriptors for molecular docking and virtual screening, providing a robust and accurate framework for predicting protein-protein binding affinities.", "authors": ["Xingjian Xu", "Jiahui Chen", "Chunmei Wang"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2024-12-24", "url": "https://arxiv.org/abs/2412.18541", "pdf_url": "https://arxiv.org/pdf/2412.18541v1", "arxiv_id": "2412.18541", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "c4d94797fa0a5b6b28fef0c0fb8d493aa6b204656cc77a209eeccf7206683d0b", "sources": ["arxiv", "semantic_scholar"], "title": "Overview of the First Workshop on Language Models for Low-Resource Languages (LoResLM 2025)", "abstract": "The first Workshop on Language Models for Low-Resource Languages (LoResLM 2025) was held in conjunction with the 31st International Conference on Computational Linguistics (COLING 2025) in Abu Dhabi, United Arab Emirates. This workshop mainly aimed to provide a forum for researchers to share and discuss their ongoing work on language models (LMs) focusing on low-resource languages, following the recent advancements in neural language models and their linguistic biases towards high-resource languages. LoResLM 2025 attracted notable interest from the natural language processing (NLP) community, resulting in 35 accepted papers from 52 submissions. These contributions cover a broad range of low-resource languages from eight language families and 13 diverse research areas, paving the way for future possibilities and promoting linguistic inclusivity in NLP.", "authors": ["Hansi Hettiarachchi", "Tharindu Ranasinghe", "Paul Rayson", "Ruslan Mitkov", "Mohamed Gaber", "Damith Premasiri", "Fiona Anting Tan", "Lasitha Uyangodage"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-20", "url": "https://arxiv.org/abs/2412.16365", "pdf_url": "https://arxiv.org/pdf/2412.16365v1", "arxiv_id": "2412.16365", "doi": "10.48550/arXiv.2412.16365", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "a79ff12487857f34176a6fb403715a3a9fe0c79020386096755e884bf0603029", "sources": ["arxiv", "semantic_scholar"], "title": "Open-Source Protein Language Models for Function Prediction and Protein Design", "abstract": "Protein language models (PLMs) have shown promise in improving the understanding of protein sequences, contributing to advances in areas such as function prediction and protein engineering. However, training these models from scratch requires significant computational resources, limiting their accessibility. To address this, we integrate a PLM into DeepChem, an open-source framework for computational biology and chemistry, to provide a more accessible platform for protein-related tasks. We evaluate the performance of the integrated model on various protein prediction tasks, showing that it achieves reasonable results across benchmarks. Additionally, we present an exploration of generating plastic-degrading enzyme candidates using the model's embeddings and latent space manipulation techniques. While the results suggest that further refinement is needed, this approach provides a foundation for future work in enzyme design. This study aims to facilitate the use of PLMs in research fields like synthetic biology and environmental sustainability, even for those with limited computational resources.", "authors": ["Shivasankaran Vanaja Pandi", "Bharath Ramsundar"], "categories": ["cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-12-18", "url": "https://arxiv.org/abs/2412.13519", "pdf_url": "https://arxiv.org/pdf/2412.13519v1", "arxiv_id": "2412.13519", "doi": "10.48550/arXiv.2412.13519", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "da02afb1cc6d2433d6371294eb83bf18719d96fae4a8023ba9519bbcc656f541", "sources": ["arxiv", "semantic_scholar"], "title": "EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations", "abstract": "Current Large Language Models (LLMs) for understanding proteins primarily treats amino acid sequences as a text modality. Meanwhile, Protein Language Models (PLMs), such as ESM-2, have learned massive sequential evolutionary knowledge from the universe of natural protein sequences. Furthermore, structure-based encoders like ProteinMPNN learn the structural information of proteins through Graph Neural Networks. However, whether the incorporation of protein encoders can enhance the protein understanding of LLMs has not been explored. To bridge this gap, we propose EvoLlama, a multimodal framework that connects a structure-based encoder, a sequence-based protein encoder and an LLM for protein understanding. EvoLlama consists of a ProteinMPNN structure encoder, an ESM-2 protein sequence encoder, a multimodal projector to align protein and text representations and a Llama-3 text decoder. To train EvoLlama, we fine-tune it on protein-oriented instructions and protein property prediction datasets verbalized via natural language instruction templates. Our experiments show that EvoLlama's protein understanding capabilities have been significantly enhanced, outperforming other fine-tuned protein-oriented LLMs in zero-shot settings by an average of 1%-8% and surpassing the state-of-the-art baseline with supervised fine-tuning by an average of 6%. On protein property prediction datasets, our approach achieves promising results that are competitive with state-of-the-art task-specific baselines. We will release our code in a future version.", "authors": ["Nuowei Liu", "Changzhi Sun", "Tao Ji", "Junfeng Tian", "Jianxin Tang", "Yuanbin Wu", "Man Lan"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-16", "url": "https://arxiv.org/abs/2412.11618", "pdf_url": "https://arxiv.org/pdf/2412.11618v1", "arxiv_id": "2412.11618", "doi": "10.48550/arXiv.2412.11618", "citation_count": 11, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2698}
{"id": "f74aec5afa642777ae4a8f1990d91a7704a2af0e6ff4de75fb3bc2c659816a2b", "sources": ["arxiv", "semantic_scholar"], "title": "Applications of Knot Theory for the Improvement of the AlphaFold Protein Database", "abstract": "AlphaFold, a groundbreaking protein prediction model, has revolutionized protein structure prediction, populating the AlphaFold Protein Database (AFDB) with millions of predicted structures. However, AlphaFold's accuracy in predicting proteins with intricate topologies, such as knots, remains a concern. This study investigates AlphaFold's performance in predicting knotted proteins and explores potential solutions to enhance the AFDB's reliability. Forty-five experimentally verified knotted protein structures from the KnotProt database were compared to their AlphaFold-generated counterparts. Knot analysis was performed using PyKnot, a PyMOL plugin, employing both Gauss codes and Alexander-Briggs knot notations. Results showed 95.6% accuracy in predicting the general shape of knots using Alexander-Briggs notation. However, Gauss code analysis revealed a 55.6% discrepancy, indicating AlphaFold's limitations in accurately representing the intricate orientation and directionality of knots. This Applications of Knot Theory for the improvement of the AlphaFold Protein Database suggests potential inaccuracies in a significant portion of the AFDB's knotted protein structures. The study underscores the need for improved knot representation in AlphaFold and proposes potential solutions, including transitioning to a single-module design or removing incorrectly predicted structures from the AFDB. These findings highlight the importance of continuous refinement for AI-based protein structure prediction tools to ensure the accuracy and reliability of protein databases for research and drug development.", "authors": ["Pranshu Jahagirdar"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2024-12-15", "url": "https://arxiv.org/abs/2412.11229", "pdf_url": "https://arxiv.org/pdf/2412.11229v1", "arxiv_id": "2412.11229", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "2a1a4aa76ffb4c30d9775eb40a07fa694f1b4c0d9a72ee7e102b719dba6a5bce", "sources": ["arxiv", "semantic_scholar"], "title": "FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction", "abstract": "Powerful generative AI models of protein-ligand structure have recently been proposed, but few of these methods support both flexible protein-ligand docking and affinity estimation. Of those that do, none can directly model multiple binding ligands concurrently or have been rigorously benchmarked on pharmacologically relevant drug targets, hindering their widespread adoption in drug discovery efforts. In this work, we propose FlowDock, the first deep geometric generative model based on conditional flow matching that learns to directly map unbound (apo) structures to their bound (holo) counterparts for an arbitrary number of binding ligands. Furthermore, FlowDock provides predicted structural confidence scores and binding affinity values with each of its generated protein-ligand complex structures, enabling fast virtual screening of new (multi-ligand) drug targets. For the well-known PoseBusters Benchmark dataset, FlowDock outperforms single-sequence AlphaFold 3 with a 51% blind docking success rate using unbound (apo) protein input structures and without any information derived from multiple sequence alignments, and for the challenging new DockGen-E dataset, FlowDock outperforms single-sequence AlphaFold 3 and matches single-sequence Chai-1 for binding pocket generalization. Additionally, in the ligand category of the 16th community-wide Critical Assessment of Techniques for Structure Prediction (CASP16), FlowDock ranked among the top-5 methods for pharmacological binding affinity estimation across 140 protein-ligand complexes, demonstrating the efficacy of its learned representations in virtual screening. Source code, data, and pre-trained models are available at https://github.com/BioinfoMachineLearning/FlowDock.", "authors": ["Alex Morehead", "Jianlin Cheng"], "categories": ["cs.LG", "cs.AI", "q-bio.BM", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology", "Medicine"], "published_date": "2024-12-14", "url": "https://arxiv.org/abs/2412.10966", "pdf_url": "https://arxiv.org/pdf/2412.10966v3", "arxiv_id": "2412.10966", "doi": "10.48550/arXiv.2412.10966", "citation_count": 13, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/BioinfoMachineLearning/FlowDock", "venue": "arXiv.org", "quality_score": 0.2865}
{"id": "9dbae3e1f9681722150f7c7954db91ffc70d27bc3d93c4c3267c0c6313af3675", "sources": ["arxiv", "semantic_scholar"], "title": "KULTURE Bench: A Benchmark for Assessing Language Model in Korean Cultural Context", "abstract": "Large language models have exhibited significant enhancements in performance across various tasks. However, the complexity of their evaluation increases as these models generate more fluent and coherent content. Current multilingual benchmarks often use translated English versions, which may incorporate Western cultural biases that do not accurately assess other languages and cultures. To address this research gap, we introduce KULTURE Bench, an evaluation framework specifically designed for Korean culture that features datasets of cultural news, idioms, and poetry. It is designed to assess language models' cultural comprehension and reasoning capabilities at the word, sentence, and paragraph levels. Using the KULTURE Bench, we assessed the capabilities of models trained with different language corpora and analyzed the results comprehensively. The results show that there is still significant room for improvement in the models' understanding of texts related to the deeper aspects of Korean culture.", "authors": ["Xiaonan Wang", "Jinyoung Yeo", "Joon-Ho Lim", "Hansaem Kim"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-10", "url": "https://arxiv.org/abs/2412.07251", "pdf_url": "https://arxiv.org/pdf/2412.07251v1", "arxiv_id": "2412.07251", "doi": "10.48550/arXiv.2412.07251", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Pacific Asia Conference on Language, Information and Computation", "quality_score": 0.2113}
{"id": "92a148adcd16723d97a819dd593838620df3c4010f08c136b6389a5c0a83a9ef", "sources": ["arxiv", "semantic_scholar"], "title": "Leveraging Multi-modal Representations to Predict Protein Melting Temperatures", "abstract": "Accurately predicting protein melting temperature changes (Delta Tm) is fundamental for assessing protein stability and guiding protein engineering. Leveraging multi-modal protein representations has shown great promise in capturing the complex relationships among protein sequences, structures, and functions. In this study, we develop models based on powerful protein language models, including ESM-2, ESM-3 and AlphaFold, using various feature extraction methods to enhance prediction accuracy. By utilizing the ESM-3 model, we achieve a new state-of-the-art performance on the s571 test dataset, obtaining a Pearson correlation coefficient (PCC) of 0.50. Furthermore, we conduct a fair evaluation to compare the performance of different protein language models in the Delta Tm prediction task. Our results demonstrate that integrating multi-modal protein representations could advance the prediction of protein melting temperatures.", "authors": ["Daiheng Zhang", "Yan Zeng", "Xinyu Hong", "Jinbo Xu"], "categories": ["cs.LG", "cs.CE"], "fields_of_study": ["Computer Science"], "published_date": "2024-12-05", "url": "https://arxiv.org/abs/2412.04526", "pdf_url": "https://arxiv.org/pdf/2412.04526v3", "arxiv_id": "2412.04526", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "5416b975d46237d4d056c15cb231a8063181b6565ab14588563889f7a68f67e1", "sources": ["arxiv", "semantic_scholar"], "title": "SeqProFT: Sequence-only Protein Property Prediction with LoRA Finetuning", "abstract": "Protein language models (PLMs) have demonstrated remarkable capabilities in learning relationships between protein sequences and functions. However, finetuning these large models requires substantial computational resources, often with suboptimal task-specific results. This study investigates how parameter-efficient finetuning via LoRA can enhance protein property prediction while significantly reducing computational demands. By applying LoRA to ESM-2 and ESM-C models of varying sizes and evaluating 10 diverse protein property prediction tasks, we demonstrate that smaller models with LoRA adaptation can match or exceed the performance of larger models without adaptation. Additionally, we integrate contact map information through a multi-head attention mechanism, improving model comprehension of structural features. Our systematic analysis reveals that LoRA finetuning enables faster convergence, better performance, and more efficient resource utilization, providing practical guidance for protein research applications in resource-constrained environments. The code is available at https://github.com/jiankliu/SeqProFT.", "authors": ["Shuo Zhang", "Jian K. Liu"], "categories": ["cs.LG", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-11-18", "url": "https://arxiv.org/abs/2411.11530", "pdf_url": "https://arxiv.org/pdf/2411.11530v2", "arxiv_id": "2411.11530", "doi": "10.1109/TAI.2025.3636109", "citation_count": 4, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/jiankliu/SeqProFT", "venue": "IEEE Transactions on Artificial Intelligence", "quality_score": 0.1747}
{"id": "90e93b88184849bb479076fe77405271bc9d370b6d38a871ba20aea98fea2d0a", "sources": ["arxiv", "semantic_scholar"], "title": "SCOP: A Sequence-Structure Contrast-Aware Framework for Protein Function Prediction", "abstract": "Improving the ability to predict protein function can potentially facilitate research in the fields of drug discovery and precision medicine. Technically, the properties of proteins are directly or indirectly reflected in their sequence and structure information, especially as the protein function is largely determined by its spatial properties. Existing approaches mostly focus on protein sequences or topological structures, while rarely exploiting the spatial properties and ignoring the relevance between sequence and structure information. Moreover, obtaining annotated data to improve protein function prediction is often time-consuming and costly. To this end, this work proposes a novel contrast-aware pre-training framework, called SCOP, for protein function prediction. We first design a simple yet effective encoder to integrate the protein topological and spatial features under the structure view. Then a convolutional neural network is utilized to learn the protein features under the sequence view. Finally, we pretrain SCOP by leveraging two types of auxiliary supervision to explore the relevance between these two views and thus extract informative representations to better predict protein function. Experimental results on four benchmark datasets and one self-built dataset demonstrate that SCOP provides more specific results, while using less pre-training data.", "authors": ["Runze Ma", "Chengxin He", "Huiru Zheng", "Xinye Wang", "Haiying Wang", "Yidan Zhang", "Lei Duan"], "categories": ["q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-11-18", "url": "https://arxiv.org/abs/2411.11366", "pdf_url": "https://arxiv.org/pdf/2411.11366v1", "arxiv_id": "2411.11366", "doi": "10.1109/BIBM62325.2024.10822541", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Bioinformatics and Biomedicine", "quality_score": 0.0753}
{"id": "31ba7fddaa9c71b0e881eab08751a1f7bcf20153612fe38fa60433e5d6982699", "sources": ["arxiv", "semantic_scholar"], "title": "Efficient Alignment of Large Language Models via Data Sampling", "abstract": "LLM alignment ensures that large language models behave safely and effectively by aligning their outputs with human values, goals, and intentions. Aligning LLMs employ huge amounts of data, computation, and time. Moreover, curating data with human feedback is expensive and takes time. Recent research depicts the benefit of data engineering in the fine-tuning and pre-training paradigms to bring down such costs. However, alignment differs from the afore-mentioned paradigms and it is unclear if data efficient alignment is feasible. In this work, we first aim to understand how the performance of LLM alignment scales with data. We find out that LLM alignment performance follows an exponential plateau pattern which tapers off post a rapid initial increase. Based on this, we identify data subsampling as a viable method to reduce resources required for alignment. Further, we propose an information theory-based methodology for efficient alignment by identifying a small high quality subset thereby reducing the computation and time required by alignment. We evaluate the proposed methodology over multiple datasets and compare the results. We find that the model aligned using our proposed methodology outperforms other sampling methods and performs comparable to the model aligned with the full dataset while using less than 10% data, leading to greater than 90% savings in costs, resources, and faster LLM alignment.", "authors": ["Amrit Khera", "Rajat Ghosh", "Debojyoti Dutta"], "categories": ["cs.LG", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-15", "url": "https://arxiv.org/abs/2411.10545", "pdf_url": "https://arxiv.org/pdf/2411.10545v2", "arxiv_id": "2411.10545", "doi": "10.48550/arXiv.2411.10545", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "a6196dfd3b26c44d9b71ee21cc182fc4b60eb62bc8d3fe9bee843592c2d369e2", "sources": ["arxiv", "semantic_scholar"], "title": "InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders", "abstract": "Protein language models (PLMs) have demonstrated remarkable success in protein modeling and design, yet their internal mechanisms for predicting structure and function remain poorly understood. Here we present a systematic approach to extract and analyze interpretable features from PLMs using sparse autoencoders (SAEs). By training SAEs on embeddings from the PLM ESM-2, we identify up to 2,548 human-interpretable latent features per layer that strongly correlate with up to 143 known biological concepts such as binding sites, structural motifs, and functional domains. In contrast, examining individual neurons in ESM-2 reveals up to 46 neurons per layer with clear conceptual alignment across 15 known concepts, suggesting that PLMs represent most concepts in superposition. Beyond capturing known annotations, we show that ESM-2 learns coherent concepts that do not map onto existing annotations and propose a pipeline using language models to automatically interpret novel latent features learned by the SAEs. As practical applications, we demonstrate how these latent features can fill in missing annotations in protein databases and enable targeted steering of protein sequence generation. Our results demonstrate that PLMs encode rich, interpretable representations of protein biology and we propose a systematic framework to extract and analyze these latent features. In the process, we recover both known biology and potentially new protein motifs. As community resources, we introduce InterPLM (interPLM.ai), an interactive visualization platform for exploring and analyzing learned PLM features, and release code for training and analysis at github.com/ElanaPearl/interPLM.", "authors": ["Elana Simon", "James Zou"], "categories": ["q-bio.BM", "cs.AI", "cs.LG", "q-bio.QM"], "fields_of_study": ["Computer Science", "Medicine", "Biology"], "published_date": "2024-11-13", "url": "https://arxiv.org/abs/2412.12101", "pdf_url": "https://arxiv.org/pdf/2412.12101v1", "arxiv_id": "2412.12101", "doi": "10.1038/s41592-025-02836-7", "citation_count": 107, "influential_citation_count": 5, "has_code": true, "code_url": null, "venue": "bioRxiv", "quality_score": 0.5084}
{"id": "1ee48b8b8781ea84f3200b1c2580b536d520d46ebdfaa70f7227894b7ea747a4", "sources": ["arxiv", "semantic_scholar"], "title": "Concept Bottleneck Language Models For protein design", "abstract": "We introduce Concept Bottleneck Protein Language Models (CB-pLM), a generative masked language model with a layer where each neuron corresponds to an interpretable concept. Our architecture offers three key benefits: i) Control: We can intervene on concept values to precisely control the properties of generated proteins, achieving a 3 times larger change in desired concept values compared to baselines. ii) Interpretability: A linear mapping between concept values and predicted tokens allows transparent analysis of the model's decision-making process. iii) Debugging: This transparency facilitates easy debugging of trained models. Our models achieve pre-training perplexity and downstream task performance comparable to traditional masked protein language models, demonstrating that interpretability does not compromise performance. While adaptable to any language model, we focus on masked protein language models due to their importance in drug discovery and the ability to validate our model's capabilities through real-world experiments and expert knowledge. We scale our CB-pLM from 24 million to 3 billion parameters, making them the largest Concept Bottleneck Models trained and the first capable of generative language modeling.", "authors": ["Aya Abdelsalam Ismail", "Tuomas Oikarinen", "Amy Wang", "Julius Adebayo", "Samuel Stanton", "Taylor Joren", "Joseph Kleinhenz", "Allen Goodman", "Héctor Corrada Bravo", "Kyunghyun Cho", "Nathan C. Frey"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-11-09", "url": "https://arxiv.org/abs/2411.06090", "pdf_url": "https://arxiv.org/pdf/2411.06090v2", "arxiv_id": "2411.06090", "doi": "10.48550/arXiv.2411.06090", "citation_count": 24, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.3495}
{"id": "f19ddee6ca9dfecf59de4a9effff5d147bb3504ee4505fe0c22090f03718c574", "sources": ["arxiv", "semantic_scholar"], "title": "Energy Efficient Protein Language Models: Leveraging Small Language Models with LoRA for Controllable Protein Generation", "abstract": "Large language models (LLMs) have demonstrated significant success in natural language processing (NLP) tasks and have shown promising results in other domains such as protein sequence generation. However, there remain salient differences between LLMs used for NLP, which effectively handle multiple tasks and are available in small sizes, and protein language models that are often specialized for specific tasks and only exist in larger sizes. In this work, we introduce two small protein language models, based on Llama-3-8B and Phi-3-mini, that are capable of both uncontrollable and controllable protein generation. For the uncontrollable generation task, our best model achieves an average pLDDT score of 69.75, demonstrating robust performance in generating viable protein structures. For the controllable generation task, in which the model generates proteins according to properties specified in the prompt, we achieve a remarkable average TM-Score of 0.84, indicating high structural similarity to target proteins. We chose 10 properties, including six classes of enzymes, to extend the capabilities of prior protein language models. Our approach utilizes the Low-Rank Adaptor (LoRA) technique, reducing trainable parameters to just 4% of the original model size, lowering computational requirements. By using a subset of the UniRef50 dataset and small models, we reduced the overall training time by 70% without compromising performance. Notably, Phi-3-mini reduced trainable parameters by 60%, decreasing training cost by 30% compared to Llama 3. Consequently, Phi-3 achieved a comparable TM-Score of 0.81, demonstrating that smaller models can match the performance of larger ones, like Llama 3. We also demonstrate the deployment of our models on the energy efficient ET-SoC-1 chip, significantly improving the TPS/W by a factor of 3.", "authors": ["Aayush Shah", "Shankar Jayaratnam"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2024-11-08", "url": "https://arxiv.org/abs/2411.05966", "pdf_url": "https://arxiv.org/pdf/2411.05966v1", "arxiv_id": "2411.05966", "doi": "10.48550/arXiv.2411.05966", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "f6a78c51e25b618cdf6668130ab0e6d6b75c1435d961ff36577b0b4dd3cc826a", "sources": ["arxiv", "semantic_scholar"], "title": "Training Compute-Optimal Protein Language Models", "abstract": "We explore optimally training protein language models, an area of significant interest in biological research where guidance on best practices is limited. Most models are trained with extensive compute resources until performance gains plateau, focusing primarily on increasing model sizes rather than optimizing the efficient compute frontier that balances performance and compute budgets. Our investigation is grounded in a massive dataset consisting of 939 million protein sequences. We trained over 300 models ranging from 3.5 million to 10.7 billion parameters on 5 to 200 billion unique tokens, to investigate the relations between model sizes, training token numbers, and objectives. First, we observed the effect of diminishing returns for the Causal Language Model (CLM) and that of overfitting for the Masked Language Model~(MLM) when repeating the commonly used Uniref database. To address this, we included metagenomic protein sequences in the training set to increase the diversity and avoid the plateau or overfitting effects. Second, we obtained the scaling laws of CLM and MLM on Transformer, tailored to the specific characteristics of protein sequence data. Third, we observe a transfer scaling phenomenon from CLM to MLM, further demonstrating the effectiveness of transfer through scaling behaviors based on estimated Effectively Transferred Tokens. Finally, to validate our scaling laws, we compare the large-scale versions of ESM-2 and PROGEN2 on downstream tasks, encompassing evaluations of protein generation as well as structure- and function-related tasks, all within less or equivalent pre-training compute budgets.", "authors": ["Xingyi Cheng", "Bo Chen", "Pan Li", "Jing Gong", "Jie Tang", "Le Song"], "categories": ["cs.LG", "cs.AI", "q-bio.QM"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2024-11-04", "url": "https://arxiv.org/abs/2411.02142", "pdf_url": "https://arxiv.org/pdf/2411.02142v1", "arxiv_id": "2411.02142", "doi": "10.1101/2024.06.06.597716", "citation_count": 38, "influential_citation_count": 4, "has_code": true, "code_url": "https://github.com/cxysteven/ScalingProteinLM", "venue": "bioRxiv", "quality_score": 0.3978}
{"id": "bca3ef1d13537ca06fb99bfd4e47b715e2654c640fbdfbf472912ace0cb5657f", "sources": ["arxiv", "semantic_scholar"], "title": "Beyond Current Boundaries: Integrating Deep Learning and AlphaFold for Enhanced Protein Structure Prediction from Low-Resolution Cryo-EM Maps", "abstract": "Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and graph neural networks (GNNs), have spurred the development of sophisticated map-to-model tools like DeepTracer and ModelAngelo, their efficacy notably diminishes with low-resolution maps beyond 4 Å. To address this shortfall, our research introduces DeepTracer-LowResEnhance, an innovative framework that synergizes a deep learning-enhanced map refinement technique with the power of AlphaFold. This methodology is designed to markedly improve the construction of models from low-resolution cryo-EM maps. DeepTracer-LowResEnhance was rigorously tested on a set of 37 protein cryo-EM maps, with resolutions ranging between 2.5 to 8.4 Å, including 22 maps with resolutions lower than 4 Å. The outcomes were compelling, demonstrating that 95.5\\% of the low-resolution maps exhibited a significant uptick in the count of total predicted residues. This denotes a pronounced improvement in atomic model building for low-resolution maps. Additionally, a comparative analysis alongside Phenix's auto-sharpening functionality delineates DeepTracer-LowResEnhance's superior capability in rendering more detailed and precise atomic models, thereby pushing the boundaries of current computational structural biology methodologies.", "authors": [" Xin", " Ma", "Dong Si"], "categories": ["q-bio.QM", "cs.LG"], "fields_of_study": ["Medicine", "Computer Science", "Biology"], "published_date": "2024-10-30", "url": "https://arxiv.org/abs/2410.23321", "pdf_url": "https://arxiv.org/pdf/2410.23321v1", "arxiv_id": "2410.23321", "doi": "10.48550/arXiv.2410.23321", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "4b418cb0afd769cfc3539cd6b4851f7765e704a22428474154cc522e8dcdf0bc", "sources": ["arxiv", "semantic_scholar"], "title": "MutaPLM: Protein Language Modeling for Mutation Explanation and Engineering", "abstract": "Studying protein mutations within amino acid sequences holds tremendous significance in life sciences. Protein language models (PLMs) have demonstrated strong capabilities in broad biological applications. However, due to architectural design and lack of supervision, PLMs model mutations implicitly with evolutionary plausibility, which is not satisfactory to serve as explainable and engineerable tools in real-world studies. To address these issues, we present MutaPLM, a unified framework for interpreting and navigating protein mutations with protein language models. MutaPLM introduces a protein delta network that captures explicit protein mutation representations within a unified feature space, and a transfer learning pipeline with a chain-of-thought (CoT) strategy to harvest protein mutation knowledge from biomedical texts. We also construct MutaDescribe, the first large-scale protein mutation dataset with rich textual annotations, which provides cross-modal supervision signals. Through comprehensive experiments, we demonstrate that MutaPLM excels at providing human-understandable explanations for mutational effects and prioritizing novel mutations with desirable properties. Our code, model, and data are open-sourced at https://github.com/PharMolix/MutaPLM.", "authors": ["Yizhen Luo", "Zikun Nie", "Massimo Hong", "Suyuan Zhao", "Hao Zhou", "Zaiqing Nie"], "categories": ["cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-10-30", "url": "https://arxiv.org/abs/2410.22949", "pdf_url": "https://arxiv.org/pdf/2410.22949v1", "arxiv_id": "2410.22949", "doi": "10.48550/arXiv.2410.22949", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/PharMolix/MutaPLM", "venue": "Neural Information Processing Systems", "quality_score": 0.1193}
{"id": "4d1937dbb43c35b72917e22fa5d43494a278092c9a0a438f595b1af870353460", "sources": ["arxiv", "semantic_scholar"], "title": "Long-context Protein Language Modeling Using Bidirectional Mamba with Shared Projection Layers", "abstract": "Self-supervised training of language models (LMs) has seen great success for protein sequences in learning meaningful representations and for generative drug design. Most protein LMs are based on the Transformer architecture trained on individual proteins with short context lengths. Such protein LMs cannot extrapolate to longer proteins and protein complexes well. They also fail to account for the underlying biological mechanisms carried out by biomolecular interactions and dynamics i.e., proteins often interact with other proteins, molecules, and pathways in complex biological systems. In this work, we propose LC-PLM based on an alternative protein LM architecture, BiMamba-S, built upon selective structured state-space models, to learn high-quality universal protein representations at the amino acid token level using masked language modeling. We also introduce its graph-contextual variant, LC-PLM, which contextualizes protein-protein interaction (PPI) graphs for a second stage of training. LC-PLM demonstrates favorable neural scaling laws, better length extrapolation capability, and up to 30% and 16% improvements on protein downstream tasks compared to Transformer-based ESM-2 when trained with 100B and 1T tokens, respectively. LC-PLM-G further trained within the context of PPI graphs shows promising results on protein structure and function prediction tasks. Our study demonstrates the benefit of increasing the context size with computationally efficient LM architecture (e.g., structured state space models) in learning universal protein representations and incorporating molecular interaction contexts contained in biological graphs.", "authors": ["Yingheng Wang", "Zichen Wang", "Gil Sadeh", "Luca Zancato", "Alessandro Achille", "George Karypis", "Huzefa Rangwala"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2024-10-29", "url": "https://arxiv.org/abs/2411.08909", "pdf_url": "https://arxiv.org/pdf/2411.08909v3", "arxiv_id": "2411.08909", "doi": "10.1101/2024.10.29.620988", "citation_count": 4, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/amazon-science/LC-PLM", "venue": "bioRxiv", "quality_score": 0.1747}
{"id": "1bb1d3b322321c1932517be23b35eb81171f1d7f412fcdcf84958dfaec705f3b", "sources": ["arxiv", "semantic_scholar"], "title": "Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model", "abstract": "Enzyme engineering enables the modification of wild-type proteins to meet industrial and research demands by enhancing catalytic activity, stability, binding affinities, and other properties. The emergence of deep learning methods for protein modeling has demonstrated superior results at lower costs compared to traditional approaches such as directed evolution and rational design. In mutation effect prediction, the key to pre-training deep learning models lies in accurately interpreting the complex relationships among protein sequence, structure, and function. This study introduces a retrieval-enhanced protein language model for comprehensive analysis of native properties from sequence and local structural interactions, as well as evolutionary properties from retrieved homologous sequences. The state-of-the-art performance of the proposed ProtREM is validated on over 2 million mutants across 217 assays from an open benchmark (ProteinGym). We also conducted post-hoc analyses of the model's ability to improve the stability and binding affinity of a VHH antibody. Additionally, we designed 10 new mutants on a DNA polymerase and conducted wet-lab experiments to evaluate their enhanced activity at higher temperatures. Both in silico and experimental evaluations confirmed that our method provides reliable predictions of mutation effects, offering an auxiliary tool for biologists aiming to evolve existing enzymes. The implementation is publicly available at https://github.com/tyang816/ProtREM.", "authors": ["Yang Tan", "Ruilin Wang", "Banghao Wu", "Liang Hong", "Bingxin Zhou"], "categories": ["cs.CL", "cs.AI", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-10-28", "url": "https://arxiv.org/abs/2410.21127", "pdf_url": "https://arxiv.org/pdf/2410.21127v1", "arxiv_id": "2410.21127", "doi": "10.48550/arXiv.2410.21127", "citation_count": 21, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/tyang816/ProtREM", "venue": "arXiv.org", "quality_score": 0.3356}
{"id": "9c3e96e327d50b157e6bd8930a71fc5bc468bb8311d27b0f21f6a802d928ce03", "sources": ["arxiv", "semantic_scholar"], "title": "A Survey of Large Language Models for Arabic Language and its Dialects", "abstract": "This survey offers a comprehensive overview of Large Language Models (LLMs) designed for Arabic language and its dialects. It covers key architectures, including encoder-only, decoder-only, and encoder-decoder models, along with the datasets used for pre-training, spanning Classical Arabic, Modern Standard Arabic, and Dialectal Arabic. The study also explores monolingual, bilingual, and multilingual LLMs, analyzing their architectures and performance across downstream tasks, such as sentiment analysis, named entity recognition, and question answering. Furthermore, it assesses the openness of Arabic LLMs based on factors, such as source code availability, training data, model weights, and documentation. The survey highlights the need for more diverse dialectal datasets and attributes the importance of openness for research reproducibility and transparency. It concludes by identifying key challenges and opportunities for future research and stressing the need for more inclusive and representative models.", "authors": ["Malak Mashaabi", "Shahad Al-Khalifa", "Hend Al-Khalifa"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-26", "url": "https://arxiv.org/abs/2410.20238", "pdf_url": "https://arxiv.org/pdf/2410.20238v2", "arxiv_id": "2410.20238", "doi": "10.1145/3807946", "citation_count": 23, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "ACM Transactions on Asian and Low-Resource Language Information Processing", "quality_score": 0.3451}
{"id": "1ca54675e685546beaf239d1e8e851f5ee61dc5ac0b2824e6f944432ab22a28b", "sources": ["arxiv", "semantic_scholar"], "title": "Structure Language Models for Protein Conformation Generation", "abstract": "Proteins adopt multiple structural conformations to perform their diverse biological functions, and understanding these conformations is crucial for advancing drug discovery. Traditional physics-based simulation methods often struggle with sampling equilibrium conformations and are computationally expensive. Recently, deep generative models have shown promise in generating protein conformations as a more efficient alternative. However, these methods predominantly rely on the diffusion process within a 3D geometric space, which typically centers around the vicinity of metastable states and is often inefficient in terms of runtime. In this paper, we introduce Structure Language Modeling (SLM) as a novel framework for efficient protein conformation generation. Specifically, the protein structures are first encoded into a compact latent space using a discrete variational auto-encoder, followed by conditional language modeling that effectively captures sequence-specific conformation distributions. This enables a more efficient and interpretable exploration of diverse ensemble modes compared to existing methods. Based on this general framework, we instantiate SLM with various popular LM architectures as well as proposing the ESMDiff, a novel BERT-like structure language model fine-tuned from ESM3 with masked diffusion. We verify our approach in various scenarios, including the equilibrium dynamics of BPTI, conformational change pairs, and intrinsically disordered proteins. SLM provides a highly efficient solution, offering a 20-100x speedup than existing methods in generating diverse conformations, shedding light on promising avenues for future research.", "authors": ["Jiarui Lu", "Xiaoyin Chen", "Stephen Zhewen Lu", "Chence Shi", "Hongyu Guo", "Yoshua Bengio", "Jian Tang"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-10-24", "url": "https://arxiv.org/abs/2410.18403", "pdf_url": "https://arxiv.org/pdf/2410.18403v2", "arxiv_id": "2410.18403", "doi": "10.48550/arXiv.2410.18403", "citation_count": 30, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.3728}
{"id": "cf534dfc404fc56bfb66af22137ded7c996248edfba10408a32eb3f94f313d99", "sources": ["arxiv", "semantic_scholar"], "title": "CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin Evaluation", "abstract": "Protein structures are important for understanding their functions and interactions. Currently, many protein structure prediction methods are enriching the structure database. Discriminating the origin of structures is crucial for distinguishing between experimentally resolved and computationally predicted structures, evaluating the reliability of prediction methods, and guiding downstream biological studies. Building on works in structure prediction, We developed a structure-sensitive supervised deep learning model, Crystal vs Predicted Evaluator for Protein Structure (CPE-Pro), to represent and discriminate the origin of protein structures. CPE-Pro learns the structural information of proteins and captures inter-structural differences to achieve accurate traceability on four data classes, and is expected to be extended to more. Simultaneously, we utilized Foldseek to encode protein structures into \"structure-sequences\" and trained a protein Structural Sequence Language Model, SSLM. Preliminary experiments demonstrated that, compared to large-scale protein language models pre-trained on vast amounts of amino acid sequences, the \"structure-sequence\" enables the language model to learn more informative protein features, enhancing and optimizing structural representations. We have provided the code, model weights, and all related materials on https://github.com/GouWenrui/CPE-Pro-main.git.", "authors": ["Wenrui Gou", "Wenhui Ge", "Yang Tan", "Mingchen Li", "Guisheng Fan", "Huiqun Yu"], "categories": ["q-bio.BM", "cs.CL", "cs.LG", "q-bio.QM"], "fields_of_study": ["Biology", "Computer Science", "Medicine"], "published_date": "2024-10-21", "url": "https://arxiv.org/abs/2410.15592", "pdf_url": "https://arxiv.org/pdf/2410.15592v2", "arxiv_id": "2410.15592", "doi": "10.1007/s12539-025-00732-4", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/GouWenrui/CPE-Pro-main.git", "venue": "Interdisciplinary Sciences Computational Life Sciences", "quality_score": 0.1193}
{"id": "97f43958cb9ba379473dc0a3277c4478f82e334a039107dafb8a4a31b470edfa", "sources": ["arxiv", "semantic_scholar"], "title": "Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction", "abstract": "In recent years, advances in artificial intelligence (AI) have transformed structural biology, particularly protein structure prediction. Though AI-based methods, such as AlphaFold (AF), often predict single conformations of proteins with high accuracy and confidence, predictions of alternative folds are often inaccurate, low-confidence, or simply not predicted at all. Here, we review three blind spots that alternative conformations reveal about AF-based protein structure prediction. First, proteins that assume conformations distinct from their training-set homologs can be mispredicted. Second, AF overrelies on its training set to predict alternative conformations. Third, degeneracies in pairwise representations can lead to high-confidence predictions inconsistent with experiment. These weaknesses suggest approaches to predict alternative folds more reliably.", "authors": ["Devlina Chakravarty", "Myeongsang Lee", "Lauren L. Porter"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology", "Medicine"], "published_date": "2024-10-18", "url": "https://arxiv.org/abs/2410.14898", "pdf_url": "https://arxiv.org/pdf/2410.14898v1", "arxiv_id": "2410.14898", "doi": null, "citation_count": 25, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3537}
{"id": "109fd66ea0e6363514757dd867feb8869a40c4bf05ac6133b32f20b139acae0a", "sources": ["arxiv", "semantic_scholar"], "title": "DPLM-2: A Multimodal Diffusion Protein Language Model", "abstract": "Proteins are essential macromolecules defined by their amino acid sequences, which determine their three-dimensional structures and, consequently, their functions in all living organisms. Therefore, generative protein modeling necessitates a multimodal approach to simultaneously model, understand, and generate both sequences and structures. However, existing methods typically use separate models for each modality, limiting their ability to capture the intricate relationships between sequence and structure. This results in suboptimal performance in tasks that requires joint understanding and generation of both modalities. In this paper, we introduce DPLM-2, a multimodal protein foundation model that extends discrete diffusion protein language model (DPLM) to accommodate both sequences and structures. To enable structural learning with the language model, 3D coordinates are converted to discrete tokens using a lookup-free quantization-based tokenizer. By training on both experimental and high-quality synthetic structures, DPLM-2 learns the joint distribution of sequence and structure, as well as their marginals and conditionals. We also implement an efficient warm-up strategy to exploit the connection between large-scale evolutionary data and structural inductive biases from pre-trained sequence-based protein language models. Empirical evaluation shows that DPLM-2 can simultaneously generate highly compatible amino acid sequences and their corresponding 3D structures eliminating the need for a two-stage generation approach. Moreover, DPLM-2 demonstrates competitive performance in various conditional generation tasks, including folding, inverse folding, and scaffolding with multimodal motif inputs, as well as providing structure-aware representations for predictive tasks.", "authors": ["Xinyou Wang", "Zaixiang Zheng", "Fei Ye", "Dongyu Xue", "Shujian Huang", "Quanquan Gu"], "categories": ["cs.LG", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-10-17", "url": "https://arxiv.org/abs/2410.13782", "pdf_url": "https://arxiv.org/pdf/2410.13782v1", "arxiv_id": "2410.13782", "doi": "10.48550/arXiv.2410.13782", "citation_count": 72, "influential_citation_count": 8, "has_code": false, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.4771}
{"id": "b7e06740c37266946cf41d752dfee1a8a17b8a311ad1674013be22389d475f8b", "sources": ["arxiv", "semantic_scholar"], "title": "Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning", "abstract": "Graph representation learning, involving both node features and graph structures, is crucial for real-world applications but often encounters pervasive noise. State-of-the-art methods typically address noise by focusing separately on node features with large language models (LLMs) and on graph structures with graph structure learning models (GSLMs). In this paper, we introduce LangGSL, a robust framework that integrates the complementary strengths of pre-trained language models and GSLMs to jointly enhance both node feature and graph structure learning. In LangGSL, we first leverage LLMs to filter noise in the raw data and extract valuable cleaned information as features, enhancing the synergy of downstream models. During the mutual learning phase in LangGSL, the core idea is to leverage the relatively small language model (LM) to process local attributes and generate reliable pseudo-labels and informative node embeddings, which are then integrated into the GSLM's prediction phase. This approach enriches the global context and enhances overall performance. Meanwhile, GSLM refines the evolving graph structure constructed from the LM's output, offering updated labels back to the LM as additional guidance, thus facilitating a more effective mutual learning process. The LM and GSLM work synergistically, complementing each other's strengths and offsetting weaknesses within a variational information-maximizing framework, resulting in enhanced node features and a more robust graph structure. Extensive experiments on diverse graph datasets of varying scales and across different task scenarios demonstrate the scalability and effectiveness of the proposed approach.", "authors": ["Guangxin Su", "Yifan Zhu", "Wenjie Zhang", "Hanchen Wang", "Ying Zhang"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-15", "url": "https://arxiv.org/abs/2410.12096", "pdf_url": "https://arxiv.org/pdf/2410.12096v1", "arxiv_id": "2410.12096", "doi": "10.48550/arXiv.2410.12096", "citation_count": 3, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "371cf8ac654a825410b42abbfe095a6b8b1ae4b3b590ba384882cca6ff404f1a", "sources": ["arxiv", "semantic_scholar"], "title": "Model-based Large Language Model Customization as Service", "abstract": "Prominent Large Language Model (LLM) services from providers like OpenAI and Google excel at general tasks but often underperform on domain-specific applications. Current customization services for these LLMs typically require users to upload data for fine-tuning, posing significant privacy risks. While differentially private (DP) data synthesis presents a potential alternative, its application commonly results in low effectiveness due to the introduction of excessive noise on data for DP. To overcome this, we introduce Llamdex, a novel framework that facilitates LLM customization as a service, where the client uploads pre-trained domain-specific models rather than data. This client-uploaded model, optionally protected by DP with much lower noise, is inserted into the base LLM via connection modules. Significantly, these connecting modules are trained without requiring sensitive domain data, enabling clients to customize LLM services while preserving data privacy. Experiments demonstrate that Llamdex improves domain-specific accuracy by up to 26% over state-of-the-art private data synthesis methods under identical privacy constraints and, by obviating the need for users to provide domain context within queries, maintains inference efficiency comparable to the original LLM service.", "authors": ["Zhaomin Wu", "Jizhou Guo", "Junyi Hou", "Bingsheng He", "Lixin Fan", "Qiang Yang"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-14", "url": "https://arxiv.org/abs/2410.10481", "pdf_url": "https://arxiv.org/pdf/2410.10481v5", "arxiv_id": "2410.10481", "doi": "10.18653/v1/2025.emnlp-main.248", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.1193}
{"id": "6467666eaa8ea3ab6346a96ba43c19a6ff5c79cd9515cd7d41a049297e0183b8", "sources": ["arxiv", "semantic_scholar"], "title": "From N-grams to Pre-trained Multilingual Models For Language Identification", "abstract": "In this paper, we investigate the use of N-gram models and Large Pre-trained Multilingual models for Language Identification (LID) across 11 South African languages. For N-gram models, this study shows that effective data size selection remains crucial for establishing effective frequency distributions of the target languages, that efficiently model each language, thus, improving language ranking. For pre-trained multilingual models, we conduct extensive experiments covering a diverse set of massively pre-trained multilingual (PLM) models -- mBERT, RemBERT, XLM-r, and Afri-centric multilingual models -- AfriBERTa, Afro-XLMr, AfroLM, and Serengeti. We further compare these models with available large-scale Language Identification tools: Compact Language Detector v3 (CLD V3), AfroLID, GlotLID, and OpenLID to highlight the importance of focused-based LID. From these, we show that Serengeti is a superior model across models: N-grams to Transformers on average. Moreover, we propose a lightweight BERT-based LID model (za_BERT_lid) trained with NHCLT + Vukzenzele corpus, which performs on par with our best-performing Afri-centric models.", "authors": ["Thapelo Sindane", "Vukosi Marivate"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-11", "url": "https://arxiv.org/abs/2410.08728", "pdf_url": "https://arxiv.org/pdf/2410.08728v1", "arxiv_id": "2410.08728", "doi": "10.48550/arXiv.2410.08728", "citation_count": 5, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1945}
{"id": "d6200f974bff103076f9972960728197bbf44cdf6d5796bf7027abcd8b8d0570", "sources": ["arxiv", "semantic_scholar"], "title": "Metalic: Meta-Learning In-Context with Protein Language Models", "abstract": "Predicting the biophysical and functional properties of proteins is essential for in silico protein design. Machine learning has emerged as a promising technique for such prediction tasks. However, the relative scarcity of in vitro annotations means that these models often have little, or no, specific data on the desired fitness prediction task. As a result of limited data, protein language models (PLMs) are typically trained on general protein sequence modeling tasks, and then fine-tuned, or applied zero-shot, to protein fitness prediction. When no task data is available, the models make strong assumptions about the correlation between the protein sequence likelihood and fitness scores. In contrast, we propose meta-learning over a distribution of standard fitness prediction tasks, and demonstrate positive transfer to unseen fitness prediction tasks. Our method, called Metalic (Meta-Learning In-Context), uses in-context learning and fine-tuning, when data is available, to adapt to new tasks. Crucially, fine-tuning enables considerable generalization, even though it is not accounted for during meta-training. Our fine-tuned models achieve strong results with 18 times fewer parameters than state-of-the-art models. Moreover, our method sets a new state-of-the-art in low-data settings on ProteinGym, an established fitness-prediction benchmark. Due to data scarcity, we believe meta-learning will play a pivotal role in advancing protein engineering.", "authors": ["Jacob Beck", "Shikha Surana", "Manus McAuliffe", "Oliver Bent", "Thomas D. Barrett", "Juan Jose Garau Luis", "Paul Duckworth"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-10", "url": "https://arxiv.org/abs/2410.08355", "pdf_url": "https://arxiv.org/pdf/2410.08355v3", "arxiv_id": "2410.08355", "doi": "10.48550/arXiv.2410.08355", "citation_count": 4, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/instadeepai/metalic", "venue": "International Conference on Learning Representations", "quality_score": 0.1747}
{"id": "4bb659bee2033032c6c6bfa8f48c3349746dc8d24dd274b38a3c47ac10903f6c", "sources": ["arxiv", "semantic_scholar"], "title": "Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding with LLMs", "abstract": "Proteins, as essential biomolecules, play a central role in biological processes, including metabolic reactions and DNA replication. Accurate prediction of their properties and functions is crucial in biological applications. Recent development of protein language models (pLMs) with supervised fine tuning provides a promising solution to this problem. However, the fine-tuned model is tailored for particular downstream prediction task, and achieving general-purpose protein understanding remains a challenge. In this paper, we introduce Structure-Enhanced Protein Instruction Tuning (SEPIT) framework to bridge this gap. Our approach incorporates a novel structure-aware module into pLMs to enrich their structural knowledge, and subsequently integrates these enhanced pLMs with large language models (LLMs) to advance protein understanding. In this framework, we propose a novel instruction tuning pipeline. First, we warm up the enhanced pLMs using contrastive learning and structure denoising. Then, caption-based instructions are used to establish a basic understanding of proteins. Finally, we refine this understanding by employing a mixture of experts (MoEs) to capture more complex properties and functional information with the same number of activated parameters. Moreover, we construct the largest and most comprehensive protein instruction dataset to date, which allows us to train and evaluate the general-purpose protein understanding model. Extensive experiments on both open-ended generation and closed-set answer tasks demonstrate the superior performance of SEPIT over both closed-source general LLMs and open-source LLMs trained with protein knowledge.", "authors": ["Wei Wu", "Chao Wang", "Liyi Chen", "Mingze Yin", "Yiheng Zhu", "Kun Fu", "Jieping Ye", "Hui Xiong", "Zheng Wang"], "categories": ["cs.CL", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-10-04", "url": "https://arxiv.org/abs/2410.03553", "pdf_url": "https://arxiv.org/pdf/2410.03553v3", "arxiv_id": "2410.03553", "doi": "10.1145/3711896.3737138", "citation_count": 6, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "Knowledge Discovery and Data Mining", "quality_score": 0.2113}
{"id": "cba57da086e8c65f4a0251179f68c1cc0d6fe39cc7c3a106d40768137b06ce60", "sources": ["arxiv", "semantic_scholar"], "title": "Function-Guided Conditional Generation Using Protein Language Models with Adapters", "abstract": "The conditional generation of proteins with desired functions is a key goal for generative models. Existing methods based on prompting of protein language models (PLMs) can generate proteins conditioned on a target functionality, such as a desired enzyme family. However, these methods are limited to simple, tokenized conditioning and have not been shown to generalize to unseen functions. In this study, we propose ProCALM (Protein Conditionally Adapted Language Model), an approach for the conditional generation of proteins using adapters to PLMs. While previous methods have used adapters for structure-conditioned generation from PLMs, our implementation of ProCALM involves finetuning ProGen2 to condition generation based on versatile representations of protein function-e.g. enzyme family, taxonomy, or natural language descriptions. ProCALM matches or exceeds the performance of existing methods at conditional sequence generation from target functions. Impressively, it can also generalize to rare and unseen functions. Overall, ProCALM is a flexible and computationally efficient approach, and we expect that it can be extended to a wide range of generative language models.", "authors": ["Jason Yang", "Aadyot Bhatnagar", "Jeffrey A. Ruffolo", "Ali Madani"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2024-10-04", "url": "https://arxiv.org/abs/2410.03634", "pdf_url": "https://arxiv.org/pdf/2410.03634v2", "arxiv_id": "2410.03634", "doi": null, "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.25}
{"id": "e895f6c0ed280a291504ffa368015646f7fa71278ddabeab8ddbf804ff229e54", "sources": ["arxiv", "semantic_scholar"], "title": "Morphological evaluation of subwords vocabulary used by BETO language model", "abstract": "Subword tokenization algorithms used by Large Language Models are significantly more efficient and can independently build the necessary vocabulary of words and subwords without human intervention. However, those subwords do not always align with real morphemes, potentially impacting the models' performance, though it remains uncertain when this might occur. In previous research, we proposed a method to assess the morphological quality of vocabularies, focusing on the overlap between these vocabularies and the morphemes of a given language. Our evaluation method was built on three quality measures, relevance, cohesion, and morphological accuracy, and a procedure for their assessment. By applying this method to vocabularies created by three subword tokenization algorithms, BPE, Wordpiece, and Unigram, we concluded that these vocabularies generally exhibit very low morphological quality. In this article, we apply this evaluation to the tokenizer of BETO, a BERT language model trained on large Spanish corpora. This evaluation, along with our previous results, helped us conclude that its vocabulary has a low morphological quality, and we also found that training the tokenizer in a larger corpus does not improve the morphological quality of the generated vocabulary. Additionally, this evaluation helps clarify the algorithm used by the tokenizer, that is, Wordpiece, given the inconsistencies between the authors' claims and the model's configuration.", "authors": ["Óscar García-Sierra", "Ana Fernández-Pampillón Cesteros", "Miguel Ortega-Martín"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-10-03", "url": "https://arxiv.org/abs/2410.02283", "pdf_url": "https://arxiv.org/pdf/2410.02283v1", "arxiv_id": "2410.02283", "doi": "10.48550/arXiv.2410.02283", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "6653216f5243e6956c8eba87dca5a675122c6bf85aa465d5904d969d2bb1426e", "sources": ["arxiv", "semantic_scholar"], "title": "Recent advances in interpretable machine learning using structure-based protein representations", "abstract": "Recent advancements in machine learning (ML) are transforming the field of structural biology. For example, AlphaFold, a groundbreaking neural network for protein structure prediction, has been widely adopted by researchers. The availability of easy-to-use interfaces and interpretable outcomes from the neural network architecture, such as the confidence scores used to color the predicted structures, have made AlphaFold accessible even to non-ML experts. In this paper, we present various methods for representing protein 3D structures from low- to high-resolution, and show how interpretable ML methods can support tasks such as predicting protein structures, protein function, and protein-protein interactions. This survey also emphasizes the significance of interpreting and visualizing ML-based inference for structure-based protein representations that enhance interpretability and knowledge discovery. Developing such interpretable approaches promises to further accelerate fields including drug development and protein design.", "authors": ["Luiz Felipe Vecchietti", "Minji Lee", "Begench Hangeldiyev", "Hyunkyu Jung", "Hahnbeom Park", "Tae-Kyun Kim", "Meeyoung Cha", "Ho Min Kim"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-26", "url": "https://arxiv.org/abs/2409.17726", "pdf_url": "https://arxiv.org/pdf/2409.17726v1", "arxiv_id": "2409.17726", "doi": "10.48550/arXiv.2409.17726", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "822045c4adce0b15601f705784e562244288128e3d3397ae9d316bef326b9a66", "sources": ["arxiv", "semantic_scholar"], "title": "Behavioral Bias of Vision-Language Models: A Behavioral Finance View", "abstract": "Large Vision-Language Models (LVLMs) evolve rapidly as Large Language Models (LLMs) was equipped with vision modules to create more human-like models. However, we should carefully evaluate their applications in different domains, as they may possess undesired biases. Our work studies the potential behavioral biases of LVLMs from a behavioral finance perspective, an interdisciplinary subject that jointly considers finance and psychology. We propose an end-to-end framework, from data collection to new evaluation metrics, to assess LVLMs' reasoning capabilities and the dynamic behaviors manifested in two established human financial behavioral biases: recency bias and authority bias. Our evaluations find that recent open-source LVLMs such as LLaVA-NeXT, MobileVLM-V2, Mini-Gemini, MiniCPM-Llama3-V 2.5 and Phi-3-vision-128k suffer significantly from these two biases, while the proprietary model GPT-4o is negligibly impacted. Our observations highlight directions in which open-source models can improve. The code is available at https://github.com/mydcxiao/vlm_behavioral_fin.", "authors": ["Yuhang Xiao", "Yudi Lin", "Ming-Chang Chiu"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-23", "url": "https://arxiv.org/abs/2409.15256", "pdf_url": "https://arxiv.org/pdf/2409.15256v1", "arxiv_id": "2409.15256", "doi": "10.48550/arXiv.2409.15256", "citation_count": 3, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/mydcxiao/vlm_behavioral_fin", "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "46bb5830a9e81b4015dc6e808d5e63abf6fa7f98268b979053136b6c4938df4e", "sources": ["arxiv", "semantic_scholar"], "title": "Protein-Mamba: Biological Mamba Models for Protein Function Prediction", "abstract": "Protein function prediction is a pivotal task in drug discovery, significantly impacting the development of effective and safe therapeutics. Traditional machine learning models often struggle with the complexity and variability inherent in predicting protein functions, necessitating more sophisticated approaches. In this work, we introduce Protein-Mamba, a novel two-stage model that leverages both self-supervised learning and fine-tuning to improve protein function prediction. The pre-training stage allows the model to capture general chemical structures and relationships from large, unlabeled datasets, while the fine-tuning stage refines these insights using specific labeled datasets, resulting in superior prediction performance. Our extensive experiments demonstrate that Protein-Mamba achieves competitive performance, compared with a couple of state-of-the-art methods across a range of protein function datasets. This model's ability to effectively utilize both unlabeled and labeled data highlights the potential of self-supervised learning in advancing protein function prediction and offers a promising direction for future research in drug discovery.", "authors": ["Bohao Xu", "Yingzhou Lu", "Yoshitaka Inoue", "Namkyeong Lee", "Tianfan Fu", "Jintai Chen"], "categories": ["cs.LG", "q-bio.BM", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-09-22", "url": "https://arxiv.org/abs/2409.14617", "pdf_url": "https://arxiv.org/pdf/2409.14617v1", "arxiv_id": "2409.14617", "doi": "10.48550/arXiv.2409.14617", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "3d2f98480d25274dd22e67d0520faca7df349312af9e7a5f1d1b3e0b2e9e36ae", "sources": ["arxiv", "semantic_scholar"], "title": "Natural Language Processing Methods for the Study of Protein-Ligand Interactions", "abstract": "Recent advances in Natural Language Processing (NLP) have ignited interest in developing effective methods for predicting protein-ligand interactions (PLIs) given their relevance to drug discovery and protein engineering efforts and the ever-growing volume of biochemical sequence and structural data available. The parallels between human languages and the \"languages\" used to represent proteins and ligands have enabled the use of NLP machine learning approaches to advance PLI studies. In this review, we explain where and how such approaches have been applied in the recent literature and discuss useful mechanisms such as long short-term memory, transformers, and attention. We conclude with a discussion of the current limitations of NLP methods for the study of PLIs as well as key challenges that need to be addressed in future work.", "authors": ["James Michels", "Ramya Bandarupalli", "Amin Ahangar Akbari", "Thai Le", "Hong Xiao", "Jing Li", "Erik F. Y. Hom"], "categories": ["q-bio.QM", "cs.CL"], "fields_of_study": ["Biology", "Computer Science", "Medicine"], "published_date": "2024-09-19", "url": "https://arxiv.org/abs/2409.13057", "pdf_url": "https://arxiv.org/pdf/2409.13057v2", "arxiv_id": "2409.13057", "doi": "10.48550/arXiv.2409.13057", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "53d85d931d50a96a4bd3812b2f0fbe7715b89b477235fea0485d655e5994f70c", "sources": ["arxiv", "semantic_scholar"], "title": "THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models", "abstract": "Hallucination, the generation of factually incorrect content, is a growing challenge in Large Language Models (LLMs). Existing detection and mitigation methods are often isolated and insufficient for domain-specific needs, lacking a standardized pipeline. This paper introduces THaMES (Tool for Hallucination Mitigations and EvaluationS), an integrated framework and library addressing this gap. THaMES offers an end-to-end solution for evaluating and mitigating hallucinations in LLMs, featuring automated test set generation, multifaceted benchmarking, and adaptable mitigation strategies. It automates test set creation from any corpus, ensuring high data quality, diversity, and cost-efficiency through techniques like batch processing, weighted sampling, and counterfactual validation. THaMES assesses a model's ability to detect and reduce hallucinations across various tasks, including text generation and binary classification, applying optimal mitigation strategies like In-Context Learning (ICL), Retrieval Augmented Generation (RAG), and Parameter-Efficient Fine-tuning (PEFT). Evaluations of state-of-the-art LLMs using a knowledge base of academic papers, political news, and Wikipedia reveal that commercial models like GPT-4o benefit more from RAG than ICL, while open-weight models like Llama-3.1-8B-Instruct and Mistral-Nemo gain more from ICL. Additionally, PEFT significantly enhances the performance of Llama-3.1-8B-Instruct in both evaluation tasks.", "authors": ["Mengfei Liang", "Archish Arun", "Zekun Wu", "Cristian Munoz", "Jonathan Lutch", "Emre Kazim", "Adriano Koshiyama", "Philip Treleaven"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-17", "url": "https://arxiv.org/abs/2409.11353", "pdf_url": "https://arxiv.org/pdf/2409.11353v3", "arxiv_id": "2409.11353", "doi": "10.48550/arXiv.2409.11353", "citation_count": 8, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "2e4e3ecd950a0b6aa0f08a9f06a0b7f9c3110fb36cb78010805e944f99c756c7", "sources": ["arxiv", "semantic_scholar"], "title": "Unforgettable Generalization in Language Models", "abstract": "When language models (LMs) are trained to forget (or \"unlearn'') a skill, how precisely does their behavior change? We study the behavior of transformer LMs in which tasks have been forgotten via fine-tuning on randomized labels. Such LMs learn to generate near-random predictions for individual examples in the \"training'' set used for forgetting. Across tasks, however, LMs exhibit extreme variability in whether LM predictions change on examples outside the training set. In some tasks (like entailment classification), forgetting generalizes robustly, and causes models to produce uninformative predictions on new task instances; in other tasks (like physical commonsense reasoning and scientific question answering) forgetting affects only the training examples, and models continue to perform the \"forgotten'' task accurately even for examples very similar to those that appeared in the training set. Dataset difficulty is not predictive of whether a behavior can be forgotten; instead, generalization in forgetting is (weakly) predicted by the confidence of LMs' initial task predictions and the variability of LM representations of training data, with low confidence and low variability both associated with greater generalization. Perhaps most surprisingly, random-label forgetting appears to be somewhat insensitive to the contents of the training set: for example, models trained on science questions with random labels continue to answer other science questions accurately, but begin to produce random labels on entailment classification tasks. Finally, we show that even generalizable forgetting is shallow: linear probes trained on LMs' representations can still perform tasks reliably after forgetting. Our results highlight the difficulty and unpredictability of performing targeted skill removal from models via fine-tuning.", "authors": ["Eric Zhang", "Leshem Chosen", "Jacob Andreas"], "categories": ["cs.LG", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-09-03", "url": "https://arxiv.org/abs/2409.02228", "pdf_url": "https://arxiv.org/pdf/2409.02228v1", "arxiv_id": "2409.02228", "doi": "10.48550/arXiv.2409.02228", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "ebfc22335033f3e4ddfe7992f30a7defe5901bcc82e19a507fcdb5ba92dff938", "sources": ["arxiv", "semantic_scholar"], "title": "SpeechPrompt: Prompting Speech Language Models for Speech Processing Tasks", "abstract": "Prompting has become a practical method for utilizing pre-trained language models (LMs). This approach offers several advantages. It allows an LM to adapt to new tasks with minimal training and parameter updates, thus achieving efficiency in both storage and computation. Additionally, prompting modifies only the LM's inputs and harnesses the generative capabilities of language models to address various downstream tasks in a unified manner. This significantly reduces the need for human labor in designing task-specific models. These advantages become even more evident as the number of tasks served by the LM scales up. Motivated by the strengths of prompting, we are the first to explore the potential of prompting speech LMs in the domain of speech processing. Recently, there has been a growing interest in converting speech into discrete units for language modeling. Our pioneer research demonstrates that these quantized speech units are highly versatile within our unified prompting framework. Not only can they serve as class labels, but they also contain rich phonetic information that can be re-synthesized back into speech signals for speech generation tasks. Specifically, we reformulate speech processing tasks into speech-to-unit generation tasks. As a result, we can seamlessly integrate tasks such as speech classification, sequence generation, and speech generation within a single, unified prompting framework. The experiment results show that the prompting method can achieve competitive performance compared to the strong fine-tuning method based on self-supervised learning models with a similar number of trainable parameters. The prompting method also shows promising results in the few-shot setting. Moreover, with the advanced speech LMs coming into the stage, the proposed prompting framework attains great potential.", "authors": ["Kai-Wei Chang", "Haibin Wu", "Yu-Kai Wang", "Yuan-Kuei Wu", "Hua Shen", "Wei-Cheng Tseng", "Iu-thing Kang", "Shang-Wen Li", "Hung-yi Lee"], "categories": ["eess.AS", "cs.AI", "cs.CL", "cs.LG"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2024-08-23", "url": "https://arxiv.org/abs/2408.13040", "pdf_url": "https://arxiv.org/pdf/2408.13040v1", "arxiv_id": "2408.13040", "doi": "10.1109/TASLP.2024.3436618", "citation_count": 17, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE/ACM Transactions on Audio Speech and Language Processing", "quality_score": 0.3138}
{"id": "cf287b087371d8417a98f91edf87975972b5db6c9bf4b4e7e19d55f75fe2bf4c", "sources": ["arxiv", "semantic_scholar"], "title": "AlphaFolding: 4D Diffusion for Dynamic Protein Structure Prediction with Reference and Motion Guidance", "abstract": "Protein structure prediction is pivotal for understanding the structure-function relationship of proteins, advancing biological research, and facilitating pharmaceutical development and experimental design. While deep learning methods and the expanded availability of experimental 3D protein structures have accelerated structure prediction, the dynamic nature of protein structures has received limited attention. This study introduces an innovative 4D diffusion model incorporating molecular dynamics (MD) simulation data to learn dynamic protein structures. Our approach is distinguished by the following components: (1) a unified diffusion model capable of generating dynamic protein structures, including both the backbone and side chains, utilizing atomic grouping and side-chain dihedral angle predictions; (2) a reference network that enhances structural consistency by integrating the latent embeddings of the initial 3D protein structures; and (3) a motion alignment module aimed at improving temporal structural coherence across multiple time steps. To our knowledge, this is the first diffusion-based model aimed at predicting protein trajectories across multiple time steps simultaneously. Validation on benchmark datasets demonstrates that our model exhibits high accuracy in predicting dynamic 3D structures of proteins containing up to 256 amino acids over 32 time steps, effectively capturing both local flexibility in stable states and significant conformational changes. URL: https://fudan-generative-vision.github.io/AlphaFolding/#/", "authors": ["Kaihui Cheng", "Ce Liu", "Qingkun Su", "Jun Wang", "Liwei Zhang", "Yining Tang", "Yao Yao", "Siyu Zhu", "Yuan Qi"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-22", "url": "https://arxiv.org/abs/2408.12419", "pdf_url": "https://arxiv.org/pdf/2408.12419v3", "arxiv_id": "2408.12419", "doi": null, "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "a4c973dafd55796e28b7623e472283d1ee8ed9ff823d67c38df0beb16701660d", "sources": ["arxiv", "semantic_scholar"], "title": "CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Prediction", "abstract": "Accurately measuring protein-RNA binding affinity is crucial in many biological processes and drug design. Previous computational methods for protein-RNA binding affinity prediction rely on either sequence or structure features, unable to capture the binding mechanisms comprehensively. The recent emerging pre-trained language models trained on massive unsupervised sequences of protein and RNA have shown strong representation ability for various in-domain downstream tasks, including binding site prediction. However, applying different-domain language models collaboratively for complex-level tasks remains unexplored. In this paper, we propose CoPRA to bridge pre-trained language models from different biological domains via Complex structure for Protein-RNA binding Affinity prediction. We demonstrate for the first time that cross-biological modal language models can collaborate to improve binding affinity prediction. We propose a Co-Former to combine the cross-modal sequence and structure information and a bi-scope pre-training strategy for improving Co-Former's interaction understanding. Meanwhile, we build the largest protein-RNA binding affinity dataset PRA310 for performance evaluation. We also test our model on a public dataset for mutation effect prediction. CoPRA reaches state-of-the-art performance on all the datasets. We provide extensive analyses and verify that CoPRA can (1) accurately predict the protein-RNA binding affinity; (2) understand the binding affinity change caused by mutations; and (3) benefit from scaling data and model size.", "authors": ["Rong Han", "Xiaohong Liu", "Tong Pan", "Jing Xu", "Xiaoyu Wang", "Wuyang Lan", "Zhenyu Li", "Zixuan Wang", "Jiangning Song", "Guangyu Wang", "Ting Chen"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2024-08-21", "url": "https://arxiv.org/abs/2409.03773", "pdf_url": "https://arxiv.org/pdf/2409.03773v2", "arxiv_id": "2409.03773", "doi": "10.48550/arXiv.2409.03773", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.2386}
{"id": "ea2979422e2bdc93ee3dc87bcd50692b63747540fec3bdada2be465162838aed", "sources": ["arxiv", "semantic_scholar"], "title": "ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding", "abstract": "Understanding biological processes, drug development, and biotechnological advancements requires a detailed analysis of protein structures and functions, a task that is inherently complex and time-consuming in traditional protein research. To streamline this process, we introduce ProteinGPT, a state-of-the-art multimodal large language model for proteins that enables users to upload protein sequences and/or structures for comprehensive analysis and responsive inquiries. ProteinGPT integrates protein sequence and structure encoders with linear projection layers to ensure precise representation adaptation and leverages a large language model (LLM) to generate accurate, contextually relevant responses. To train ProteinGPT, we constructed a large-scale dataset of 132,092 proteins, each annotated with 20-30 property tags and 5-10 QA pairs per protein, and optimized the instruction-tuning process using GPT-4o. Experiments demonstrate that ProteinGPT effectively generates informative responses to protein-related questions, achieving high performance on both semantic and lexical metrics and significantly outperforming baseline models and general-purpose LLMs in understanding and responding to protein-related queries. Our code and data are available at https://github.com/ProteinGPT/ProteinGPT.", "authors": ["Yijia Xiao", "Edward Sun", "Yiqiao Jin", "Qifan Wang", "Wei Wang"], "categories": ["cs.AI", "cs.CE", "cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-08-21", "url": "https://arxiv.org/abs/2408.11363", "pdf_url": "https://arxiv.org/pdf/2408.11363v2", "arxiv_id": "2408.11363", "doi": "10.48550/arXiv.2408.11363", "citation_count": 43, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/ProteinGPT/ProteinGPT", "venue": "arXiv.org", "quality_score": 0.4109}
{"id": "29c7678109761d95ff7948a07cab9481d71e13940635c287517cc2d09a908843", "sources": ["arxiv", "semantic_scholar"], "title": "Design Proteins Using Large Language Models: Enhancements and Comparative Analyses", "abstract": "Pre-trained LLMs have demonstrated substantial capabilities across a range of conventional natural language processing (NLP) tasks, such as summarization and entity recognition. In this paper, we explore the application of LLMs in the generation of high-quality protein sequences. Specifically, we adopt a suite of pre-trained LLMs, including Mistral-7B1, Llama-2-7B2, Llama-3-8B3, and gemma-7B4, to produce valid protein sequences. All of these models are publicly available.5 Unlike previous work in this field, our approach utilizes a relatively small dataset comprising 42,000 distinct human protein sequences. We retrain these models to process protein-related data, ensuring the generation of biologically feasible protein structures. Our findings demonstrate that even with limited data, the adapted models exhibit efficiency comparable to established protein-focused models such as ProGen varieties, ProtGPT2, and ProLLaMA, which were trained on millions of protein sequences. To validate and quantify the performance of our models, we conduct comparative analyses employing standard metrics such as pLDDT, RMSD, TM-score, and REU. Furthermore, we commit to making the trained versions of all four models publicly available, fostering greater transparency and collaboration in the field of computational biology.", "authors": ["Kamyar Zeinalipour", "Neda Jamshidi", "Monica Bianchini", "Marco Maggini", "Marco Gori"], "categories": ["q-bio.QM", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-08-12", "url": "https://arxiv.org/abs/2408.06396", "pdf_url": "https://arxiv.org/pdf/2408.06396v1", "arxiv_id": "2408.06396", "doi": "10.18653/v1/2024.langmol-1.5", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "ba042b02e1a3d3ba3fe6eb0a4149c05d980b433e1adff6ec26b837721c84b24d", "sources": ["arxiv", "semantic_scholar"], "title": "Peptide Sequencing Via Protein Language Models", "abstract": "We introduce a protein language model for determining the complete sequence of a peptide based on measurement of a limited set of amino acids. To date, protein sequencing relies on mass spectrometry, with some novel edman degregation based platforms able to sequence non-native peptides. Current protein sequencing techniques face limitations in accurately identifying all amino acids, hindering comprehensive proteome analysis. Our method simulates partial sequencing data by selectively masking amino acids that are experimentally difficult to identify in protein sequences from the UniRef database. This targeted masking mimics real-world sequencing limitations. We then modify and finetune a ProtBert derived transformer-based model, for a new downstream task predicting these masked residues, providing an approximation of the complete sequence. Evaluating on three bacterial Escherichia species, we achieve per-amino-acid accuracy up to 90.5% when only four amino acids ([KCYM]) are known. Structural assessment using AlphaFold and TM-score validates the biological relevance of our predictions. The model also demonstrates potential for evolutionary analysis through cross-species performance. This integration of simulated experimental constraints with computational predictions offers a promising avenue for enhancing protein sequence analysis, potentially accelerating advancements in proteomics and structural biology by providing a probabilistic reconstruction of the complete protein sequence from limited experimental data.", "authors": ["Thuong Le Hoai Pham", "Jillur Rahman Saurav", "Aisosa A. Omere", "Calvin J. Heyl", "Mohammad Sadegh Nasr", "Cody Tyler Reynolds", "Jai Prakash Yadav Veerla", "Helen H Shang", "Justyn Jaworski", "Alison Ravenscraft", "Joseph Anthony Buonomo", "Jacob M. Luber"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-08-01", "url": "https://arxiv.org/abs/2408.00892", "pdf_url": "https://arxiv.org/pdf/2408.00892v1", "arxiv_id": "2408.00892", "doi": "10.1145/3698587.3701385", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "ACM International Conference on Bioinformatics, Computational Biology and Biomedicine", "quality_score": 0.1193}
{"id": "87429a29a88e563ad1de849fe67007d3fca8bc5ad613b4fcbe96060ea2508fc3", "sources": ["arxiv", "semantic_scholar"], "title": "Improving Text Embeddings for Smaller Language Models Using Contrastive Fine-tuning", "abstract": "While Large Language Models show remarkable performance in natural language understanding, their resource-intensive nature makes them less accessible. In contrast, smaller language models such as MiniCPM offer more sustainable scalability, but often underperform without specialized optimization. In this paper, we explore the enhancement of smaller language models through the improvement of their text embeddings. We select three language models, MiniCPM, Phi-2, and Gemma, to conduct contrastive fine-tuning on the NLI dataset. Our results demonstrate that this fine-tuning method enhances the quality of text embeddings for all three models across various benchmarks, with MiniCPM showing the most significant improvements of an average 56.33% performance gain. The contrastive fine-tuning code is publicly available at https://github.com/trapoom555/Language-Model-STS-CFT.", "authors": ["Trapoom Ukarapol", "Zhicheng Lee", "Amy Xin"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-08-01", "url": "https://arxiv.org/abs/2408.00690", "pdf_url": "https://arxiv.org/pdf/2408.00690v2", "arxiv_id": "2408.00690", "doi": "10.48550/arXiv.2408.00690", "citation_count": 3, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/trapoom555/Language-Model-STS-CFT", "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "eb3655e9400f731cf47d3228deb7238875ed1df66c571b3d98d06662301a2762", "sources": ["arxiv", "semantic_scholar"], "title": "Ranking protein-protein models with large language models and graph neural networks", "abstract": "Protein-protein interactions (PPIs) are associated with various diseases, including cancer, infections, and neurodegenerative disorders. Obtaining three-dimensional structural information on these PPIs serves as a foundation to interfere with those or to guide drug design. Various strategies can be followed to model those complexes, all typically resulting in a large number of models. A challenging step in this process is the identification of good models (near-native PPI conformations) from the large pool of generated models. To address this challenge, we previously developed DeepRank-GNN-esm, a graph-based deep learning algorithm for ranking modelled PPI structures harnessing the power of protein language models. Here, we detail the use of our software with examples. DeepRank-GNN-esm is freely available at https://github.com/haddocking/DeepRank-GNN-esm", "authors": ["Xiaotong Xu", "Alexandre M. J. J. Bonvin"], "categories": ["q-bio.BM", "cs.AI"], "fields_of_study": ["Medicine", "Biology", "Computer Science"], "published_date": "2024-07-23", "url": "https://arxiv.org/abs/2407.16375", "pdf_url": "https://arxiv.org/pdf/2407.16375v1", "arxiv_id": "2407.16375", "doi": "10.1007/978-1-0716-4623-6_4", "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/haddocking/DeepRank-GNN-esm", "venue": null, "quality_score": 0.0753}
{"id": "60129d51a27615b5c98c7791bc9e01f6d1ab52f5e41e96925438816939b2a7ee", "sources": ["arxiv", "semantic_scholar"], "title": "GraphPrint: Extracting Features from 3D Protein Structure for Drug Target Affinity Prediction", "abstract": "Accurate drug target affinity prediction can improve drug candidate selection, accelerate the drug discovery process, and reduce drug production costs. Previous work focused on traditional fingerprints or used features extracted based on the amino acid sequence in the protein, ignoring its 3D structure which affects its binding affinity. In this work, we propose GraphPrint: a framework for incorporating 3D protein structure features for drug target affinity prediction. We generate graph representations for protein 3D structures using amino acid residue location coordinates and combine them with drug graph representation and traditional features to jointly learn drug target affinity. Our model achieves a mean square error of 0.1378 and a concordance index of 0.8929 on the KIBA dataset and improves over using traditional protein features alone. Our ablation study shows that the 3D protein structure-based features provide information complementary to traditional features.", "authors": ["Amritpal Singh"], "categories": ["cs.LG", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-15", "url": "https://arxiv.org/abs/2407.10452", "pdf_url": "https://arxiv.org/pdf/2407.10452v1", "arxiv_id": "2407.10452", "doi": "10.48550/arXiv.2407.10452", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "c18c774bacf89e06c3b19a948b2b1a22d36b14e914b03ef51d944679475bed08", "sources": ["arxiv", "semantic_scholar"], "title": "Daisy: An integrated repeat protein curation service", "abstract": "Tandem repeats in proteins identification, classification and curation is a complex process that requires manual processing from experts, processing power and time. There are recent and relevant advances applying machine learning for protein structure prediction and repeat classification that are useful for this process. However, no service contemplates required databases and software to supplement researching on repeat proteins. In this publication we present Daisy, an integrated repeat protein curation web service. This service can process Protein Data Bank (PDB) and the AlphaFold Database entries for tandem repeats identification. In addition, it uses an algorithm to search a sequence against a library of Pfam hidden Markov model (HMM). Repeat classifications are associated with the identified families through RepeatsDB. This prediction is considered for enhancing the ReUPred algorithm execution and hastening the repeat units identification process. The service can also operate every associated PDB and AlphaFold structure with a UniProt proteome registry. Availability: The Daisy web service is freely accessible at daisy.bioinformatica.org.", "authors": ["Manuel Bezerra-Brandao", "Ronaldo Romario Tunque Cahui", "Layla Hirsh"], "categories": ["cs.SE", "cs.DB"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2024-07-10", "url": "https://arxiv.org/abs/2407.07817", "pdf_url": "https://arxiv.org/pdf/2407.07817v1", "arxiv_id": "2407.07817", "doi": "10.1016/j.jsb.2023.108033", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Structural Biology", "quality_score": 0.0}
{"id": "a3896f444ffa5a98ee968b2c8fedcee4d5532f0d25b2e834b405527c638a0b07", "sources": ["arxiv", "semantic_scholar"], "title": "Dihedral Angle Adherence: Evaluating Protein Structure Predictions in the Absence of Experimental Data", "abstract": "Determining the 3D structures of proteins is essential in understanding their behavior in the cellular environment. Computational methods of predicting protein structures have advanced, but assessing prediction accuracy remains a challenge. The traditional method, RMSD, relies on experimentally determined structures and lacks insight into improvement areas of predictions. We propose an alternative: analyzing dihedral angles, bypassing the need for the reference structure of an evaluated protein. Our method segments proteins into amino acid subsequences and searches for matches, comparing dihedral angles across numerous proteins to compute a metric using Mahalanobis distance. Evaluated on many predictions, our approach correlates with RMSD and identifies areas for prediction enhancement. This method offers a promising route for accurate protein structure prediction assessment and improvement.", "authors": ["Musa Azeem", "Homayoun Valafar"], "categories": ["q-bio.BM", "cs.CE"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2024-07-09", "url": "https://arxiv.org/abs/2407.18336", "pdf_url": "https://arxiv.org/pdf/2407.18336v1", "arxiv_id": "2407.18336", "doi": "10.48550/arXiv.2407.18336", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "44ed8d6c79bbbcdbe40d9f13279f5e0f1b8fa285b8a5848c2e3452f58087b80b", "sources": ["arxiv", "semantic_scholar"], "title": "Improving AlphaFlow for Efficient Protein Ensembles Generation", "abstract": "Investigating conformational landscapes of proteins is a crucial way to understand their biological functions and properties. AlphaFlow stands out as a sequence-conditioned generative model that introduces flexibility into structure prediction models by fine-tuning AlphaFold under the flow-matching framework. Despite the advantages of efficient sampling afforded by flow-matching, AlphaFlow still requires multiple runs of AlphaFold to finally generate one single conformation. Due to the heavy consumption of AlphaFold, its applicability is limited in sampling larger set of protein ensembles or the longer chains within a constrained timeframe. In this work, we propose a feature-conditioned generative model called AlphaFlow-Lit to realize efficient protein ensembles generation. In contrast to the full fine-tuning on the entire structure, we focus solely on the light-weight structure module to reconstruct the conformation. AlphaFlow-Lit performs on-par with AlphaFlow and surpasses its distilled version without pretraining, all while achieving a significant sampling acceleration of around 47 times. The advancement in efficiency showcases the potential of AlphaFlow-Lit in enabling faster and more scalable generation of protein ensembles.", "authors": ["Shaoning Li", "Mingyu Li", "Yusong Wang", "Xinheng He", "Nanning Zheng", "Jian Zhang", "Pheng-Ann Heng"], "categories": ["cs.LG", "cs.AI", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-07-08", "url": "https://arxiv.org/abs/2407.12053", "pdf_url": "https://arxiv.org/pdf/2407.12053v1", "arxiv_id": "2407.12053", "doi": "10.48550/arXiv.2407.12053", "citation_count": 12, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2785}
{"id": "d6c6830be78d7dbfbc3d99e11f933fedbd8bc9dfd894f95e89db388a079bd5c2", "sources": ["arxiv", "semantic_scholar"], "title": "Manipulating language models' training data to study syntactic constraint learning: the case of English passivization", "abstract": "Grammatical rules in natural languages are often characterized by exceptions. How do language learners learn these exceptions to otherwise general patterns? Here, we study this question through the case study of English passivization. While passivization is in general quite productive, there are cases where it cannot apply (cf. the following sentence is ungrammatical: *One hour was lasted by the meeting). Using neural network language models as theories of language acquisition, we explore the sources of indirect evidence that a learner can leverage to learn whether a verb can be passivized. We first characterize English speakers' judgments of exceptions to the passive, and confirm that speakers find some verbs more passivizable than others. We then show that a neural network language model's verb passivizability judgments are largely similar to those displayed by humans, suggesting that evidence for these exceptions is available in the linguistic input. Finally, we test two hypotheses as to the source of evidence that language models use to learn these restrictions: frequency (entrenchment) and semantics (affectedness). We do so by training models on versions of the corpus that have had sentences of the types implicated by each hypothesis removed, altered, or introduced. We find support for both hypotheses: entrenchment and affectedness make independent contributions to a verb's passivizability. From a methodological point of view, this study highlights the utility of altering a language model's training data for answering questions where complete control over a learner's input is vital.", "authors": ["Cara Su-Yi Leong", "Tal Linzen"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-05", "url": "https://arxiv.org/abs/2407.04593", "pdf_url": "https://arxiv.org/pdf/2407.04593v3", "arxiv_id": "2407.04593", "doi": null, "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2698}
{"id": "3e0bbbe4273350a1b1dff04066f7614248bc2760c86e92bc075e5ee8e0f6d3d9", "sources": ["arxiv", "semantic_scholar"], "title": "Open foundation models for Azerbaijani language", "abstract": "The emergence of multilingual large language models has enabled the development of language understanding and generation systems in Azerbaijani. However, most of the production-grade systems rely on cloud solutions, such as GPT-4. While there have been several attempts to develop open foundation models for Azerbaijani, these works have not found their way into common use due to a lack of systemic benchmarking. This paper encompasses several lines of work that promote open-source foundation models for Azerbaijani. We introduce (1) a large text corpus for Azerbaijani, (2) a family of encoder-only language models trained on this dataset, (3) labeled datasets for evaluating these models, and (4) extensive evaluation that covers all major open-source models with Azerbaijani support.", "authors": ["Jafar Isbarov", "Kavsar Huseynova", "Elvin Mammadov", "Mammad Hajili", "Duygu Ataman"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-02", "url": "https://arxiv.org/abs/2407.02337", "pdf_url": "https://arxiv.org/pdf/2407.02337v2", "arxiv_id": "2407.02337", "doi": "10.48550/arXiv.2407.02337", "citation_count": 4, "influential_citation_count": 1, "has_code": true, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "e04788c820c9992ab035a8b08bf2becb131cecd0c320ac264e225b1cc0c06f65", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring Advanced Large Language Models with LLMsuite", "abstract": "This tutorial explores the advancements and challenges in the development of Large Language Models (LLMs) such as ChatGPT and Gemini. It addresses inherent limitations like temporal knowledge cutoffs, mathematical inaccuracies, and the generation of incorrect information, proposing solutions like Retrieval Augmented Generation (RAG), Program-Aided Language Models (PAL), and frameworks such as ReAct and LangChain. The integration of these techniques enhances LLM performance and reliability, especially in multi-step reasoning and complex task execution. The paper also covers fine-tuning strategies, including instruction fine-tuning, parameter-efficient methods like LoRA, and Reinforcement Learning from Human Feedback (RLHF) as well as Reinforced Self-Training (ReST). Additionally, it provides a comprehensive survey of transformer architectures and training techniques for LLMs. The source code can be accessed by contacting the author via email for a request.", "authors": ["Giorgio Roffo"], "categories": ["cs.CL", "cs.CV"], "fields_of_study": ["Computer Science"], "published_date": "2024-07-01", "url": "https://arxiv.org/abs/2407.12036", "pdf_url": "https://arxiv.org/pdf/2407.12036v2", "arxiv_id": "2407.12036", "doi": "10.13140/RG.2.2.11774.80963", "citation_count": 6, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "36675636f0e114873de41e718122d605bfb98b4d3e1150af8df25b5a36ceb1b9", "sources": ["arxiv", "semantic_scholar"], "title": "DCI: An Accurate Quality Assessment Criteria for Protein Complex Structure Models", "abstract": "The structure of proteins is the basis for studying protein function and drug design. The emergence of AlphaFold 2 has greatly promoted the prediction of protein 3D structures, and it is of great significance to give an overall and accurate evaluation of the predicted models, especially the complex models. Among the existing methods for evaluating multimer structures, DockQ is the most commonly used. However, as a more suitable metric for complex docking, DockQ cannot provide a unique and accurate evaluation in the non-docking situation. Therefore, it is necessary to propose an evaluation strategy that can directly evaluate the whole complex without limitation and achieve good results. In this work, we proposed DCI score, a new evaluation strategy for protein complex structure models, which only bases on distance map and CI (contact-interface) map, DCI focuses on the prediction accuracy of the contact interface based on the overall evaluation of complex structure, is not inferior to DockQ in the evaluation accuracy according to CAPRI classification, and is able to handle the non-docking situation better than DockQ. Besides, we calculated DCI score on CASP datasets and compared it with CASP official assessment, which obtained good results. In addition, we found that DCI can better evaluate the overall structure deviation caused by interface prediction errors in the case of multi-chains. Our DCI is available at \\url{https://gitee.com/WendaWang/DCI-score.git}, and the online-server is available at \\url{http://mialab.ruc.edu.cn/DCIServer/}.", "authors": ["Wenda Wang", "Jiaqi Zhai", "He Huang", "Xinqi Gong"], "categories": ["q-bio.BM", "math.OC"], "fields_of_study": ["Biology", "Mathematics"], "published_date": "2024-06-30", "url": "https://arxiv.org/abs/2407.00560", "pdf_url": "https://arxiv.org/pdf/2407.00560v1", "arxiv_id": "2407.00560", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "e47404aa57488ae73b9a1bb36219e8421a0655ed46edc2c12085808125de508f", "sources": ["arxiv", "semantic_scholar"], "title": "ProtSolM: Protein Solubility Prediction with Multi-modal Features", "abstract": "Understanding protein solubility is essential for their functional applications. Computational methods for predicting protein solubility are crucial for reducing experimental costs and enhancing the efficiency and success rates of protein engineering. Existing methods either construct a supervised learning scheme on small-scale datasets with manually processed physicochemical properties, or blindly apply pre-trained protein language models to extract amino acid interaction information. The scale and quality of available training datasets leave significant room for improvement in terms of accuracy and generalization. To address these research gaps, we propose \\sol, a novel deep learning method that combines pre-training and fine-tuning schemes for protein solubility prediction. ProtSolM integrates information from multiple dimensions, including physicochemical properties, amino acid sequences, and protein backbone structures. Our model is trained using \\data, the largest solubility dataset that we have constructed. PDBSol includes over $60,000$ protein sequences and structures. We provide a comprehensive leaderboard of existing statistical learning and deep learning methods on independent datasets with computational and experimental labels. ProtSolM achieved state-of-the-art performance across various evaluation metrics, demonstrating its potential to significantly advance the accuracy of protein solubility prediction.", "authors": ["Yang Tan", "Jia Zheng", "Liang Hong", "Bingxin Zhou"], "categories": ["q-bio.QM"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2024-06-28", "url": "https://arxiv.org/abs/2406.19744", "pdf_url": "https://arxiv.org/pdf/2406.19744v1", "arxiv_id": "2406.19744", "doi": "10.1109/BIBM62325.2024.10822310", "citation_count": 19, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Bioinformatics and Biomedicine", "quality_score": 0.3253}
{"id": "355c1431b3f4837cf02a6d170d62ef13c87af018dc192db0d6e01c92fcecf22c", "sources": ["arxiv", "semantic_scholar"], "title": "Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Models", "abstract": "We describe the accurate prediction of ligand-protein interaction (LPI) affinities, also known as drug-target interactions (DTI), with instruction fine-tuned pretrained generative small language models (SLMs). We achieved accurate predictions for a range of affinity values associated with ligand-protein interactions on out-of-sample data in a zero-shot setting. Only the SMILES string of the ligand and the amino acid sequence of the protein were used as the model inputs. Our results demonstrate a clear improvement over machine learning (ML) and free-energy perturbation (FEP+) based methods in accurately predicting a range of ligand-protein interaction affinities, which can be leveraged to further accelerate drug discovery campaigns against challenging therapeutic targets.", "authors": ["Ben Fauber"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-27", "url": "https://arxiv.org/abs/2407.00111", "pdf_url": "https://arxiv.org/pdf/2407.00111v1", "arxiv_id": "2407.00111", "doi": "10.48550/arXiv.2407.00111", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "196bc02db178f8c3c7c0932f529b00bab8ad6aa19c4f49e269cff9d4632d372f", "sources": ["arxiv", "semantic_scholar"], "title": "Evaluating representation learning on the protein structure universe", "abstract": "We introduce ProteinWorkshop, a comprehensive benchmark suite for representation learning on protein structures with Geometric Graph Neural Networks. We consider large-scale pre-training and downstream tasks on both experimental and predicted structures to enable the systematic evaluation of the quality of the learned structural representation and their usefulness in capturing functional relationships for downstream tasks. We find that: (1) large-scale pretraining on AlphaFold structures and auxiliary tasks consistently improve the performance of both rotation-invariant and equivariant GNNs, and (2) more expressive equivariant GNNs benefit from pretraining to a greater extent compared to invariant models. We aim to establish a common ground for the machine learning and computational biology communities to rigorously compare and advance protein structure representation learning. Our open-source codebase reduces the barrier to entry for working with large protein structure datasets by providing: (1) storage-efficient dataloaders for large-scale structural databases including AlphaFoldDB and ESM Atlas, as well as (2) utilities for constructing new tasks from the entire PDB. ProteinWorkshop is available at: github.com/a-r-j/ProteinWorkshop.", "authors": ["Arian R. Jamasb", "Alex Morehead", "Chaitanya K. Joshi", "Zuobai Zhang", "Kieran Didi", "Simon V. Mathis", "Charles Harris", "Jian Tang", "Jianlin Cheng", "Pietro Lio", "Tom L. Blundell"], "categories": ["cs.LG", "q-bio.BM"], "fields_of_study": ["Medicine", "Computer Science", "Biology"], "published_date": "2024-06-19", "url": "https://arxiv.org/abs/2406.13864", "pdf_url": "https://arxiv.org/pdf/2406.13864v1", "arxiv_id": "2406.13864", "doi": "10.48550/arXiv.2406.13864", "citation_count": 27, "influential_citation_count": 4, "has_code": true, "code_url": null, "venue": "International Conference on Learning Representations", "quality_score": 0.3618}
{"id": "9f42597c089362eae68e2ebbfc6a07517e1edacf5224f5c59225bb32ee1ff845", "sources": ["arxiv", "semantic_scholar"], "title": "Evaluating Large Language Models along Dimensions of Language Variation: A Systematik Invesdigatiom uv Cross-lingual Generalization", "abstract": "While large language models exhibit certain cross-lingual generalization capabilities, they suffer from performance degradation (PD) on unseen closely-related languages (CRLs) and dialects relative to their high-resource language neighbour (HRLN). However, we currently lack a fundamental understanding of what kinds of linguistic distances contribute to PD, and to what extent. Furthermore, studies of cross-lingual generalization are confounded by unknown quantities of CRL language traces in the training data, and by the frequent lack of availability of evaluation data in lower-resource related languages and dialects. To address these issues, we model phonological, morphological, and lexical distance as Bayesian noise processes to synthesize artificial languages that are controllably distant from the HRLN. We analyse PD as a function of underlying noise parameters, offering insights on model robustness to isolated and composed linguistic phenomena, and the impact of task and HRL characteristics on PD. We calculate parameter posteriors on real CRL-HRLN pair data and show that they follow computed trends of artificial languages, demonstrating the viability of our noisers. Our framework offers a cheap solution for estimating task performance on an unseen CRL given HRLN performance using its posteriors, as well as for diagnosing observed PD on a CRL in terms of its linguistic distances from its HRLN, and opens doors to principled methods of mitigating performance degradation.", "authors": ["Niyati Bafna", "Kenton Murray", "David Yarowsky"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-06-19", "url": "https://arxiv.org/abs/2406.13718", "pdf_url": "https://arxiv.org/pdf/2406.13718v2", "arxiv_id": "2406.13718", "doi": "10.48550/arXiv.2406.13718", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.2386}
{"id": "bfb628fbc9ae9b3728b634fe909f2f625981e680c653a8e6948e514a3bcf4870", "sources": ["arxiv", "semantic_scholar"], "title": "FoldToken2: Learning compact, invariant and generative protein structure language", "abstract": "The equivalent nature of 3D coordinates has posed long term challenges in protein structure representation learning, alignment, and generation. Can we create a compact and invariant language that equivalently represents protein structures? Towards this goal, we propose FoldToken2 to transfer equivariant structures into discrete tokens, while maintaining the recoverability of the original structures. From FoldToken1 to FoldToken2, we improve three key components: (1) invariant structure encoder, (2) vector-quantized compressor, and (3) equivalent structure decoder. We evaluate FoldToken2 on the protein structure reconstruction task and show that it outperforms previous FoldToken1 by 20\\% in TMScore and 81\\% in RMSD. FoldToken2 probably be the first method that works well on both single-chain and multi-chain protein structures quantization. We believe that FoldToken2 will inspire further improvement in protein structure representation learning, structure alignment, and structure generation tasks.", "authors": ["Zhangyang Gao", "Cheng Tan", "Stan Z. Li"], "categories": ["q-bio.BM", "cs.AI", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2024-06-11", "url": "https://arxiv.org/abs/2407.00050", "pdf_url": "https://arxiv.org/pdf/2407.00050v1", "arxiv_id": "2407.00050", "doi": "10.1101/2024.06.11.598584", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "bioRxiv", "quality_score": 0.2113}
{"id": "9c17e11331649c12fe84d71b53786165dacd92a8914d0ea2c762f5f0d517af05", "sources": ["arxiv", "semantic_scholar"], "title": "A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding", "abstract": "The parallels between protein sequences and natural language in their sequential structures have inspired the application of large language models (LLMs) to protein understanding. Despite the success of LLMs in NLP, their effectiveness in comprehending protein sequences remains an open question, largely due to the absence of datasets linking protein sequences to descriptive text. Researchers have then attempted to adapt LLMs for protein understanding by integrating a protein sequence encoder with a pre-trained LLM. However, this adaptation raises a fundamental question: \"Can LLMs, originally designed for NLP, effectively comprehend protein sequences as a form of language?\" Current datasets fall short in addressing this question due to the lack of a direct correlation between protein sequences and corresponding text descriptions, limiting the ability to train and evaluate LLMs for protein understanding effectively. To bridge this gap, we introduce ProteinLMDataset, a dataset specifically designed for further self-supervised pretraining and supervised fine-tuning (SFT) of LLMs to enhance their capability for protein sequence comprehension. Specifically, ProteinLMDataset includes 17.46 billion tokens for pretraining and 893,000 instructions for SFT. Additionally, we present ProteinLMBench, the first benchmark dataset consisting of 944 manually verified multiple-choice questions for assessing the protein understanding capabilities of LLMs. ProteinLMBench incorporates protein-related details and sequences in multiple languages, establishing a new standard for evaluating LLMs' abilities in protein comprehension. The large language model InternLM2-7B, pretrained and fine-tuned on the ProteinLMDataset, outperforms GPT-4 on ProteinLMBench, achieving the highest accuracy score.", "authors": ["Yiqing Shen", "Zan Chen", "Michail Mamalakis", "Luhan He", "Haiyang Xia", "Tianbin Li", "Yanzhou Su", "Junjun He", "Yu Guang Wang"], "categories": ["q-bio.QM", "cs.AI", "cs.CL", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-06-08", "url": "https://arxiv.org/abs/2406.05540", "pdf_url": "https://arxiv.org/pdf/2406.05540v2", "arxiv_id": "2406.05540", "doi": "10.1109/BIBM62325.2024.10821894", "citation_count": 30, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Bioinformatics and Biomedicine", "quality_score": 0.3728}
{"id": "1b491df66514c78c2eb3dc8f7710cb8992db807cf7dbd1a673fc4008fa4c2a63", "sources": ["arxiv", "semantic_scholar"], "title": "MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training", "abstract": "Multiple Sequence Alignment (MSA) plays a pivotal role in unveiling the evolutionary trajectories of protein families. The accuracy of protein structure predictions is often compromised for protein sequences that lack sufficient homologous information to construct high quality MSA. Although various methods have been proposed to generate virtual MSA under these conditions, they fall short in comprehensively capturing the intricate coevolutionary patterns within MSA or require guidance from external oracle models. Here we introduce MSAGPT, a novel approach to prompt protein structure predictions via MSA generative pretraining in the low MSA regime. MSAGPT employs a simple yet effective 2D evolutionary positional encoding scheme to model complex evolutionary patterns. Endowed by this, its flexible 1D MSA decoding framework facilitates zero or few shot learning. Moreover, we demonstrate that leveraging the feedback from AlphaFold2 can further enhance the model capacity via Rejective Fine tuning (RFT) and Reinforcement Learning from AF2 Feedback (RLAF). Extensive experiments confirm the efficacy of MSAGPT in generating faithful virtual MSA to enhance the structure prediction accuracy. The transfer learning capabilities also highlight its great potential for facilitating other protein tasks.", "authors": ["Bo Chen", "Zhilei Bei", "Xingyi Cheng", "Pan Li", "Jie Tang", "Le Song"], "categories": ["q-bio.BM", "cs.AI", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2024-06-08", "url": "https://arxiv.org/abs/2406.05347", "pdf_url": "https://arxiv.org/pdf/2406.05347v3", "arxiv_id": "2406.05347", "doi": "10.1101/2024.06.10.598380", "citation_count": 17, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "bioRxiv", "quality_score": 0.3138}
{"id": "d1241cbe57df01b11eb8ec808f8623bb8f89bdd1051acff41109bc2692bc0866", "sources": ["arxiv", "semantic_scholar"], "title": "Benchmarking AlphaFold3's protein-protein complex accuracy and machine learning prediction reliability for binding free energy changes upon mutation", "abstract": "AlphaFold 3 (AF3), the latest version of protein structure prediction software, goes beyond its predecessors by predicting protein-protein complexes. It could revolutionize drug discovery and protein engineering, marking a major step towards comprehensive, automated protein structure prediction. However, independent validation of AF3's predictions is necessary. Evaluated using the SKEMPI 2.0 database which involves 317 protein-protein complexes and 8338 mutations, AF3 complex structures give rise to a very good Pearson correlation coefficient of 0.86 for predicting protein-protein binding free energy changes upon mutation, slightly less than the 0.88 achieved earlier with the Protein Data Bank (PDB) structures. Nonetheless, AF3 complex structures led to a 8.6% increase in the prediction RMSE compared to original PDB complex structures. Additionally, some of AF3's complex structures have large errors, which were not captured in its ipTM performance metric. Finally, it is found that AF3's complex structures are not reliable for intrinsically flexible regions or domains.", "authors": ["JunJie Wee", "Guo-Wei Wei"], "categories": ["q-bio.BM", "math.AT", "q-bio.QM"], "fields_of_study": ["Biology", "Mathematics", "Medicine"], "published_date": "2024-06-06", "url": "https://arxiv.org/abs/2406.03979", "pdf_url": "https://arxiv.org/pdf/2406.03979v1", "arxiv_id": "2406.03979", "doi": null, "citation_count": 23, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3451}
{"id": "5684b3f2a90ed51a71b783a90d8f74a047b0320edef40eeba153078ee0de2e90", "sources": ["arxiv", "semantic_scholar"], "title": "PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications", "abstract": "Developing intelligent pediatric consultation systems offers promising prospects for improving diagnostic efficiency, especially in China, where healthcare resources are scarce. Despite recent advances in Large Language Models (LLMs) for Chinese medicine, their performance is sub-optimal in pediatric applications due to inadequate instruction data and vulnerable training procedures. To address the above issues, this paper builds PedCorpus, a high-quality dataset of over 300,000 multi-task instructions from pediatric textbooks, guidelines, and knowledge graph resources to fulfil diverse diagnostic demands. Upon well-designed PedCorpus, we propose PediatricsGPT, the first Chinese pediatric LLM assistant built on a systematic and robust training pipeline. In the continuous pre-training phase, we introduce a hybrid instruction pre-training mechanism to mitigate the internal-injected knowledge inconsistency of LLMs for medical domain adaptation. Immediately, the full-parameter Supervised Fine-Tuning (SFT) is utilized to incorporate the general medical knowledge schema into the models. After that, we devise a direct following preference optimization to enhance the generation of pediatrician-like humanistic responses. In the parameter-efficient secondary SFT phase, a mixture of universal-specific experts strategy is presented to resolve the competency conflict between medical generalist and pediatric expertise mastery. Extensive results based on the metrics, GPT-4, and doctor evaluations on distinct doctor downstream tasks show that PediatricsGPT consistently outperforms previous Chinese medical LLMs. Our model and dataset will be open-source for community development.", "authors": ["Dingkang Yang", "Jinjie Wei", "Dongling Xiao", "Shunli Wang", "Tong Wu", "Gang Li", "Mingcheng Li", "Shuaibing Wang", "Jiawei Chen", "Yue Jiang", "Qingyao Xu", "Ke Li", "Peng Zhai", "Lihua Zhang"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-29", "url": "https://arxiv.org/abs/2405.19266", "pdf_url": "https://arxiv.org/pdf/2405.19266v4", "arxiv_id": "2405.19266", "doi": "10.48550/arXiv.2405.19266", "citation_count": 37, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "Neural Information Processing Systems", "quality_score": 0.3949}
{"id": "eb05d23bacc9aca7ed631e50d6d3635a7a343bfe7ab81fe76e91876643eaf147", "sources": ["arxiv", "semantic_scholar"], "title": "Boosting Protein Language Models with Negative Sample Mining", "abstract": "We introduce a pioneering methodology for boosting large language models in the domain of protein representation learning. Our primary contribution lies in the refinement process for correlating the over-reliance on co-evolution knowledge, in a way that networks are trained to distill invaluable insights from negative samples, constituted by protein pairs sourced from disparate categories. By capitalizing on this novel approach, our technique steers the training of transformer-based models within the attention score space. This advanced strategy not only amplifies performance but also reflects the nuanced biological behaviors exhibited by proteins, offering aligned evidence with traditional biological mechanisms such as protein-protein interaction. We experimentally observed improved performance on various tasks over datasets, on top of several well-established large protein models. This innovative paradigm opens up promising horizons for further progress in the realms of protein research and computational biology.", "authors": ["Yaoyao Xu", "Xinjian Zhao", "Xiaozhuang Song", "Benyou Wang", "Tianshu Yu"], "categories": ["cs.AI", "cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-28", "url": "https://arxiv.org/abs/2405.17902", "pdf_url": "https://arxiv.org/pdf/2405.17902v2", "arxiv_id": "2405.17902", "doi": "10.48550/arXiv.2405.17902", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "a0b7cc577019d1575ad631664c31136505b36d6e08d9c62369f9516a5113e6e7", "sources": ["arxiv", "semantic_scholar"], "title": "PatchProt: Hydrophobic patch prediction using protein foundation models", "abstract": "Hydrophobic patches on protein surfaces play important functional roles in protein-protein and protein-ligand interactions. Large hydrophobic surfaces are also involved in the progression of aggregation diseases. Predicting exposed hydrophobic patches from a protein sequence has been shown to be a difficult task. Fine-tuning foundation models allows for adapting a model to the specific nuances of a new task using a much smaller dataset. Additionally, multi-task deep learning offers a promising solution for addressing data gaps, simultaneously outperforming single-task methods. In this study, we harnessed a recently released leading large language model ESM-2. Efficient fine-tuning of ESM-2 was achieved by leveraging a recently developed parameter-efficient fine-tuning method. This approach enabled comprehensive training of model layers without excessive parameters and without the need to include a computationally expensive multiple sequence analysis. We explored several related tasks, at local (residue) and global (protein) levels, to improve the representation of the model. As a result, our fine-tuned ESM-2 model, PatchProt, cannot only predict hydrophobic patch areas but also outperforms existing methods at predicting primary tasks, including secondary structure and surface accessibility predictions. Importantly, our analysis shows that including related local tasks can improve predictions on more difficult global tasks. This research sets a new standard for sequence-based protein property prediction and highlights the remarkable potential of fine-tuning foundation models enriching the model representation by training over related tasks.", "authors": ["Dea Gogishvili", "Emmanuel Minois-Genin", "Jan van Eck", "Sanne Abeln"], "categories": ["q-bio.QM", "cs.AI", "cs.LG"], "fields_of_study": ["Biology", "Computer Science", "Medicine"], "published_date": "2024-05-24", "url": "https://arxiv.org/abs/2405.15928", "pdf_url": "https://arxiv.org/pdf/2405.15928v1", "arxiv_id": "2405.15928", "doi": "10.1093/bioadv/vbae154", "citation_count": 6, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Bioinformatics Advances", "quality_score": 0.2113}
{"id": "70122fc9f0338bf385215485ac3fa710996065401d7bd83abdab3517d8eb2ba0", "sources": ["arxiv", "semantic_scholar"], "title": "Learning the Language of Protein Structure", "abstract": "Representation learning and \\emph{de novo} generation of proteins are pivotal computational biology tasks. Whilst natural language processing (NLP) techniques have proven highly effective for protein sequence modelling, structure modelling presents a complex challenge, primarily due to its continuous and three-dimensional nature. Motivated by this discrepancy, we introduce an approach using a vector-quantized autoencoder that effectively tokenizes protein structures into discrete representations. This method transforms the continuous, complex space of protein structures into a manageable, discrete format with a codebook ranging from 4096 to 64000 tokens, achieving high-fidelity reconstructions with backbone root mean square deviations (RMSD) of approximately 1-5 Å. To demonstrate the efficacy of our learned representations, we show that a simple GPT model trained on our codebooks can generate novel, diverse, and designable protein structures. Our approach not only provides representations of protein structure, but also mitigates the challenges of disparate modal representations and sets a foundation for seamless, multi-modal integration, enhancing the capabilities of computational methods in protein design.", "authors": ["Benoit Gaujac", "Jérémie Donà", "Liviu Copoiu", "Timothy Atkinson", "Thomas Pierrot", "Thomas D. Barrett"], "categories": ["q-bio.QM", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-05-24", "url": "https://arxiv.org/abs/2405.15840", "pdf_url": "https://arxiv.org/pdf/2405.15840v2", "arxiv_id": "2405.15840", "doi": "10.48550/arXiv.2405.15840", "citation_count": 17, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3138}
{"id": "71d2f676af919902dbd3ee3ab6e0affdfbe5ab43b26320eb6033f12732353e22", "sources": ["arxiv", "semantic_scholar"], "title": "Babysit A Language Model From Scratch: Interactive Language Learning by Trials and Demonstrations", "abstract": "Humans are efficient language learners and inherently social creatures. Our language development is largely shaped by our social interactions, for example, the demonstration and feedback from caregivers. Contrary to human language learning, recent advancements in large language models have primarily adopted a non-interactive training paradigm, and refined pre-trained models through feedback afterward. In this work, we explore how corrective feedback from interactions influences neural language acquisition from scratch through systematically controlled experiments, assessing whether it contributes to word learning efficiency in language models. We introduce a trial-and-demonstration (TnD) learning framework that incorporates three distinct components: student trials, teacher demonstrations, and a reward conditioned on language competence at various developmental stages. Our experiments reveal that the TnD approach accelerates word acquisition for student models of equal and smaller numbers of parameters, and we highlight the significance of both trials and demonstrations. We further show that the teacher's choices of words influence students' word-specific learning efficiency, and a practice-makes-perfect effect is evident by a strong correlation between the frequency of words in trials and their respective learning curves. Our findings suggest that interactive language learning, with teacher demonstrations and active trials, can facilitate efficient word learning in language models.", "authors": ["Ziqiao Ma", "Zekun Wang", "Joyce Chai"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-05-22", "url": "https://arxiv.org/abs/2405.13828", "pdf_url": "https://arxiv.org/pdf/2405.13828v2", "arxiv_id": "2405.13828", "doi": "10.48550/arXiv.2405.13828", "citation_count": 14, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "North American Chapter of the Association for Computational Linguistics", "quality_score": 0.294}
{"id": "3ae1698ce8c0a871547135b6242855a4ebb9129b0fe0085882bba33e307ab8c3", "sources": ["arxiv", "semantic_scholar"], "title": "Identifying the minimal sets of distance restraints for FRET-assisted protein structural modeling", "abstract": "Proteins naturally occur in crowded cellular environments and interact with other proteins, nucleic acids, and organelles. Since most previous experimental protein structure determination techniques require that proteins occur in idealized, non-physiological environments, the effects of realistic cellular environments on protein structure are largely unexplored. Recently, Förster resonance energy transfer (FRET) has been shown to be an effective experimental method for investigating protein structure in vivo. Inter-residue distances measured in vivo can be incorporated as restraints in molecular dynamics (MD) simulations to model protein structural dynamics in vivo. Since most FRET studies only obtain inter-residue separations for a small number of amino acid pairs, it is important to determine the minimum number of restraints in the MD simulations that are required to achieve a given root-mean-square deviation (RMSD) from the experimental structural ensemble. Further, what is the optimal method for selecting these inter-residue restraints? Here, we implement several methods for selecting the most important FRET pairs and determine the number of pairs $N_{r}$ that are needed to induce conformational changes in proteins between two experimentally determined structures. We find that enforcing only a small fraction of restraints, $N_{r}/N \\lesssim 0.08$, where $N$ is the number of amino acids, can induce the conformational changes. These results establish the efficacy of FRET-assisted MD simulations for atomic scale structural modeling of proteins in vivo.", "authors": ["Zhuoyi Liu", "Alex T. Grigas", "Jacob Sumner", "Edward Knab", "Caitlin M. Davis", "Corey S. O'Hern"], "categories": ["physics.bio-ph", "q-bio.BM"], "fields_of_study": ["Physics", "Biology", "Medicine"], "published_date": "2024-05-13", "url": "https://arxiv.org/abs/2405.07983", "pdf_url": "https://arxiv.org/pdf/2405.07983v2", "arxiv_id": "2405.07983", "doi": "10.1002/pro.5219", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Protein Science", "quality_score": 0.0}
{"id": "27aa4f17a5d75f6efa1677455377ada76e922dcabfe586039b56b09597bef78d", "sources": ["arxiv", "semantic_scholar"], "title": "Fine-tuning Protein Language Models with Deep Mutational Scanning improves Variant Effect Prediction", "abstract": "Protein Language Models (PLMs) have emerged as performant and scalable tools for predicting the functional impact and clinical significance of protein-coding variants, but they still lag experimental accuracy. Here, we present a novel fine-tuning approach to improve the performance of PLMs with experimental maps of variant effects from Deep Mutational Scanning (DMS) assays using a Normalised Log-odds Ratio (NLR) head. We find consistent improvements in a held-out protein test set, and on independent DMS and clinical variant annotation benchmarks from ProteinGym and ClinVar. These findings demonstrate that DMS is a promising source of sequence diversity and supervised training data for improving the performance of PLMs for variant effect prediction.", "authors": ["Aleix Lafita", "Ferran Gonzalez", "Mahmoud Hossam", "Paul Smyth", "Jacob Deasy", "Ari Allyn-Feuer", "Daniel Seaton", "Stephen Young"], "categories": ["q-bio.GN", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-05-10", "url": "https://arxiv.org/abs/2405.06729", "pdf_url": "https://arxiv.org/pdf/2405.06729v1", "arxiv_id": "2405.06729", "doi": "10.48550/arXiv.2405.06729", "citation_count": 16, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3076}
{"id": "e8e9616cc1ca90d9351412243e1e3a9787ea28c3e7130bdb0c637635c1158e0c", "sources": ["arxiv", "semantic_scholar"], "title": "Protein binding affinity prediction under multiple substitutions applying eGNNs on Residue and Atomic graphs combined with Language model information: eGRAL", "abstract": "Protein-protein interactions (PPIs) play a crucial role in numerous biological processes. Developing methods that predict binding affinity changes under substitution mutations is fundamental for modelling and re-engineering biological systems. Deep learning is increasingly recognized as a powerful tool capable of bridging the gap between in-silico predictions and in-vitro observations. With this contribution, we propose eGRAL, a novel SE(3) equivariant graph neural network (eGNN) architecture designed for predicting binding affinity changes from multiple amino acid substitutions in protein complexes. eGRAL leverages residue, atomic and evolutionary scales, thanks to features extracted from protein large language models. To address the limited availability of large-scale affinity assays with structural information, we generate a simulated dataset comprising approximately 500,000 data points. Our model is pre-trained on this dataset, then fine-tuned and tested on experimental data.", "authors": ["Arturo Fiorellini-Bernardis", "Sebastien Boyer", "Christoph Brunken", "Bakary Diallo", "Karim Beguir", "Nicolas Lopez-Carranza", "Oliver Bent"], "categories": ["q-bio.QM", "cs.AI", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2024-05-03", "url": "https://arxiv.org/abs/2405.02374", "pdf_url": "https://arxiv.org/pdf/2405.02374v1", "arxiv_id": "2405.02374", "doi": "10.48550/arXiv.2405.02374", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0753}
{"id": "bd9fa1e533ac60d118af2ff6e4816192fd52a4d2e0bca7b7b99b1e5c7671df31", "sources": ["arxiv", "semantic_scholar"], "title": "Detection of circular permutations by Protein Language Models", "abstract": "Protein circular permutations are crucial for understanding protein evolution and functionality. Traditional detection methods, sequence-based or structure-based, struggle with accuracy and computational efficiency, the latter also limited by treating proteins as rigid bodies. The plmCP method, utilizing a protein language model, not only speeds up the detection process but also enhances the accuracy of identifying circular permutations, contributing significantly to protein research and engineering by acknowledging structural flexibility.", "authors": ["Yue Hu", "Bin Huang", "Chunzi Zang"], "categories": ["q-bio.QM"], "fields_of_study": ["Medicine", "Biology"], "published_date": "2024-04-23", "url": "https://arxiv.org/abs/2404.15087", "pdf_url": "https://arxiv.org/pdf/2404.15087v2", "arxiv_id": "2404.15087", "doi": "10.1016/j.csbj.2024.12.029", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Computational and Structural Biotechnology Journal", "quality_score": 0.0753}
{"id": "3178545dea9826a95d7ef4b463c338df7ae9d07165c419654c9276ae6c49d870", "sources": ["arxiv", "semantic_scholar"], "title": "Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models", "abstract": "Fine-tuning Pre-trained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches. As a widely applied powerful technique in natural language processing, employing Parameter-Efficient Fine-Tuning techniques could potentially enhance the performance of PLMs. However, the direct transfer to life science tasks is non-trivial due to the different training strategies and data forms. To address this gap, we introduce SES-Adapter, a simple, efficient, and scalable adapter method for enhancing the representation learning of PLMs. SES-Adapter incorporates PLM embeddings with structural sequence embeddings to create structure-aware representations. We show that the proposed method is compatible with different PLM architectures and across diverse tasks. Extensive evaluations are conducted on 2 types of folding structures with notable quality differences, 9 state-of-the-art baselines, and 9 benchmark datasets across distinct downstream tasks. Results show that compared to vanilla PLMs, SES-Adapter improves downstream task performance by a maximum of 11% and an average of 3%, with significantly accelerated training speed by a maximum of 1034% and an average of 362%, the convergence rate is also improved by approximately 2 times. Moreover, positive optimization is observed even with low-quality predicted structures. The source code for SES-Adapter is available at https://github.com/tyang816/SES-Adapter.", "authors": ["Yang Tan", "Mingchen Li", "Bingxin Zhou", "Bozitao Zhong", "Lirong Zheng", "Pan Tan", "Ziyi Zhou", "Huiqun Yu", "Guisheng Fan", "Liang Hong"], "categories": ["cs.CL", "cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology", "Medicine"], "published_date": "2024-04-23", "url": "https://arxiv.org/abs/2404.14850", "pdf_url": "https://arxiv.org/pdf/2404.14850v1", "arxiv_id": "2404.14850", "doi": "10.48550/arXiv.2404.14850", "citation_count": 21, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/tyang816/SES-Adapter", "venue": "Journal of Chemical Information and Modeling", "quality_score": 0.3356}
{"id": "82bac94f1ebc01f0031e52a53e4916ee4a3feb7c0e0d19033e465a62b854845d", "sources": ["arxiv", "semantic_scholar"], "title": "ScaleFold: Reducing AlphaFold Initial Training Time to 10 Hours", "abstract": "AlphaFold2 has been hailed as a breakthrough in protein folding. It can rapidly predict protein structures with lab-grade accuracy. However, its implementation does not include the necessary training code. OpenFold is the first trainable public reimplementation of AlphaFold. AlphaFold training procedure is prohibitively time-consuming, and gets diminishing benefits from scaling to more compute resources. In this work, we conducted a comprehensive analysis on the AlphaFold training procedure based on Openfold, identified that inefficient communications and overhead-dominated computations were the key factors that prevented the AlphaFold training from effective scaling. We introduced ScaleFold, a systematic training method that incorporated optimizations specifically for these factors. ScaleFold successfully scaled the AlphaFold training to 2080 NVIDIA H100 GPUs with high resource utilization. In the MLPerf HPC v3.0 benchmark, ScaleFold finished the OpenFold benchmark in 7.51 minutes, shown over $6\\times$ speedup than the baseline. For training the AlphaFold model from scratch, ScaleFold completed the pretraining in 10 hours, a significant improvement over the seven days required by the original AlphaFold pretraining baseline.", "authors": ["Feiwen Zhu", "Arkadiusz Nowaczynski", "Rundong Li", "Jie Xin", "Yifei Song", "Michal Marcinkiewicz", "Sukru Burc Eryilmaz", "Jun Yang", "Michael Andersch"], "categories": ["cs.LG", "cs.AI", "cs.DC", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-04-17", "url": "https://arxiv.org/abs/2404.11068", "pdf_url": "https://arxiv.org/pdf/2404.11068v1", "arxiv_id": "2404.11068", "doi": "10.1145/3649329.3657326", "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Design Automation Conference", "quality_score": 0.2698}
{"id": "2e66784743ed2c15fd4452170a70c0787c82369075458d2d53884a6d81f0c93e", "sources": ["arxiv", "semantic_scholar"], "title": "HelixFold-Multimer: Elevating Protein Complex Structure Prediction to New Heights", "abstract": "While monomer protein structure prediction tools boast impressive accuracy, the prediction of protein complex structures remains a daunting challenge in the field. This challenge is particularly pronounced in scenarios involving complexes with protein chains from different species, such as antigen-antibody interactions, where accuracy often falls short. Limited by the accuracy of complex prediction, tasks based on precise protein-protein interaction analysis also face obstacles. In this report, we highlight the ongoing advancements of our protein complex structure prediction model, HelixFold-Multimer, underscoring its enhanced performance. HelixFold-Multimer provides precise predictions for diverse protein complex structures, especially in therapeutic protein interactions. Notably, HelixFold-Multimer achieves remarkable success in antigen-antibody and peptide-protein structure prediction, greatly surpassing AlphaFold 3. HelixFold-Multimer is now available for public use on the PaddleHelix platform, offering both a general version and an antigen-antibody version. Researchers can conveniently access and utilize this service for their development needs.", "authors": ["Xiaomin Fang", "Jie Gao", "Jing Hu", "Lihang Liu", "Yang Xue", "Xiaonan Zhang", "Kunrui Zhu"], "categories": ["q-bio.BM", "cs.AI"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-04-16", "url": "https://arxiv.org/abs/2404.10260", "pdf_url": "https://arxiv.org/pdf/2404.10260v2", "arxiv_id": "2404.10260", "doi": "10.48550/arXiv.2404.10260", "citation_count": 14, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.294}
{"id": "ea062e2411156c0d3caa22f40dafc93738cd42fbcaf81582356fd02b79a16076", "sources": ["arxiv", "semantic_scholar"], "title": "PRODIS -- a speech database and a phoneme-based language model for the study of predictability effects in Polish", "abstract": "We present a speech database and a phoneme-level language model of Polish. The database and model are designed for the analysis of prosodic and discourse factors and their impact on acoustic parameters in interaction with predictability effects. The database is also the first large, publicly available Polish speech corpus of excellent acoustic quality that can be used for phonetic analysis and training of multi-speaker speech technology systems. The speech in the database is processed in a pipeline that achieves a 90% degree of automation. It incorporates state-of-the-art, freely available tools enabling database expansion or adaptation to additional languages.", "authors": ["Zofia Malisz", "Jan Foremski", "Małgorzata Kul"], "categories": ["cs.CL", "cs.SD", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-04-15", "url": "https://arxiv.org/abs/2404.10112", "pdf_url": "https://arxiv.org/pdf/2404.10112v1", "arxiv_id": "2404.10112", "doi": "10.48550/arXiv.2404.10112", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Language Resources and Evaluation", "quality_score": 0.0753}
{"id": "12bb9962964829e2930b933f77322bdfffdc9979d32f6509b3d9ba2fefb95c37", "sources": ["arxiv", "semantic_scholar"], "title": "Auxiliary task demands mask the capabilities of smaller language models", "abstract": "Developmental psychologists have argued about when cognitive capacities such as language understanding or theory of mind emerge. These debates often hinge on the concept of \"task demands\" -- the auxiliary challenges associated with performing a particular evaluation -- that may mask the child's underlying ability. The same issues arise when measuring the capacities of language models (LMs): performance on a task is a function of the model's underlying knowledge, combined with the model's ability to interpret and perform the task given its available resources. Here, we show that for analogical reasoning, reflective reasoning, word prediction, and grammaticality judgments, evaluation methods with greater task demands yield lower performance than evaluations with reduced demands. This \"demand gap\" is most pronounced for models with fewer parameters and less training data. Our results illustrate that LM performance should not be interpreted as a direct indication of intelligence (or lack thereof), but as a reflection of capacities seen through the lens of researchers' design choices.", "authors": ["Jennifer Hu", "Michael C. Frank"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-04-03", "url": "https://arxiv.org/abs/2404.02418", "pdf_url": "https://arxiv.org/pdf/2404.02418v2", "arxiv_id": "2404.02418", "doi": "10.48550/arXiv.2404.02418", "citation_count": 62, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4498}
{"id": "66acb8541b33578b36663425d587945f09653793619d871d74cc701ba2bb2bba", "sources": ["arxiv", "semantic_scholar"], "title": "ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction", "abstract": "The prediction of protein-protein interactions (PPIs) is crucial for understanding biological functions and diseases. Previous machine learning approaches to PPI prediction mainly focus on direct physical interactions, ignoring the broader context of nonphysical connections through intermediate proteins, thus limiting their effectiveness. The emergence of Large Language Models (LLMs) provides a new opportunity for addressing this complex biological challenge. By transforming structured data into natural language prompts, we can map the relationships between proteins into texts. This approach allows LLMs to identify indirect connections between proteins, tracing the path from upstream to downstream. Therefore, we propose a novel framework ProLLM that employs an LLM tailored for PPI for the first time. Specifically, we propose Protein Chain of Thought (ProCoT), which replicates the biological mechanism of signaling pathways as natural language prompts. ProCoT considers a signaling pathway as a protein reasoning process, which starts from upstream proteins and passes through several intermediate proteins to transmit biological signals to downstream proteins. Thus, we can use ProCoT to predict the interaction between upstream proteins and downstream proteins. The training of ProLLM employs the ProCoT format, which enhances the model's understanding of complex biological problems. In addition to ProCoT, this paper also contributes to the exploration of embedding replacement of protein sites in natural language prompts, and instruction fine-tuning in protein knowledge datasets. We demonstrate the efficacy of ProLLM through rigorous validation against benchmark datasets, showing significant improvement over existing methods in terms of prediction accuracy and generalizability. The code is available at: https://github.com/MingyuJ666/ProLLM.", "authors": ["Mingyu Jin", "Haochen Xue", "Zhenting Wang", "Boming Kang", "Ruosong Ye", "Kaixiong Zhou", "Mengnan Du", "Yongfeng Zhang"], "categories": ["q-bio.BM", "cs.LG", "q-bio.MN"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2024-03-30", "url": "https://arxiv.org/abs/2405.06649", "pdf_url": "https://arxiv.org/pdf/2405.06649v2", "arxiv_id": "2405.06649", "doi": "10.1101/2024.04.18.590025", "citation_count": 35, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/MingyuJ666/ProLLM", "venue": "bioRxiv", "quality_score": 0.3891}
{"id": "e7a4c05fe267c4a25bd9ae4c2c68e3a040840bc2e647b6cafb71fa1342ac3e93", "sources": ["arxiv", "semantic_scholar"], "title": "IDP-Bert: Predicting Properties of Intrinsically Disordered Proteins (IDP) Using Large Language Models", "abstract": "Intrinsically Disordered Proteins (IDPs) constitute a large and structure-less class of proteins with significant functions. The existence of IDPs challenges the conventional notion that the biological functions of proteins rely on their three-dimensional structures. Despite lacking well-defined spatial arrangements, they exhibit diverse biological functions, influencing cellular processes and shedding light on the disease mechanisms. However, it is expensive to run experiments or simulations to characterize this class of proteins. Consequently, we designed an ML model that relies solely on amino acid sequences. In this study, we introduce IDP-Bert model, a deep-learning architecture leveraging Transformers and Protein Language Models (PLMs) to map sequences directly to IDPs properties. Our experiments demonstrate accurate predictions of IDPs properties, including Radius of Gyration, end-to-end Decorrelation Time, and Heat Capacity.", "authors": ["Parisa Mollaei", "Danush Sadasivam", "Chakradhar Guntuboina", "Amir Barati Farimani"], "categories": ["q-bio.BM"], "fields_of_study": ["Medicine", "Biology"], "published_date": "2024-03-28", "url": "https://arxiv.org/abs/2403.19762", "pdf_url": "https://arxiv.org/pdf/2403.19762v2", "arxiv_id": "2403.19762", "doi": "10.1021/acs.jpcb.4c02507", "citation_count": 10, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Physical Chemistry B", "quality_score": 0.2603}
{"id": "79e2e96fd884323e4fb0ed5426f8afc7b716b92f7a54400662b72aef932b8b45", "sources": ["arxiv", "semantic_scholar"], "title": "Are Compressed Language Models Less Subgroup Robust?", "abstract": "To reduce the inference cost of large language models, model compression is increasingly used to create smaller scalable models. However, little is known about their robustness to minority subgroups defined by the labels and attributes of a dataset. In this paper, we investigate the effects of 18 different compression methods and settings on the subgroup robustness of BERT language models. We show that worst-group performance does not depend on model size alone, but also on the compression method used. Additionally, we find that model compression does not always worsen the performance on minority subgroups. Altogether, our analysis serves to further research into the subgroup robustness of model compression.", "authors": ["Leonidas Gee", "Andrea Zugarini", "Novi Quadrianto"], "categories": ["cs.LG", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-26", "url": "https://arxiv.org/abs/2403.17811", "pdf_url": "https://arxiv.org/pdf/2403.17811v1", "arxiv_id": "2403.17811", "doi": "10.18653/v1/2023.emnlp-main.983", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.1193}
{"id": "f13a3031b7e1b19d233f29125e0d9d81ff088cb8330048b9c0dd1024e2bf25a7", "sources": ["arxiv", "semantic_scholar"], "title": "Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models", "abstract": "Language models have shown unprecedented capabilities, sparking debate over the source of their performance. Is it merely the outcome of learning syntactic patterns and surface level statistics, or do they extract semantics and a world model from the text? Prior work by Li et al. investigated this by training a GPT model on synthetic, randomly generated Othello games and found that the model learned an internal representation of the board state. We extend this work into the more complex domain of chess, training on real games and investigating our model's internal representations using linear probes and contrastive activations. The model is given no a priori knowledge of the game and is solely trained on next character prediction, yet we find evidence of internal representations of board state. We validate these internal representations by using them to make interventions on the model's activations and edit its internal board state. Unlike Li et al's prior synthetic dataset approach, our analysis finds that the model also learns to estimate latent variables like player skill to better predict the next character. We derive a player skill vector and add it to the model, improving the model's win rate by up to 2.6 times.", "authors": ["Adam Karvonen"], "categories": ["cs.LG", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-21", "url": "https://arxiv.org/abs/2403.15498", "pdf_url": "https://arxiv.org/pdf/2403.15498v2", "arxiv_id": "2403.15498", "doi": "10.48550/arXiv.2403.15498", "citation_count": 59, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4445}
{"id": "5cf25741665611e22072be9eb97db4afa25dc5927046a6b41539586549068b00", "sources": ["arxiv", "semantic_scholar"], "title": "Clinical information extraction for Low-resource languages with Few-shot learning using Pre-trained language models and Prompting", "abstract": "Automatic extraction of medical information from clinical documents poses several challenges: high costs of required clinical expertise, limited interpretability of model predictions, restricted computational resources and privacy regulations. Recent advances in domain-adaptation and prompting methods showed promising results with minimal training data using lightweight masked language models, which are suited for well-established interpretability methods. We are first to present a systematic evaluation of these methods in a low-resource setting, by performing multi-class section classification on German doctor's letters. We conduct extensive class-wise evaluations supported by Shapley values, to validate the quality of our small training data set and to ensure the interpretability of model predictions. We demonstrate that a lightweight, domain-adapted pretrained model, prompted with just 20 shots, outperforms a traditional classification model by 30.5% accuracy. Our results serve as a process-oriented guideline for clinical information extraction projects working with low-resource.", "authors": ["Phillip Richter-Pechanski", "Philipp Wiesenbach", "Dominic M. Schwab", "Christina Kiriakou", "Nicolas Geis", "Christoph Dieterich", "Anette Frank"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-20", "url": "https://arxiv.org/abs/2403.13369", "pdf_url": "https://arxiv.org/pdf/2403.13369v2", "arxiv_id": "2403.13369", "doi": "10.1017/nlp.2024.52", "citation_count": 12, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Natural Language Processing", "quality_score": 0.2785}
{"id": "5906bbc9c25873cbc482ed652fd00b99c63697c63b94defd62768fe697692e1d", "sources": ["arxiv", "semantic_scholar"], "title": "Document Author Classification Using Parsed Language Structure", "abstract": "Over the years there has been ongoing interest in detecting authorship of a text based on statistical properties of the text, such as by using occurrence rates of noncontextual words. In previous work, these techniques have been used, for example, to determine authorship of all of \\emph{The Federalist Papers}. Such methods may be useful in more modern times to detect fake or AI authorship. Progress in statistical natural language parsers introduces the possibility of using grammatical structure to detect authorship. In this paper we explore a new possibility for detecting authorship using grammatical structural information extracted using a statistical natural language parser. This paper provides a proof of concept, testing author classification based on grammatical structure on a set of \"proof texts,\" The Federalist Papers and Sanditon which have been as test cases in previous authorship detection studies. Several features extracted from the statistical natural language parser were explored: all subtrees of some depth from any level; rooted subtrees of some depth, part of speech, and part of speech by level in the parse tree. It was found to be helpful to project the features into a lower dimensional space. Statistical experiments on these documents demonstrate that information from a statistical parser can, in fact, assist in distinguishing authors.", "authors": ["Todd K Moon", "Jacob H. Gunther"], "categories": ["cs.CL", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2024-03-20", "url": "https://arxiv.org/abs/2403.13253", "pdf_url": "https://arxiv.org/pdf/2403.13253v1", "arxiv_id": "2403.13253", "doi": "10.5121/ijnlc.2024.13104", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Journal on Natural Language Computing", "quality_score": 0.0}
{"id": "85e0b9ee143f1fcc602843116f647c2421c54521c54cbd63c84b4e8d608feda1", "sources": ["arxiv", "semantic_scholar"], "title": "Pragmatic Competence Evaluation of Large Language Models for the Korean Language", "abstract": "Benchmarks play a significant role in the current evaluation of Large Language Models (LLMs), yet they often overlook the models' abilities to capture the nuances of human language, primarily focusing on evaluating embedded knowledge and technical skills. To address this gap, our study evaluates how well LLMs understand context-dependent expressions from a pragmatic standpoint, specifically in Korean. We use both Multiple-Choice Questions (MCQs) for automatic evaluation and Open-Ended Questions (OEQs) assessed by human experts. Our results show that GPT-4 leads with scores of 81.11 in MCQs and 85.69 in OEQs, closely followed by HyperCLOVA X. Additionally, while few-shot learning generally improves performance, Chain-of-Thought (CoT) prompting tends to encourage literal interpretations, which may limit effective pragmatic inference. Our findings highlight the need for LLMs to better understand and generate language that reflects human communicative norms.", "authors": ["Dojun Park", "Jiwoo Lee", "Hyeyun Jeong", "Seohyun Park", "Sungeun Lee"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-19", "url": "https://arxiv.org/abs/2403.12675", "pdf_url": "https://arxiv.org/pdf/2403.12675v2", "arxiv_id": "2403.12675", "doi": null, "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Pacific Asia Conference on Language, Information and Computation", "quality_score": 0.25}
{"id": "c308ff793346730f430bd9bb7891dc496dbe4dd1d152b39a6e64dc671afd1e2a", "sources": ["arxiv", "semantic_scholar"], "title": "Language Evolution with Deep Learning", "abstract": "Computational modeling plays an essential role in the study of language emergence. It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language within a simulated controlled environment. Several methods have been used to investigate the origin of our language, including agent-based systems, Bayesian agents, genetic algorithms, and rule-based systems. This chapter explores another class of computational models that have recently revolutionized the field of machine learning: deep learning models. The chapter introduces the basic concepts of deep and reinforcement learning methods and summarizes their helpfulness for simulating language emergence. It also discusses the key findings, limitations, and recent attempts to build realistic simulations. This chapter targets linguists and cognitive scientists seeking an introduction to deep learning as a tool to investigate language evolution.", "authors": ["Mathieu Rita", "Paul Michel", "Rahma Chaabouni", "Olivier Pietquin", "Emmanuel Dupoux", "Florian Strub"], "categories": ["cs.CL", "cs.MA"], "fields_of_study": ["Computer Science"], "published_date": "2024-03-18", "url": "https://arxiv.org/abs/2403.11958", "pdf_url": "https://arxiv.org/pdf/2403.11958v1", "arxiv_id": "2403.11958", "doi": "10.48550/arXiv.2403.11958", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "23bf9dbb62f19e2e7d79222f2d0a38475c004cd7f6e6c33bc1d7527e99668ef4", "sources": ["arxiv", "semantic_scholar"], "title": "On Recovering Higher-order Interactions from Protein Language Models", "abstract": "Protein language models leverage evolutionary information to perform state-of-the-art 3D structure and zero-shot variant prediction. Yet, extracting and explaining all the mutational interactions that govern model predictions remains difficult as it requires querying the entire amino acid space for $n$ sites using $20^n$ sequences, which is computationally expensive even for moderate values of $n$ (e.g., $n\\sim10$). Although approaches to lower the sample complexity exist, they often limit the interpretability of the model to just single and pairwise interactions. Recently, computationally scalable algorithms relying on the assumption of sparsity in the Fourier domain have emerged to learn interactions from experimental data. However, extracting interactions from language models poses unique challenges: it's unclear if sparsity is always present or if it is the only metric needed to assess the utility of Fourier algorithms. Herein, we develop a framework to do a systematic Fourier analysis of the protein language model ESM2 applied on three proteins-green fluorescent protein (GFP), tumor protein P53 (TP53), and G domain B1 (GB1)-across various sites for 228 experiments. We demonstrate that ESM2 is dominated by three regions in the sparsity-ruggedness plane, two of which are better suited for sparse Fourier transforms. Validations on two sample proteins demonstrate recovery of all interactions with $R^2=0.72$ in the more sparse region and $R^2=0.66$ in the more dense region, using only 7 million out of $20^{10}\\sim10^{13}$ ESM2 samples, reducing the computational time by a staggering factor of 15,000. All codes and data are available on our GitHub repository https://github.com/amirgroup-codes/InteractionRecovery.", "authors": ["Darin Tsui", "Amirali Aghazadeh"], "categories": ["q-bio.BM", "cs.AI", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2024-03-15", "url": "https://arxiv.org/abs/2405.06645", "pdf_url": "https://arxiv.org/pdf/2405.06645v1", "arxiv_id": "2405.06645", "doi": "10.48550/arXiv.2405.06645", "citation_count": 9, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/amirgroup-codes/InteractionRecovery", "venue": "arXiv.org", "quality_score": 0.25}
{"id": "81d8ac72001a2f9a2b9ff6141d4704d9d34de13762fb0325af67c9facc5e2fe9", "sources": ["arxiv", "semantic_scholar"], "title": "Diffusion on language model encodings for protein sequence generation", "abstract": "Protein sequence design has seen significant advances through discrete diffusion and autoregressive approaches, yet the potential of continuous diffusion remains underexplored. Here, we present DiMA, a latent diffusion framework that operates on protein language model representations. Through systematic exploration of architectural choices and diffusion components, we develop a robust methodology that generalizes across multiple protein encoders ranging from 8M to 3B parameters. We demonstrate that our framework achieves consistently high performance across sequence-only (ESM-2, ESMc), dual-decodable (CHEAP), and multimodal (SaProt) representations using the same architecture and training approach. We extensively evaluate existing methods alongside DiMA using multiple metrics across two protein modalities, covering quality, diversity, novelty, and distribution matching of generated proteins. DiMA consistently produces novel, high-quality and diverse protein sequences and achieves strong results compared to baselines such as autoregressive, discrete diffusion and flow matching language models. The model demonstrates versatile functionality, supporting conditional generation tasks including protein family-generation, motif scaffolding and infilling, and fold-specific sequence design. This work provides a universal continuous diffusion framework for protein sequence generation, offering both architectural insights and practical applicability across various protein design scenarios. Code is released at \\href{https://github.com/MeshchaninovViacheslav/DiMA}{GitHub}.", "authors": ["Viacheslav Meshchaninov", "Pavel Strashnov", "Andrey Shevtsov", "Fedor Nikolaev", "Nikita Ivanisenko", "Olga Kardymon", "Dmitry Vetrov"], "categories": ["cs.LG", "cs.AI", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-03-06", "url": "https://arxiv.org/abs/2403.03726", "pdf_url": "https://arxiv.org/pdf/2403.03726v4", "arxiv_id": "2403.03726", "doi": null, "citation_count": 25, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/MeshchaninovViacheslav/DiMA}{GitHub}", "venue": "International Conference on Machine Learning", "quality_score": 0.3537}
{"id": "627b133b064603a539be91a0e27517549ec47db60c456d6c79068192dd878eae", "sources": ["arxiv", "semantic_scholar"], "title": "ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling", "abstract": "Protein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom level. This limitation prevents us from fully exploiting the capabilities of protein language models for applications involving both proteins and small molecules. In this paper, we propose ESM-AA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular modeling. ESM-AA achieves this by pre-training on multi-scale code-switch protein sequences and utilizing a multi-scale position encoding to capture relationships among residues and atoms. Experimental results indicate that ESM-AA surpasses previous methods in protein-molecule tasks, demonstrating the full utilization of protein language models. Further investigations reveal that through unified molecular modeling, ESM-AA not only gains molecular knowledge but also retains its understanding of proteins. The source codes of ESM-AA are publicly released at https://github.com/zhengkangjie/ESM-AA.", "authors": ["Kangjie Zheng", "Siyu Long", "Tianyu Lu", "Junwei Yang", "Xinyu Dai", "Ming Zhang", "Zaiqing Nie", "Wei-Ying Ma", "Hao Zhou"], "categories": ["q-bio.BM", "cs.CE", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2024-03-05", "url": "https://arxiv.org/abs/2403.12995", "pdf_url": "https://arxiv.org/pdf/2403.12995v4", "arxiv_id": "2403.12995", "doi": "10.1101/2024.03.04.583284", "citation_count": 19, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/zhengkangjie/ESM-AA", "venue": "bioRxiv", "quality_score": 0.3253}
{"id": "16fe3c2c6abb50260051879d07d2e2f84b992b27d92b039395736072129baf9c", "sources": ["arxiv", "semantic_scholar"], "title": "A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration", "abstract": "Proteins are essential for life, and their structure determines their function. The protein secondary structure is formed by the folding of the protein primary structure, and the protein tertiary structure is formed by the bending and folding of the secondary structure. Therefore, the study of protein secondary structure is very helpful to the overall understanding of protein structure. Although the accuracy of protein secondary structure prediction has continuously improved with the development of machine learning and deep learning, progress in the field of protein structure prediction, unfortunately, remains insufficient to meet the large demand for protein information. Therefore, based on the advantages of deep learning-based methods in feature extraction and learning ability, this paper adopts a two-dimensional fusion deep neural network model, DstruCCN, which uses Convolutional Neural Networks (CCN) and a supervised Transformer protein language model for single-sequence protein structure prediction. The training features of the two are combined to predict the protein Transformer binding site matrix, and then the three-dimensional structure is reconstructed using energy minimization.", "authors": ["Yanlin Zhou", "Kai Tan", "Xinyu Shen", "Zheng He", "Haotian Zheng"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-02-29", "url": "https://arxiv.org/abs/2402.19095", "pdf_url": "https://arxiv.org/pdf/2402.19095v2", "arxiv_id": "2402.19095", "doi": "10.1109/ICAACE61206.2024.10548253", "citation_count": 17, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3138}
{"id": "8566277431df6c4de2e60b89887ee89d0def38c46ca1933ed95fa6d808171f0f", "sources": ["arxiv", "semantic_scholar"], "title": "Protein Multimer Structure Prediction via Prompt Learning", "abstract": "Understanding the 3D structures of protein multimers is crucial, as they play a vital role in regulating various cellular processes. It has been empirically confirmed that the multimer structure prediction~(MSP) can be well handled in a step-wise assembly fashion using provided dimer structures and predicted protein-protein interactions~(PPIs). However, due to the biological gap in the formation of dimers and larger multimers, directly applying PPI prediction techniques can often cause a \\textit{poor generalization} to the MSP task. To address this challenge, we aim to extend the PPI knowledge to multimers of different scales~(i.e., chain numbers). Specifically, we propose \\textbf{\\textsc{PromptMSP}}, a pre-training and \\textbf{Prompt} tuning framework for \\textbf{M}ultimer \\textbf{S}tructure \\textbf{P}rediction. First, we tailor the source and target tasks for effective PPI knowledge learning and efficient inference, respectively. We design PPI-inspired prompt learning to narrow the gaps of two task formats and generalize the PPI knowledge to multimers of different scales. We provide a meta-learning strategy to learn a reliable initialization of the prompt model, enabling our prompting framework to effectively adapt to limited data for large-scale multimers. Empirically, we achieve both significant accuracy (RMSD and TM-Score) and efficiency improvements compared to advanced MSP models. The code, data and checkpoints are released at \\url{https://github.com/zqgao22/PromptMSP}.", "authors": ["Ziqi Gao", "Xiangguo Sun", "Zijing Liu", "Yu Li", "Hong Cheng", "Jia Li"], "categories": ["cs.CE"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-29", "url": "https://arxiv.org/abs/2402.18813", "pdf_url": "https://arxiv.org/pdf/2402.18813v1", "arxiv_id": "2402.18813", "doi": "10.48550/arXiv.2402.18813", "citation_count": 16, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/zqgao22/PromptMSP}", "venue": "International Conference on Learning Representations", "quality_score": 0.3076}
{"id": "f7ed9611db3d0e691778ac27b501a598a3db0c1ab01301849aecc792b84bbdcf", "sources": ["arxiv", "semantic_scholar"], "title": "ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training", "abstract": "We propose ProtLLM, a versatile cross-modal large language model (LLM) for both protein-centric and protein-language tasks. ProtLLM features a unique dynamic protein mounting mechanism, enabling it to handle complex inputs where the natural language text is interspersed with an arbitrary number of proteins. Besides, we propose the protein-as-word language modeling approach to train ProtLLM. By developing a specialized protein vocabulary, we equip the model with the capability to predict not just natural language but also proteins from a vast pool of candidates. Additionally, we construct a large-scale interleaved protein-text dataset, named InterPT, for pre-training. This dataset comprehensively encompasses both (1) structured data sources like protein annotations and (2) unstructured data sources like biological research papers, thereby endowing ProtLLM with crucial knowledge for understanding proteins. We evaluate ProtLLM on classic supervised protein-centric tasks and explore its novel protein-language applications. Experimental results demonstrate that ProtLLM not only achieves superior performance against protein-specialized baselines on protein-centric tasks but also induces zero-shot and in-context learning capabilities on protein-language tasks.", "authors": ["Le Zhuo", "Zewen Chi", "Minghao Xu", "Heyan Huang", "Heqi Zheng", "Conghui He", "Xian-Ling Mao", "Wentao Zhang"], "categories": ["q-bio.BM", "cs.AI", "cs.CL", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-02-28", "url": "https://arxiv.org/abs/2403.07920", "pdf_url": "https://arxiv.org/pdf/2403.07920v1", "arxiv_id": "2403.07920", "doi": "10.48550/arXiv.2403.07920", "citation_count": 29, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.3693}
{"id": "1db93edbc47a3acd607b4e620e329fa4ed25c4c0dc0f48470727c205b67da618", "sources": ["arxiv", "semantic_scholar"], "title": "Diffusion Language Models Are Versatile Protein Learners", "abstract": "This paper introduces diffusion protein language model (DPLM), a versatile protein language model that demonstrates strong generative and predictive capabilities for protein sequences. We first pre-train scalable DPLMs from evolutionary-scale protein sequences within a generative self-supervised discrete diffusion probabilistic framework, which generalizes language modeling for proteins in a principled way. After pre-training, DPLM exhibits the ability to generate structurally plausible, novel, and diverse protein sequences for unconditional generation. We further demonstrate the proposed diffusion generative pre-training makes DPLM possess a better understanding of proteins, making it a superior representation learner, which can be fine-tuned for various predictive tasks, comparing favorably to ESM2 (Lin et al., 2022). Moreover, DPLM can be tailored for various needs, which showcases its prowess of conditional generation in several ways: (1) conditioning on partial peptide sequences, e.g., generating scaffolds for functional motifs with high success rate; (2) incorporating other modalities as conditioner, e.g., structure-conditioned generation for inverse folding; and (3) steering sequence generation towards desired properties, e.g., satisfying specified secondary structures, through a plug-and-play classifier guidance. Code is released at \\url{https://github.com/bytedance/dplm}.", "authors": ["Xinyou Wang", "Zaixiang Zheng", "Fei Ye", "Dongyu Xue", "Shujian Huang", "Quanquan Gu"], "categories": ["cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-02-28", "url": "https://arxiv.org/abs/2402.18567", "pdf_url": "https://arxiv.org/pdf/2402.18567v2", "arxiv_id": "2402.18567", "doi": "10.48550/arXiv.2402.18567", "citation_count": 130, "influential_citation_count": 15, "has_code": true, "code_url": "https://github.com/bytedance/dplm}", "venue": "International Conference on Machine Learning", "quality_score": 0.6021}
{"id": "2d361a6742ab3b6880d0db2962ed86fa6848d91092645eb75a088e08a9a77af2", "sources": ["arxiv", "semantic_scholar"], "title": "ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing", "abstract": "Recent advances in Protein Language Models (PLMs) have transformed protein engineering, yet unlike their counterparts in Natural Language Processing (NLP), current PLMs exhibit a fundamental limitation: they excel in either Protein Language Understanding (PLU) or Protein Language Generation (PLG), but rarely both. This fragmentation hinders progress in protein engineering. To bridge this gap, we introduce ProLLaMA, a multitask protein language model enhanced by the Evolutionary Protein Generation Framework (EPGF). We construct a comprehensive instruction dataset containing approximately 13 million samples with over 11,000 superfamily annotations to facilitate better modeling of sequence-function landscapes. We leverage a two-stage training approach to develop ProLLaMA, a multitask LLM with protein domain expertise. Our EPGF addresses the mismatch between statistic language modeling and biological constraints through three innovations: a multi-dimensional interpretable scorer, hierarchical efficient decoding, and a probabilistic-biophysical joint selection mechanism. Extensive experiments demonstrate that ProLLaMA excels in both unconditional and controllable protein generation tasks, achieving superior structural quality metrics compared to existing PLMs. Additionally, ProLLaMA demonstrates strong understanding capabilities with a 67.1% exact match rate in superfamily prediction. EPGF significantly enhances the biological viability of generated sequences, as evidenced by improved biophysical scores (+4.3%) and structural metrics (+14.5%). The project is available at https://github.com/PKU-YuanGroup/ProLLaMA.", "authors": ["Liuzhenghao Lv", "Zongying Lin", "Hao Li", "Yuyang Liu", "Jiaxi Cui", "Calvin Yu-Chian Chen", "Li Yuan", "Yonghong Tian"], "categories": ["cs.CE", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-02-26", "url": "https://arxiv.org/abs/2402.16445", "pdf_url": "https://arxiv.org/pdf/2402.16445v3", "arxiv_id": "2402.16445", "doi": "10.1109/TAI.2025.3564914", "citation_count": 89, "influential_citation_count": 10, "has_code": true, "code_url": "https://github.com/PKU-YuanGroup/ProLLaMA", "venue": "IEEE Transactions on Artificial Intelligence", "quality_score": 0.5207}
{"id": "4b3fa5ac2b12de8e3fe36b67a29a76eac458045ee52277a5a15fbff825a5860a", "sources": ["arxiv", "semantic_scholar"], "title": "How Important Is Tokenization in French Medical Masked Language Models?", "abstract": "Subword tokenization has become the prevailing standard in the field of natural language processing (NLP) over recent years, primarily due to the widespread utilization of pre-trained language models. This shift began with Byte-Pair Encoding (BPE) and was later followed by the adoption of SentencePiece and WordPiece. While subword tokenization consistently outperforms character and word-level tokenization, the precise factors contributing to its success remain unclear. Key aspects such as the optimal segmentation granularity for diverse tasks and languages, the influence of data sources on tokenizers, and the role of morphological information in Indo-European languages remain insufficiently explored. This is particularly pertinent for biomedical terminology, characterized by specific rules governing morpheme combinations. Despite the agglutinative nature of biomedical terminology, existing language models do not explicitly incorporate this knowledge, leading to inconsistent tokenization strategies for common terms. In this paper, we seek to delve into the complexities of subword tokenization in French biomedical domain across a variety of NLP tasks and pinpoint areas where further enhancements can be made. We analyze classical tokenization algorithms, including BPE and SentencePiece, and introduce an original tokenization strategy that integrates morpheme-enriched word segmentation into existing tokenization methods.", "authors": ["Yanis Labrak", "Adrien Bazoge", "Beatrice Daille", "Mickael Rouvier", "Richard Dufour"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-22", "url": "https://arxiv.org/abs/2402.15010", "pdf_url": "https://arxiv.org/pdf/2402.15010v2", "arxiv_id": "2402.15010", "doi": "10.48550/arXiv.2402.15010", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Language Resources and Evaluation", "quality_score": 0.1193}
{"id": "cd6a8a9ee40166a4dba02d9f168ca0a5b9b5acd51793aa42d5108a5cb53cd484", "sources": ["arxiv", "semantic_scholar"], "title": "Analysing The Impact of Sequence Composition on Language Model Pre-Training", "abstract": "Most language model pre-training frameworks concatenate multiple documents into fixed-length sequences and use causal masking to compute the likelihood of each token given its context; this strategy is widely adopted due to its simplicity and efficiency. However, to this day, the influence of the pre-training sequence composition strategy on the generalisation properties of the model remains under-explored. In this work, we find that applying causal masking can lead to the inclusion of distracting information from previous documents during pre-training, which negatively impacts the performance of the models on language modelling and downstream tasks. In intra-document causal masking, the likelihood of each token is only conditioned on the previous tokens in the same document, eliminating potential distracting information from previous documents and significantly improving performance. Furthermore, we find that concatenating related documents can reduce some potential distractions during pre-training, and our proposed efficient retrieval-based sequence construction method, BM25Chunk, can improve in-context learning (+11.6\\%), knowledge memorisation (+9.8\\%), and context utilisation (+7.2\\%) abilities of language models without sacrificing efficiency.", "authors": ["Yu Zhao", "Yuanbin Qu", "Konrad Staniszewski", "Szymon Tworkowski", "Wei Liu", "Piotr Miłoś", "Yuxiang Wu", "Pasquale Minervini"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-21", "url": "https://arxiv.org/abs/2402.13991", "pdf_url": "https://arxiv.org/pdf/2402.13991v1", "arxiv_id": "2402.13991", "doi": "10.18653/v1/2024.acl-long.427", "citation_count": 23, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.3451}
{"id": "4f29fe266275011a800fbb298a64913404ac6356e54a4aebabdf381b53238deb", "sources": ["arxiv", "semantic_scholar"], "title": "TEXT2AFFORD: Probing Object Affordance Prediction abilities of Language Models solely from Text", "abstract": "We investigate the knowledge of object affordances in pre-trained language models (LMs) and pre-trained Vision-Language models (VLMs). A growing body of literature shows that PTLMs fail inconsistently and non-intuitively, demonstrating a lack of reasoning and grounding. To take a first step toward quantifying the effect of grounding (or lack thereof), we curate a novel and comprehensive dataset of object affordances -- Text2Afford, characterized by 15 affordance classes. Unlike affordance datasets collected in vision and language domains, we annotate in-the-wild sentences with objects and affordances. Experimental results reveal that PTLMs exhibit limited reasoning abilities when it comes to uncommon object affordances. We also observe that pre-trained VLMs do not necessarily capture object affordances effectively. Through few-shot fine-tuning, we demonstrate improvement in affordance knowledge in PTLMs and VLMs. Our research contributes a novel dataset for language grounding tasks, and presents insights into LM capabilities, advancing the understanding of object affordances. Codes and data are available at https://github.com/sayantan11995/Text2Afford", "authors": ["Sayantan Adak", "Daivik Agrawal", "Animesh Mukherjee", "Somak Aditya"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-20", "url": "https://arxiv.org/abs/2402.12881", "pdf_url": "https://arxiv.org/pdf/2402.12881v3", "arxiv_id": "2402.12881", "doi": "10.18653/v1/2024.conll-1.27", "citation_count": 7, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/sayantan11995/Text2Afford", "venue": "Conference on Computational Natural Language Learning", "quality_score": 0.2258}
{"id": "97aa69029586ee3c37de4bdabc558289466e08f5b3c14f62f8e8e5e2bb8e8d51", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions", "abstract": "The advent of Large Language Models (LLMs) has significantly reshaped the trajectory of the AI revolution. Nevertheless, these LLMs exhibit a notable limitation, as they are primarily adept at processing textual information. To address this constraint, researchers have endeavored to integrate visual capabilities with LLMs, resulting in the emergence of Vision-Language Models (VLMs). These advanced models are instrumental in tackling more intricate tasks such as image captioning and visual question answering. In our comprehensive survey paper, we delve into the key advancements within the realm of VLMs. Our classification organizes VLMs into three distinct categories: models dedicated to vision-language understanding, models that process multimodal inputs to generate unimodal (textual) outputs and models that both accept and produce multimodal inputs and outputs.This classification is based on their respective capabilities and functionalities in processing and generating various modalities of data.We meticulously dissect each model, offering an extensive analysis of its foundational architecture, training data sources, as well as its strengths and limitations wherever possible, providing readers with a comprehensive understanding of its essential components. We also analyzed the performance of VLMs in various benchmark datasets. By doing so, we aim to offer a nuanced understanding of the diverse landscape of VLMs. Additionally, we underscore potential avenues for future research in this dynamic domain, anticipating further breakthroughs and advancements.", "authors": ["Akash Ghosh", "Arkadeep Acharya", "Sriparna Saha", "Vinija Jain", "Aman Chadha"], "categories": ["cs.CV", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-20", "url": "https://arxiv.org/abs/2404.07214", "pdf_url": "https://arxiv.org/pdf/2404.07214v4", "arxiv_id": "2404.07214", "doi": "10.48550/arXiv.2404.07214", "citation_count": 85, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4836}
{"id": "c2e884c616d99c0b45de7f42f49d7c491805537ba6d607e924412c2438ec1a06", "sources": ["arxiv", "semantic_scholar"], "title": "Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents", "abstract": "Large language models (LLMs) have achieved success in acting as agents, which interact with environments through tools such as search engines. However, LLMs are optimized for language generation instead of tool use during training or alignment, limiting their effectiveness as agents. To resolve this problem, previous work has first collected interaction trajectories between LLMs and environments, using only trajectories that successfully finished the task to fine-tune smaller models, making fine-tuning data scarce and acquiring it both difficult and costly. Discarding failed trajectories also leads to significant wastage of data and resources and limits the possible optimization paths during fine-tuning. In this paper, we argue that unsuccessful trajectories offer valuable insights, and LLMs can learn from these trajectories through appropriate quality control and fine-tuning strategies. By simply adding a prefix or suffix that tells the model whether to generate a successful trajectory during training, we improve model performance by a large margin on mathematical reasoning, multi-hop question answering, and strategic question answering tasks. We further analyze the inference results and find that our method provides a better trade-off between valuable information and errors in unsuccessful trajectories. To our knowledge, we are the first to demonstrate the value of negative trajectories and their application in agent-tunning scenarios. Our findings offer guidance for developing better agent-tuning methods and low-resource data usage techniques.", "authors": ["Renxi Wang", "Haonan Li", "Xudong Han", "Yixuan Zhang", "Timothy Baldwin"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-18", "url": "https://arxiv.org/abs/2402.11651", "pdf_url": "https://arxiv.org/pdf/2402.11651v2", "arxiv_id": "2402.11651", "doi": "10.48550/arXiv.2402.11651", "citation_count": 46, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.418}
{"id": "3a2b4d363f14a51266e4ed10929e95c205685227469ebebdb852655f7bbbe91d", "sources": ["arxiv", "semantic_scholar"], "title": "Fast Vocabulary Transfer for Language Model Compression", "abstract": "Real-world business applications require a trade-off between language model performance and size. We propose a new method for model compression that relies on vocabulary transfer. We evaluate the method on various vertical domains and downstream tasks. Our results indicate that vocabulary transfer can be effectively used in combination with other compression techniques, yielding a significant reduction in model size and inference time while marginally compromising on performance.", "authors": ["Leonidas Gee", "Andrea Zugarini", "Leonardo Rigutini", "Paolo Torroni"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-15", "url": "https://arxiv.org/abs/2402.09977", "pdf_url": "https://arxiv.org/pdf/2402.09977v1", "arxiv_id": "2402.09977", "doi": "10.18653/v1/2022.emnlp-industry.41", "citation_count": 50, "influential_citation_count": 10, "has_code": false, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.5207}
{"id": "09101d37ca0bf2bb7fcbd8d414984aefb5842198af722f0c99dd9890c458ac97", "sources": ["arxiv", "semantic_scholar"], "title": "ProtChatGPT: Towards Understanding Proteins with Large Language Models", "abstract": "Protein research is crucial in various fundamental disciplines, but understanding their intricate structure-function relationships remains challenging. Recent Large Language Models (LLMs) have made significant strides in comprehending task-specific knowledge, suggesting the potential for ChatGPT-like systems specialized in protein to facilitate basic research. In this work, we introduce ProtChatGPT, which aims at learning and understanding protein structures via natural languages. ProtChatGPT enables users to upload proteins, ask questions, and engage in interactive conversations to produce comprehensive answers. The system comprises protein encoders, a Protein-Language Pertaining Transformer (PLP-former), a projection adapter, and an LLM. The protein first undergoes protein encoders and PLP-former to produce protein embeddings, which are then projected by the adapter to conform with the LLM. The LLM finally combines user questions with projected embeddings to generate informative answers. Experiments show that ProtChatGPT can produce promising responses to proteins and their corresponding questions. We hope that ProtChatGPT could form the basis for further exploration and application in protein research. Code and our pre-trained model will be publicly available.", "authors": ["Chao Wang", "Hehe Fan", "Ruijie Quan", "Yi Yang"], "categories": ["cs.CE", "cs.AI", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-02-15", "url": "https://arxiv.org/abs/2402.09649", "pdf_url": "https://arxiv.org/pdf/2402.09649v2", "arxiv_id": "2402.09649", "doi": "10.48550/arXiv.2402.09649", "citation_count": 27, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3618}
{"id": "af4a70d73d6e73598363457efb55c1a5c0a988b1c5b1c542c05311e5cad1a24a", "sources": ["arxiv", "semantic_scholar"], "title": "Structured Language Generation Model: Loss Calibration and Formatted Decoding for Robust Structure Prediction and Knowledge Retrieval", "abstract": "Modern generative pre-trained language models excel at open-ended text generation, yet continue to underperform on structure-related tasks such as NER, relation extraction, and semantic role labeling, especially when compared to encoder-only models of similar sizes. While this gap has been attributed to limited structure knowledge, we hypothesize this is also due to the missing connection between the model's internal representations of linguistic structure and the output space used during supervised fine-tuning. We propose the Structured Language Generation Model (SLGM), a model- and task-agnostic framework that reformulates structured prediction as a classification problem through three components: (1) reinforced input formatting with structural cues, (2) loss design, and (3) format-aware decoding that constrains generation to task-valid outputs. Across 5 tasks and 13 datasets, SLGM substantially improves structure prediction without relying on dataset-specific engineering or additional model parameters, strengthening alignment between the model's internal structure representation and output. It outperforms baseline fine-tuning on models of the same size, achieves comparable performance to much larger models when used with <1B parameter models, and acts as a zero-weight adapter that reproduces the benefits of dataset-specific fine-tuning in low-resource settings.", "authors": ["Minho Lee", "Junghyun Min", "Yerang Kim", "Woochul Lee", "Yeonsoo Lee"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-14", "url": "https://arxiv.org/abs/2402.08971", "pdf_url": "https://arxiv.org/pdf/2402.08971v3", "arxiv_id": "2402.08971", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1193}
{"id": "0be0aad374a66a9f6877747bee25bb60c6bb085669c69dbea7c4edb89a9bdeac", "sources": ["arxiv", "semantic_scholar"], "title": "PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction", "abstract": "Compound-Protein Interaction (CPI) prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery. Existing deep learning-based methods utilize only the single modality of protein sequences or structures and lack the co-modeling of the joint distribution of the two modalities, which may lead to significant performance drops in complex real-world scenarios due to various factors, e.g., modality missing and domain shifting. More importantly, these methods only model protein sequences and structures at a single fixed scale, neglecting more fine-grained multi-scale information, such as those embedded in key protein fragments. In this paper, we propose a novel multi-scale Protein Sequence-structure Contrasting framework for CPI prediction (PSC-CPI), which captures the dependencies between protein sequences and structures through both intra-modality and cross-modality contrasting. We further apply length-variable protein augmentation to allow contrasting to be performed at different scales, from the amino acid level to the sequence level. Finally, in order to more fairly evaluate the model generalizability, we split the test data into four settings based on whether compounds and proteins have been observed during the training stage. Extensive experiments have shown that PSC-CPI generalizes well in all four settings, particularly in the more challenging ``Unseen-Both\" setting, where neither compounds nor proteins have been observed during training. Furthermore, even when encountering a situation of modality missing, i.e., inference with only single-modality protein data, PSC-CPI still exhibits comparable or even better performance than previous approaches.", "authors": ["Lirong Wu", "Yufei Huang", "Cheng Tan", "Zhangyang Gao", "Bozhen Hu", "Haitao Lin", "Zicheng Liu", "Stan Z. Li"], "categories": ["q-bio.BM", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-02-13", "url": "https://arxiv.org/abs/2402.08198", "pdf_url": "https://arxiv.org/pdf/2402.08198v1", "arxiv_id": "2402.08198", "doi": "10.48550/arXiv.2402.08198", "citation_count": 22, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.3404}
{"id": "6152b478148e883b6b67f2f7ce39b9dcf85e58a28cc4a0d08f29ba870cb5498e", "sources": ["arxiv", "semantic_scholar"], "title": "Do Membership Inference Attacks Work on Large Language Models?", "abstract": "Membership inference attacks (MIAs) attempt to predict whether a particular datapoint is a member of a target model's training data. Despite extensive research on traditional machine learning models, there has been limited work studying MIA on the pre-training data of large language models (LLMs). We perform a large-scale evaluation of MIAs over a suite of language models (LMs) trained on the Pile, ranging from 160M to 12B parameters. We find that MIAs barely outperform random guessing for most settings across varying LLM sizes and domains. Our further analyses reveal that this poor performance can be attributed to (1) the combination of a large dataset and few training iterations, and (2) an inherently fuzzy boundary between members and non-members. We identify specific settings where LLMs have been shown to be vulnerable to membership inference and show that the apparent success in such settings can be attributed to a distribution shift, such as when members and non-members are drawn from the seemingly identical domain but with different temporal ranges. We release our code and data as a unified benchmark package that includes all existing MIAs, supporting future work.", "authors": ["Michael Duan", "Anshuman Suri", "Niloofar Mireshghallah", "Sewon Min", "Weijia Shi", "Luke Zettlemoyer", "Yulia Tsvetkov", "Yejin Choi", "David Evans", "Hannaneh Hajishirzi"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-12", "url": "https://arxiv.org/abs/2402.07841", "pdf_url": "https://arxiv.org/pdf/2402.07841v2", "arxiv_id": "2402.07841", "doi": null, "citation_count": 217, "influential_citation_count": 34, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.772}
{"id": "a4d05d6d7fa04c8f551b6e7015b8894144a07e894a3d4fa58469030ed72d5354", "sources": ["arxiv", "semantic_scholar"], "title": "X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Molecular Design", "abstract": "We report a mixture of expert strategy to create fine-tuned large language models using a deep layer-wise token-level approach based on low-rank adaptation (LoRA). Starting with a set of pre-trained LoRA adapters, our gating strategy uses the hidden states to dynamically mix adapted layers, allowing the resulting X-LoRA model to draw upon different capabilities and create never-before-used deep layer-wise combinations to solve tasks. The design is inspired by the biological principles of universality and diversity, where neural network building blocks are reused in different hierarchical manifestations. Hence, the X-LoRA model can be easily implemented for any existing large language model (LLM) without a need for modifications of the underlying structure. We develop a tailored X-LoRA model that offers scientific capabilities including forward/inverse analysis tasks and enhanced reasoning capability, focused on biomaterial analysis, protein mechanics and design. The impact of this work include access to readily expandable and adaptable models with strong domain knowledge and the capability to integrate across areas of knowledge. Featuring experts in biology, mathematics, reasoning, bio-inspired materials, mechanics and materials, chemistry, protein biophysics, mechanics and quantum-mechanics based molecular properties, we conduct a series of physics-focused case studies. We examine knowledge recall, protein mechanics forward/inverse tasks, protein design, adversarial agentic modeling including ontological knowledge graph construction, as well as molecular design. The model is capable not only of making quantitative predictions of nanomechanical properties of proteins or quantum mechanical molecular properties, but also reasons over the results and correctly predicts likely mechanisms that explain distinct molecular behaviors.", "authors": ["Eric L. Buehler", "Markus J. Buehler"], "categories": ["cond-mat.soft", "cond-mat.dis-nn", "cs.AI", "cs.CL", "cs.LG", "q-bio.QM"], "fields_of_study": ["Computer Science", "Physics", "Biology"], "published_date": "2024-02-11", "url": "https://arxiv.org/abs/2402.07148", "pdf_url": "https://arxiv.org/pdf/2402.07148v2", "arxiv_id": "2402.07148", "doi": "10.48550/arXiv.2402.07148", "citation_count": 62, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "APL Machine Learning", "quality_score": 0.4498}
{"id": "8567ed94c811fb0acafc4bf47e5ad5a9ea8e1da988728f978eb076b99d1de874", "sources": ["arxiv", "semantic_scholar"], "title": "Structure-Informed Protein Language Model", "abstract": "Protein language models are a powerful tool for learning protein representations through pre-training on vast protein sequence datasets. However, traditional protein language models lack explicit structural supervision, despite its relevance to protein function. To address this issue, we introduce the integration of remote homology detection to distill structural information into protein language models without requiring explicit protein structures as input. We evaluate the impact of this structure-informed training on downstream protein function prediction tasks. Experimental results reveal consistent improvements in function annotation accuracy for EC number and GO term prediction. Performance on mutant datasets, however, varies based on the relationship between targeted properties and protein structures. This underscores the importance of considering this relationship when applying structure-aware training to protein function prediction tasks. Code and model weights are available at https://github.com/DeepGraphLearning/esm-s.", "authors": ["Zuobai Zhang", "Jiarui Lu", "Vijil Chenthamarakshan", "Aurélie Lozano", "Payel Das", "Jian Tang"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-02-07", "url": "https://arxiv.org/abs/2402.05856", "pdf_url": "https://arxiv.org/pdf/2402.05856v1", "arxiv_id": "2402.05856", "doi": "10.48550/arXiv.2402.05856", "citation_count": 15, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/DeepGraphLearning/esm-s", "venue": "arXiv.org", "quality_score": 0.301}
{"id": "1b5f3e7b743400689022dd8e7a65b34aee90f3aa1c54b82344006df5df4ef40e", "sources": ["arxiv", "semantic_scholar"], "title": "AlphaFold Meets Flow Matching for Generating Protein Ensembles", "abstract": "The biological functions of proteins often depend on dynamic structural ensembles. In this work, we develop a flow-based generative modeling approach for learning and sampling the conformational landscapes of proteins. We repurpose highly accurate single-state predictors such as AlphaFold and ESMFold and fine-tune them under a custom flow matching framework to obtain sequence-conditoned generative models of protein structure called AlphaFlow and ESMFlow. When trained and evaluated on the PDB, our method provides a superior combination of precision and diversity compared to AlphaFold with MSA subsampling. When further trained on ensembles from all-atom MD, our method accurately captures conformational flexibility, positional distributions, and higher-order ensemble observables for unseen proteins. Moreover, our method can diversify a static PDB structure with faster wall-clock convergence to certain equilibrium properties than replicate MD trajectories, demonstrating its potential as a proxy for expensive physics-based simulations. Code is available at https://github.com/bjing2016/alphaflow.", "authors": ["Bowen Jing", "Bonnie Berger", "Tommi Jaakkola"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-02-07", "url": "https://arxiv.org/abs/2402.04845", "pdf_url": "https://arxiv.org/pdf/2402.04845v2", "arxiv_id": "2402.04845", "doi": "10.48550/arXiv.2402.04845", "citation_count": 248, "influential_citation_count": 29, "has_code": true, "code_url": "https://github.com/bjing2016/alphaflow", "venue": "International Conference on Machine Learning", "quality_score": 0.7386}
{"id": "b81cd57a435f3ce758b52e1895bf8bf730a90b96d5d694c2442416da4e683c36", "sources": ["arxiv", "semantic_scholar"], "title": "Learning immune receptor representations with protein language models", "abstract": "Protein language models (PLMs) learn contextual representations from protein sequences and are profoundly impacting various scientific disciplines spanning protein design, drug discovery, and structural predictions. One particular research area where PLMs have gained considerable attention is adaptive immune receptors, whose tremendous sequence diversity dictates the functional recognition of the adaptive immune system. The self-supervised nature underlying the training of PLMs has been recently leveraged to implement a variety of immune receptor-specific PLMs. These models have demonstrated promise in tasks such as predicting antigen-specificity and structure, computationally engineering therapeutic antibodies, and diagnostics. However, challenges including insufficient training data and considerations related to model architecture, training strategies, and data and model availability must be addressed before fully unlocking the potential of PLMs in understanding, translating, and engineering immune receptors.", "authors": ["Andreas Dounas", "Tudor-Stefan Cotet", "Alexander Yermanos"], "categories": ["q-bio.QM"], "fields_of_study": ["Biology"], "published_date": "2024-02-06", "url": "https://arxiv.org/abs/2402.03823", "pdf_url": "https://arxiv.org/pdf/2402.03823v1", "arxiv_id": "2402.03823", "doi": null, "citation_count": 6, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2113}
{"id": "fb085fe9398a841835cd9372771b9700e807793afcd55db9c85ad2ce34045fd2", "sources": ["arxiv", "semantic_scholar"], "title": "Detecting Mode Collapse in Language Models via Narration", "abstract": "No two authors write alike. Personal flourishes invoked in written narratives, from lexicon to rhetorical devices, imply a particular author--what literary theorists label the implied or virtual author; distinct from the real author or narrator of a text. Early large language models trained on unfiltered training sets drawn from a variety of discordant sources yielded incoherent personalities, problematic for conversational tasks but proving useful for sampling literature from multiple perspectives. Successes in alignment research in recent years have allowed researchers to impose subjectively consistent personae on language models via instruction tuning and reinforcement learning from human feedback (RLHF), but whether aligned models retain the ability to model an arbitrary virtual author has received little scrutiny. By studying 4,374 stories sampled from three OpenAI language models, we show successive versions of GPT-3 suffer from increasing degrees of \"mode collapse\" whereby overfitting the model during alignment constrains it from generalizing over authorship: models suffering from mode collapse become unable to assume a multiplicity of perspectives. Our method and results are significant for researchers seeking to employ language models in sociological simulations.", "authors": ["Sil Hamilton"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2024-02-06", "url": "https://arxiv.org/abs/2402.04477", "pdf_url": "https://arxiv.org/pdf/2402.04477v1", "arxiv_id": "2402.04477", "doi": "10.48550/arXiv.2402.04477", "citation_count": 23, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "https://aclanthology.org/2024.scalellm-1.5/", "quality_score": 0.3451}
{"id": "fd4cfe0bd527b1bd43e1d6ea9570458bf2d506069cc816171da590de9bfcc101", "sources": ["arxiv", "semantic_scholar"], "title": "idMotif: An Interactive Motif Identification in Protein Sequences", "abstract": "This article introduces idMotif, a visual analytics framework designed to aid domain experts in the identification of motifs within protein sequences. Motifs, short sequences of amino acids, are critical for understanding the distinct functions of proteins. Identifying these motifs is pivotal for predicting diseases or infections. idMotif employs a deep learning-based method for the categorization of protein sequences, enabling the discovery of potential motif candidates within protein groups through local explanations of deep learning model decisions. It offers multiple interactive views for the analysis of protein clusters or groups and their sequences. A case study, complemented by expert feedback, illustrates idMotif's utility in facilitating the analysis and identification of protein sequences and motifs.", "authors": ["Ji Hwan Park", "Vikash Prasad", "Sydney Newsom", "Fares Najar", "Rakhi Rajan"], "categories": ["q-bio.QM", "cs.GR", "cs.HC", "cs.LG"], "fields_of_study": ["Computer Science", "Medicine", "Biology"], "published_date": "2024-02-04", "url": "https://arxiv.org/abs/2402.05953", "pdf_url": "https://arxiv.org/pdf/2402.05953v1", "arxiv_id": "2402.05953", "doi": "10.1109/MCG.2023.3345742", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE Computer Graphics and Applications", "quality_score": 0.0753}
{"id": "2f8b82da1e82d858fbc26124887f99cefef76b9f162500cdc450cc0476227f11", "sources": ["arxiv", "semantic_scholar"], "title": "Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning", "abstract": "Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Recently, due to their capacity and representation ability, pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without experimental data. However, their predictions are limited in accuracy as well as interpretability. Furthermore, such deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity. By combining the techniques of meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. The experiments across 87 deep mutational scanning datasets underscore its superiority over both unsupervised and supervised approaches, revealing its potential in facilitating AI-guided protein design.", "authors": ["Ziyi Zhou", "Liang Zhang", "Yuanxi Yu", "Mingchen Li", "Liang Hong", "Pan Tan"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2024-02-03", "url": "https://arxiv.org/abs/2402.02004", "pdf_url": "https://arxiv.org/pdf/2402.02004v1", "arxiv_id": "2402.02004", "doi": null, "citation_count": 29, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3693}
{"id": "3d9e936cd078517bd1fe6d4648569f3659b30448339c583e849328caced87184", "sources": ["arxiv", "semantic_scholar"], "title": "ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning", "abstract": "Designing de novo proteins beyond those found in nature holds significant promise for advancements in both scientific and engineering applications. Current methodologies for protein design often rely on AI-based models, such as surrogate models that address end-to-end problems by linking protein structure to material properties or vice versa. However, these models frequently focus on specific material objectives or structural properties, limiting their flexibility when incorporating out-of-domain knowledge into the design process or comprehensive data analysis is required. In this study, we introduce ProtAgents, a platform for de novo protein design based on Large Language Models (LLMs), where multiple AI agents with distinct capabilities collaboratively address complex tasks within a dynamic environment. The versatility in agent development allows for expertise in diverse domains, including knowledge retrieval, protein structure analysis, physics-based simulations, and results analysis. The dynamic collaboration between agents, empowered by LLMs, provides a versatile approach to tackling protein design and analysis problems, as demonstrated through diverse examples in this study. The problems of interest encompass designing new proteins, analyzing protein structures and obtaining new first-principles data -- natural vibrational frequencies -- via physics simulations. The concerted effort of the system allows for powerful automated and synergistic design of de novo proteins with targeted mechanical properties. The flexibility in designing the agents, on one hand, and their capacity in autonomous collaboration through the dynamic LLM-based multi-agent environment on the other hand, unleashes great potentials of LLMs in addressing multi-objective materials problems and opens up new avenues for autonomous materials discovery and design.", "authors": ["A. Ghafarollahi", "M. J. Buehler"], "categories": ["cond-mat.soft", "cs.AI", "cs.CL", "q-bio.BM"], "fields_of_study": ["Physics", "Computer Science", "Biology", "Medicine"], "published_date": "2024-01-27", "url": "https://arxiv.org/abs/2402.04268", "pdf_url": "https://arxiv.org/pdf/2402.04268v1", "arxiv_id": "2402.04268", "doi": "10.48550/arXiv.2402.04268", "citation_count": 95, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "Digital Discovery", "quality_score": 0.4956}
{"id": "c918f07b1044ab6165a8297f19832449868e3fdceabfe75befb2e433ed4ed2ec", "sources": ["arxiv", "semantic_scholar"], "title": "Endowing Protein Language Models with Structural Knowledge", "abstract": "Understanding the relationships between protein sequence, structure and function is a long-standing biological challenge with manifold implications from drug design to our understanding of evolution. Recently, protein language models have emerged as the preferred method for this challenge, thanks to their ability to harness large sequence databases. Yet, their reliance on expansive sequence data and parameter sets limits their flexibility and practicality in real-world scenarios. Concurrently, the recent surge in computationally predicted protein structures unlocks new opportunities in protein representation learning. While promising, the computational burden carried by such complex data still hinders widely-adopted practical applications. To address these limitations, we introduce a novel framework that enhances protein language models by integrating protein structural data. Drawing from recent advances in graph transformers, our approach refines the self-attention mechanisms of pretrained language transformers by integrating structural information with structure extractor modules. This refined model, termed Protein Structure Transformer (PST), is further pretrained on a small protein structure database, using the same masked language modeling objective as traditional protein language models. Empirical evaluations of PST demonstrate its superior parameter efficiency relative to protein language models, despite being pretrained on a dataset comprising only 542K structures. Notably, PST consistently outperforms the state-of-the-art foundation model for protein sequences, ESM-2, setting a new benchmark in protein function prediction. Our findings underscore the potential of integrating structural information into protein language models, paving the way for more effective and efficient protein modeling Code and pretrained models are available at https://github.com/BorgwardtLab/PST.", "authors": ["Dexiong Chen", "Philip Hartout", "Paolo Pellizzoni", "Carlos Oliver", "Karsten Borgwardt"], "categories": ["q-bio.QM", "cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2024-01-26", "url": "https://arxiv.org/abs/2401.14819", "pdf_url": "https://arxiv.org/pdf/2401.14819v1", "arxiv_id": "2401.14819", "doi": "10.48550/arXiv.2401.14819", "citation_count": 22, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/BorgwardtLab/PST", "venue": "arXiv.org", "quality_score": 0.3404}
{"id": "430fe979c85df95218bd9cc2f622d91b779bb0843c78d836fab1ec81156f253e", "sources": ["arxiv", "semantic_scholar"], "title": "Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap", "abstract": "Large language models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artificial general intelligence. The interplay between LLMs and evolutionary algorithms (EAs), despite differing in objectives and methodologies, share a common pursuit of applicability in complex problems. Meanwhile, EA can provide an optimization framework for LLM's further enhancement under black-box settings, empowering LLM with flexible global search capacities. On the other hand, the abundant domain knowledge inherent in LLMs could enable EA to conduct more intelligent searches. Furthermore, the text processing and generative capabilities of LLMs would aid in deploying EAs across a wide range of tasks. Based on these complementary advantages, this paper provides a thorough review and a forward-looking roadmap, categorizing the reciprocal inspiration into two main avenues: LLM-enhanced EA and EA-enhanced LLM. Some integrated synergy methods are further introduced to exemplify the complementarity between LLMs and EAs in diverse scenarios, including code generation, software engineering, neural architecture search, and various generation tasks. As the first comprehensive review focused on the EA research in the era of LLMs, this paper provides a foundational stepping stone for understanding the collaborative potential of LLMs and EAs. The identified challenges and future directions offer guidance for researchers and practitioners to unlock the full potential of this innovative collaboration in propelling advancements in optimization and artificial intelligence. We have created a GitHub repository to index the relevant papers: https://github.com/wuxingyu-ai/LLM4EC.", "authors": ["Xingyu Wu", "Sheng-hao Wu", "Jibin Wu", "Liang Feng", "Kay Chen Tan"], "categories": ["cs.NE", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2024-01-18", "url": "https://arxiv.org/abs/2401.10034", "pdf_url": "https://arxiv.org/pdf/2401.10034v3", "arxiv_id": "2401.10034", "doi": "10.1109/TEVC.2024.3506731", "citation_count": 181, "influential_citation_count": 4, "has_code": true, "code_url": "https://github.com/wuxingyu-ai/LLM4EC", "venue": "IEEE Transactions on Evolutionary Computation", "quality_score": 0.565}
{"id": "41713d942bb1746a0ee489db986fbd387a9cc8313e22da375315c07efebfd623", "sources": ["arxiv", "semantic_scholar"], "title": "Part-of-Speech Tagger for Bodo Language using Deep Learning approach", "abstract": "Language Processing systems such as Part-of-speech tagging, Named entity recognition, Machine translation, Speech recognition, and Language modeling (LM) are well-studied in high-resource languages. Nevertheless, research on these systems for several low-resource languages, including Bodo, Mizo, Nagamese, and others, is either yet to commence or is in its nascent stages. Language model plays a vital role in the downstream tasks of modern NLP. Extensive studies are carried out on LMs for high-resource languages. Nevertheless, languages such as Bodo, Rabha, and Mising continue to lack coverage. In this study, we first present BodoBERT, a language model for the Bodo language. To the best of our knowledge, this work is the first such effort to develop a language model for Bodo. Secondly, we present an ensemble DL-based POS tagging model for Bodo. The POS tagging model is based on combinations of BiLSTM with CRF and stacked embedding of BodoBERT with BytePairEmbeddings. We cover several language models in the experiment to see how well they work in POS tagging tasks. The best-performing model achieves an F1 score of 0.8041. A comparative experiment was also conducted on Assamese POS taggers, considering that the language is spoken in the same region as Bodo.", "authors": ["Dhrubajyoti Pathak", "Sanjib Narzary", "Sukumar Nandi", "Bidisha Som"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2024-01-06", "url": "https://arxiv.org/abs/2401.03175", "pdf_url": "https://arxiv.org/pdf/2401.03175v1", "arxiv_id": "2401.03175", "doi": "10.1017/nlp.2024.15", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Natural Language Processing", "quality_score": 0.2258}
{"id": "2fa521d10b2118188e460da13c2744ad40d4f1140016c5b8f2c3fe00778a9c71", "sources": ["arxiv", "semantic_scholar"], "title": "ACP-ESM: A novel framework for classification of anticancer peptides using protein-oriented transformer approach", "abstract": "Anticancer peptides (ACPs) are a class of molecules that have gained significant attention in the field of cancer research and therapy. ACPs are short chains of amino acids, the building blocks of proteins, and they possess the ability to selectively target and kill cancer cells. One of the key advantages of ACPs is their ability to selectively target cancer cells while sparing healthy cells to a greater extent. This selectivity is often attributed to differences in the surface properties of cancer cells compared to normal cells. That is why ACPs are being investigated as potential candidates for cancer therapy. ACPs may be used alone or in combination with other treatment modalities like chemotherapy and radiation therapy. While ACPs hold promise as a novel approach to cancer treatment, there are challenges to overcome, including optimizing their stability, improving selectivity, and enhancing their delivery to cancer cells, continuous increasing in number of peptide sequences, developing a reliable and precise prediction model. In this work, we propose an efficient transformer-based framework to identify anticancer peptides for by performing accurate a reliable and precise prediction model. For this purpose, four different transformer models, namely ESM, ProtBert, BioBERT, and SciBERT are employed to detect anticancer peptides from amino acid sequences. To demonstrate the contribution of the proposed framework, extensive experiments are carried on widely-used datasets in the literature, two versions of AntiCp2, cACP-DeepGram, ACP-740. Experiment results show the usage of proposed model enhances classification accuracy when compared to the state-of-the-art studies. The proposed framework, ESM, exhibits 96.45 of accuracy for AntiCp2 dataset, 97.66 of accuracy for cACP-DeepGram dataset, and 88.51 of accuracy for ACP-740 dataset, thence determining new state-of-the-art.", "authors": ["Zeynep Hilal Kilimci", "Mustafa Yalcin"], "categories": ["q-bio.BM", "cs.AI", "cs.CE", "cs.LG"], "fields_of_study": ["Medicine", "Computer Science", "Biology"], "published_date": "2024-01-04", "url": "https://arxiv.org/abs/2401.02124", "pdf_url": "https://arxiv.org/pdf/2401.02124v1", "arxiv_id": "2401.02124", "doi": "10.48550/arXiv.2401.02124", "citation_count": 14, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.294}
{"id": "157542eac9f9fb89d45719a0a43aec218affed145b09d578667be3383fcba9ef", "sources": ["arxiv", "semantic_scholar"], "title": "Identification of Knowledge Neurons in Protein Language Models", "abstract": "Neural language models have become powerful tools for learning complex representations of entities in natural language processing tasks. However, their interpretability remains a significant challenge, particularly in domains like computational biology where trust in model predictions is crucial. In this work, we aim to enhance the interpretability of protein language models, specifically the state-of-the-art ESM model, by identifying and characterizing knowledge neurons - components that express understanding of key information. After fine-tuning the ESM model for the task of enzyme sequence classification, we compare two knowledge neuron selection methods that preserve a subset of neurons from the original model. The two methods, activation-based and integrated gradient-based selection, consistently outperform a random baseline. In particular, these methods show that there is a high density of knowledge neurons in the key vector prediction networks of self-attention modules. Given that key vectors specialize in understanding different features of input sequences, these knowledge neurons could capture knowledge of different enzyme sequence motifs. In the future, the types of knowledge captured by each neuron could be characterized.", "authors": ["Divya Nori", "Shivali Singireddy", "Marina Ten Have"], "categories": ["cs.LG", "cs.AI", "cs.CL", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-12-17", "url": "https://arxiv.org/abs/2312.10770", "pdf_url": "https://arxiv.org/pdf/2312.10770v1", "arxiv_id": "2312.10770", "doi": "10.48550/arXiv.2312.10770", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "fad3b06eab1c317aa9fda2813083959535c503618961a660e23072540982672d", "sources": ["arxiv", "semantic_scholar"], "title": "Demystifying Instruction Mixing for Fine-tuning Large Language Models", "abstract": "Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks. However, the procedure to optimizing the mixing of instruction datasets for LLM fine-tuning is still poorly understood. This study categorizes instructions into three primary types: NLP downstream tasks, coding, and general chat. We explore the effects of instruction tuning on different combinations of datasets on LLM performance, and find that certain instruction types are more advantageous for specific applications but can negatively impact other areas. This work provides insights into instruction mixtures, laying the foundations for future research.", "authors": ["Renxi Wang", "Haonan Li", "Minghao Wu", "Yuxia Wang", "Xudong Han", "Chiyu Zhang", "Timothy Baldwin"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-17", "url": "https://arxiv.org/abs/2312.10793", "pdf_url": "https://arxiv.org/pdf/2312.10793v3", "arxiv_id": "2312.10793", "doi": "10.18653/v1/2024.acl-srw.15", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.2386}
{"id": "9438d738250c9b7e92a49cf30c6731c4c24d0dc520f3a0d6c8957ac9a4b41d70", "sources": ["arxiv", "semantic_scholar"], "title": "Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models", "abstract": "The age of social media is rife with memes. Understanding and detecting harmful memes pose a significant challenge due to their implicit meaning that is not explicitly conveyed through the surface text and image. However, existing harmful meme detection approaches only recognize superficial harm-indicative signals in an end-to-end classification manner but ignore in-depth cognition of the meme text and image. In this paper, we attempt to detect harmful memes based on advanced reasoning over the interplay of multimodal information in memes. Inspired by the success of Large Language Models (LLMs) on complex reasoning, we first conduct abductive reasoning with LLMs. Then we propose a novel generative framework to learn reasonable thoughts from LLMs for better multimodal fusion and lightweight fine-tuning, which consists of two training stages: 1) Distill multimodal reasoning knowledge from LLMs; and 2) Fine-tune the generative framework to infer harmfulness. Extensive experiments conducted on three meme datasets demonstrate that our proposed approach achieves superior performance than state-of-the-art methods on the harmful meme detection task.", "authors": ["Hongzhan Lin", "Ziyang Luo", "Jing Ma", "Long Chen"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-09", "url": "https://arxiv.org/abs/2312.05434", "pdf_url": "https://arxiv.org/pdf/2312.05434v1", "arxiv_id": "2312.05434", "doi": "10.18653/v1/2023.findings-emnlp.611", "citation_count": 27, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.3618}
{"id": "830d7d6cce586c2b9f8f0a9cf11b2d2e937491630cdad176be024e936edc6f37", "sources": ["arxiv", "semantic_scholar"], "title": "Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models", "abstract": "Predicting protein stability changes induced by single-point mutations has been a persistent challenge over the years, attracting immense interest from numerous researchers. The ability to precisely predict protein thermostability is pivotal for various subfields and applications in biochemistry, including drug development, protein evolution analysis, and enzyme synthesis. Despite the proposition of multiple methodologies aimed at addressing this issue, few approaches have successfully achieved optimal performance coupled with high computational efficiency. Two principal hurdles contribute to the existing challenges in this domain. The first is the complexity of extracting and aggregating sufficiently representative features from proteins. The second refers to the limited availability of experimental data for protein mutation analysis, further complicating the comprehensive evaluation of model performance on unseen data samples. With the advent of Large Language Models(LLM), such as the ESM models in protein research, profound interpretation of protein features is now accessibly aided by enormous training data. Therefore, LLMs are indeed to facilitate a wide range of protein research. In our study, we introduce an ESM-assisted efficient approach that integrates protein sequence and structural features to predict the thermostability changes in protein upon single-point mutations. Furthermore, we have curated a dataset meticulously designed to preclude data leakage, corresponding to two extensively employed test datasets, to facilitate a more equitable model comparison.", "authors": ["Yijie Zhang", "Zhangyang Gao", "Cheng Tan", "Stan Z. Li"], "categories": ["q-bio.BM", "cs.AI"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2023-12-07", "url": "https://arxiv.org/abs/2312.04019", "pdf_url": "https://arxiv.org/pdf/2312.04019v1", "arxiv_id": "2312.04019", "doi": "10.48550/arXiv.2312.04019", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "4f084ea725e9aaa737730044e57b254527a6592d2a675a7049cd6043006f7198", "sources": ["arxiv", "semantic_scholar"], "title": "Using a Large Language Model to generate a Design Structure Matrix", "abstract": "The Design Structure Matrix (DSM) is an established method used in dependency modelling, especially in the design of complex engineering systems. The generation of DSM is traditionally carried out through manual means and can involve interviewing experts to elicit critical system elements and the relationships between them. Such manual approaches can be time-consuming and costly. This paper presents a workflow that uses a Large Language Model (LLM) to support the generation of DSM and improve productivity. A prototype of the workflow was developed in this work and applied on a diesel engine DSM published previously. It was found that the prototype could reproduce 357 out of 462 DSM entries published (i.e. 77.3%), suggesting that the work can aid DSM generation. A no-code version of the prototype is made available online to support future research.", "authors": ["Edwin C. Y. Koh"], "categories": ["cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-12-07", "url": "https://arxiv.org/abs/2312.04134", "pdf_url": "https://arxiv.org/pdf/2312.04134v1", "arxiv_id": "2312.04134", "doi": "10.1016/j.nlp.2024.100103", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Natural Language Processing Journal", "quality_score": 0.1945}
{"id": "2792a97a581ecad2d219f85cfb3cc2d13788ec28cf60f8b9723d84f3937bd38a", "sources": ["arxiv", "semantic_scholar"], "title": "Protein Language Model-Powered 3D Ligand Binding Site Prediction from Protein Sequence", "abstract": "Prediction of ligand binding sites of proteins is a fundamental and important task for understanding the function of proteins and screening potential drugs. Most existing methods require experimentally determined protein holo-structures as input. However, such structures can be unavailable on novel or less-studied proteins. To tackle this limitation, we propose LaMPSite, which only takes protein sequences and ligand molecular graphs as input for ligand binding site predictions. The protein sequences are used to retrieve residue-level embeddings and contact maps from the pre-trained ESM-2 protein language model. The ligand molecular graphs are fed into a graph neural network to compute atom-level embeddings. Then we compute and update the protein-ligand interaction embedding based on the protein residue-level embeddings and ligand atom-level embeddings, and the geometric constraints in the inferred protein contact map and ligand distance map. A final pooling on protein-ligand interaction embedding would indicate which residues belong to the binding sites. Without any 3D coordinate information of proteins, our proposed model achieves competitive performance compared to baseline methods that require 3D protein structures when predicting binding sites. Given that less than 50% of proteins have reliable structure information in the current stage, LaMPSite will provide new opportunities for drug discovery.", "authors": ["Shuo Zhang", "Lei Xie"], "categories": ["q-bio.QM", "cs.CL", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2023-12-05", "url": "https://arxiv.org/abs/2312.03016", "pdf_url": "https://arxiv.org/pdf/2312.03016v1", "arxiv_id": "2312.03016", "doi": "10.48550/arXiv.2312.03016", "citation_count": 10, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2603}
{"id": "93ba3aa005e82c440388e61de11cadb1e28c33bad7c9da202fe42cb094499353", "sources": ["arxiv", "semantic_scholar"], "title": "ESM-NBR: fast and accurate nucleic acid-binding residue prediction via protein language model feature representation and multi-task learning", "abstract": "Protein-nucleic acid interactions play a very important role in a variety of biological activities. Accurate identification of nucleic acid-binding residues is a critical step in understanding the interaction mechanisms. Although many computationally based methods have been developed to predict nucleic acid-binding residues, challenges remain. In this study, a fast and accurate sequence-based method, called ESM-NBR, is proposed. In ESM-NBR, we first use the large protein language model ESM2 to extract discriminative biological properties feature representation from protein primary sequences; then, a multi-task deep learning model composed of stacked bidirectional long short-term memory (BiLSTM) and multi-layer perceptron (MLP) networks is employed to explore common and private information of DNA- and RNA-binding residues with ESM2 feature as input. Experimental results on benchmark data sets demonstrate that the prediction performance of ESM2 feature representation comprehensively outperforms evolutionary information-based hidden Markov model (HMM) features. Meanwhile, the ESM-NBR obtains the MCC values for DNA-binding residues prediction of 0.427 and 0.391 on two independent test sets, which are 18.61 and 10.45% higher than those of the second-best methods, respectively. Moreover, by completely discarding the time-cost multiple sequence alignment process, the prediction speed of ESM-NBR far exceeds that of existing methods (5.52s for a protein sequence of length 500, which is about 16 times faster than the second-fastest method). A user-friendly standalone package and the data of ESM-NBR are freely available for academic use at: https://github.com/wwzll123/ESM-NBR.", "authors": ["Wenwu Zeng", "Dafeng Lv", "Wenjuan Liu", "Shaoliang Peng"], "categories": ["q-bio.QM", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2023-12-01", "url": "https://arxiv.org/abs/2312.00842", "pdf_url": "https://arxiv.org/pdf/2312.00842v1", "arxiv_id": "2312.00842", "doi": "10.1109/BIBM58861.2023.10385509", "citation_count": 8, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/wwzll123/ESM-NBR", "venue": "IEEE International Conference on Bioinformatics and Biomedicine", "quality_score": 0.2386}
{"id": "b41de1ba79d23211754acef5ceee8a3cc8ba11133ac1e9a857834fdafda2d3eb", "sources": ["arxiv", "semantic_scholar"], "title": "A perspective on protein structure prediction using quantum computers", "abstract": "Despite the recent advancements by deep learning methods such as AlphaFold2, \\textit{in silico} protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage, and estimating quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.", "authors": ["Hakan Doga", "Bryan Raubenolt", "Fabio Cumbo", "Jayadev Joshi", "Frank P. DiFilippo", "Jun Qin", "Daniel Blankenberg", "Omar Shehab"], "categories": ["quant-ph"], "fields_of_study": ["Physics", "Medicine"], "published_date": "2023-12-01", "url": "https://arxiv.org/abs/2312.00875", "pdf_url": "https://arxiv.org/pdf/2312.00875v1", "arxiv_id": "2312.00875", "doi": "10.1021/acs.jctc.4c00067", "citation_count": 50, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Journal of Chemical Theory and Computation", "quality_score": 0.4269}
{"id": "ecc7d6bb5abf282df6a48a895466f03eacf25aaea8fe124a09eb8067e9ad9af1", "sources": ["arxiv", "semantic_scholar"], "title": "Acoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities", "abstract": "The auditory system plays a substantial role in shaping the overall human perceptual experience. While prevailing large language models (LLMs) and visual language models (VLMs) have shown their promise in solving a wide variety of language and vision understanding tasks, only a few of them can be generalised to the audio domain without compromising their domain-specific capability. In this work, we introduce Acoustic Prompt Tuning (APT), a new adapter extending LLMs and VLMs to the audio domain by injecting audio embeddings to the input of LLMs, namely soft prompting. Specifically, APT applies an instruction-aware audio aligner to generate soft prompts, conditioned on both input text and sounds, as the inputs to the language model. To mitigate data scarcity in the audio domain, a curriculum learning strategy is proposed by formulating diverse audio tasks in a sequential manner. Moreover, we improve the audio language model by using interleaved audio-text embeddings as the input sequence. In this improved model, zero constraints are imposed on the input format, thus it is capable of tackling diverse modelling tasks, such as few-shot audio classification and audio comparison. To further evaluate the advanced ability of the audio networks, we introduce natural language audio reasoning (NLAR), a new task that analyses two audio clips by comparison and summarisation. Experiments show that APT-enhanced LLMs (namely APT-LLMs) achieve competitive results compared to the expert models (i.e., the networks trained on the target datasets) across various tasks. We finally demonstrate APT's ability in extending frozen VLMs to the audio domain without fine-tuning, achieving promising results in audio-visual question and answering. Our code and model weights will be released at https://github.com/JinhuaLiang/APT", "authors": ["Jinhua Liang", "Xubo Liu", "Wenwu Wang", "Mark D. Plumbley", "Huy Phan", "Emmanouil Benetos"], "categories": ["eess.AS"], "fields_of_study": ["Engineering"], "published_date": "2023-11-30", "url": "https://arxiv.org/abs/2312.00249", "pdf_url": "https://arxiv.org/pdf/2312.00249v2", "arxiv_id": "2312.00249", "doi": "10.1109/TASLPRO.2025.3533375", "citation_count": 24, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/JinhuaLiang/APT", "venue": "IEEE Transactions on Audio, Speech, and Language Processing", "quality_score": 0.3495}
{"id": "f44525bd159e0fb1c03ffe0c5b1d2d856e535635fb152a020c8a95121b5e301f", "sources": ["arxiv", "semantic_scholar"], "title": "When a Language Question Is at Stake. A Revisited Approach to Label Sensitive Content", "abstract": "Many under-resourced languages require high-quality datasets for specific tasks such as offensive language detection, disinformation, or misinformation identification. However, the intricacies of the content may have a detrimental effect on the annotators. The article aims to revisit an approach of pseudo-labeling sensitive data on the example of Ukrainian tweets covering the Russian-Ukrainian war. Nowadays, this acute topic is in the spotlight of various language manipulations that cause numerous disinformation and profanity on social media platforms. The conducted experiment highlights three main stages of data annotation and underlines the main obstacles during machine annotation. Ultimately, we provide a fundamental statistical analysis of the obtained data, evaluation of models used for pseudo-labelling, and set further guidelines on how the scientists can leverage the corpus to execute more advanced research and extend the existing data samples without annotators' engagement.", "authors": ["Stetsenko Daria"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-11-17", "url": "https://arxiv.org/abs/2311.10514", "pdf_url": "https://arxiv.org/pdf/2311.10514v1", "arxiv_id": "2311.10514", "doi": "10.48550/arXiv.2311.10514", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "cd67b4d29cbff9423c4daa1e0bd25558dcb9305aa7c799bd807e62a6ac03bc79", "sources": ["arxiv", "semantic_scholar"], "title": "Efficiently Adapting Pretrained Language Models To New Languages", "abstract": "Recent large language models (LLM) exhibit sub-optimal performance on low-resource languages, as the training data of these models is usually dominated by English and other high-resource languages. Furthermore, it is challenging to train models for low-resource languages, especially from scratch, due to a lack of high quality training data. Adapting pretrained LLMs reduces the need for data in the new language while also providing cross lingual transfer capabilities. However, naively adapting to new languages leads to catastrophic forgetting and poor tokenizer efficiency. In this work, we study how to efficiently adapt any existing pretrained LLM to a new language without running into these issues. In particular, we improve the encoding efficiency of the tokenizer by adding new tokens from the target language and study the data mixing recipe to mitigate forgetting. Our experiments on adapting an English LLM to Hungarian and Thai show that our recipe can reach better performance than open source models on the target language, with minimal regressions on English.", "authors": ["Zoltan Csaki", "Pian Pawakapan", "Urmish Thakker", "Qiantong Xu"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-11-09", "url": "https://arxiv.org/abs/2311.05741", "pdf_url": "https://arxiv.org/pdf/2311.05741v2", "arxiv_id": "2311.05741", "doi": "10.48550/arXiv.2311.05741", "citation_count": 33, "influential_citation_count": 4, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3829}
{"id": "9e8ccae539771af72541e502e79594757be3df8ab5317589d61ca0c7f813d676", "sources": ["arxiv", "semantic_scholar"], "title": "Impact of the Ce $4f$ states in the electronic structure of the intermediate-valence superconductor CeIr$_3$", "abstract": "The electronic structure of the $f$-based superconductor $\\mathrm{CeIr_3}$ was studied by photoelectron spectroscopy. The energy distribution of the $\\mathrm{Ce}~4f$ states were revealed by the $\\mathrm{Ce}~3d-4f$ resonant photoelectron spectroscopy. The $\\mathrm{Ce}~4f$ states were mostly distributed in the vicinity of the Fermi energy, suggesting the itinerant character of the $\\mathrm{Ce}~4f$ states. The contribution of the $\\mathrm{Ce}~4f$ states to the density of states (DOS) at the Fermi energy was estimated to be nearly half of that of the $\\mathrm{Ir}~5d$ states, implying that the $\\mathrm{Ce}~4f$ states have a considerable contribution to the DOS at the Fermi energy. The $\\mathrm{Ce}~3d$ core-level and $\\mathrm{Ce}~3d$ X-ray absorption spectra were analyzed based on a single-impurity Anderson model. The number of the $\\mathrm{Ce}~4f$ states in the ground state was estimated to be $0.8-0.9$, which is much larger than the values obtained in the previous studies (i.e., $0-0.4$).", "authors": ["Shin-ichi Fujimori", "Ikuto Kawasaki", "Yukiharu Takeda", "Hiroshi Yamagami", "Norimasa Sasabe", "Yoshiki J. Sato", "Ai Nakamura", "Yusei Shimizu", "Arvind Maurya", "Yoshiya Homma", "Dexin Li", "Fuminori Honda", "Dai Aoki"], "categories": ["cond-mat.str-el", "cond-mat.supr-con"], "fields_of_study": ["Physics"], "published_date": "2023-11-07", "url": "https://arxiv.org/abs/2311.03640", "pdf_url": "https://arxiv.org/pdf/2311.03640v1", "arxiv_id": "2311.03640", "doi": "10.1088/2516-1075/ad0a3d", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Electronic Structure", "quality_score": 0.0}
{"id": "202b5db5492de11698d8518a7534bc87276f14ec3f24b48215395d1c9edb48ce", "sources": ["arxiv", "semantic_scholar"], "title": "Can Language Models Be Tricked by Language Illusions? Easier with Syntax, Harder with Semantics", "abstract": "Language models (LMs) have been argued to overlap substantially with human beings in grammaticality judgment tasks. But when humans systematically make errors in language processing, should we expect LMs to behave like cognitive models of language and mimic human behavior? We answer this question by investigating LMs' more subtle judgments associated with \"language illusions\" -- sentences that are vague in meaning, implausible, or ungrammatical but receive unexpectedly high acceptability judgments by humans. We looked at three illusions: the comparative illusion (e.g. \"More people have been to Russia than I have\"), the depth-charge illusion (e.g. \"No head injury is too trivial to be ignored\"), and the negative polarity item (NPI) illusion (e.g. \"The hunter who no villager believed to be trustworthy will ever shoot a bear\"). We found that probabilities represented by LMs were more likely to align with human judgments of being \"tricked\" by the NPI illusion which examines a structural dependency, compared to the comparative and the depth-charge illusions which require sophisticated semantic understanding. No single LM or metric yielded results that are entirely consistent with human behavior. Ultimately, we show that LMs are limited both in their construal as cognitive models of human language processing and in their capacity to recognize nuanced but critical information in complicated language materials.", "authors": ["Yuhan Zhang", "Edward Gibson", "Forrest Davis"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-11-02", "url": "https://arxiv.org/abs/2311.01386", "pdf_url": "https://arxiv.org/pdf/2311.01386v2", "arxiv_id": "2311.01386", "doi": "10.18653/v1/2023.conll-1.1", "citation_count": 8, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Conference on Computational Natural Language Learning", "quality_score": 0.2386}
{"id": "e2fbc9168d24e9c011e6ed8f23d41ac3dc7fe947b022b22ba95e7d317447bfa8", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring Post-Training Quantization of Protein Language Models", "abstract": "Recent advancements in unsupervised protein language models (ProteinLMs), like ESM-1b and ESM-2, have shown promise in different protein prediction tasks. However, these models face challenges due to their high computational demands, significant memory needs, and latency, restricting their usage on devices with limited resources. To tackle this, we explore post-training quantization (PTQ) for ProteinLMs, focusing on ESMFold, a simplified version of AlphaFold based on ESM-2 ProteinLM. Our study is the first attempt to quantize all weights and activations of ProteinLMs. We observed that the typical uniform quantization method performs poorly on ESMFold, causing a significant drop in TM-Score when using 8-bit quantization. We conducted extensive quantization experiments, uncovering unique challenges associated with ESMFold, particularly highly asymmetric activation ranges before Layer Normalization, making representation difficult using low-bit fixed-point formats. To address these challenges, we propose a new PTQ method for ProteinLMs, utilizing piecewise linear quantization for asymmetric activation values to ensure accurate approximation. We demonstrated the effectiveness of our method in protein structure prediction tasks, demonstrating that ESMFold can be accurately quantized to low-bit widths without compromising accuracy. Additionally, we applied our method to the contact prediction task, showcasing its versatility. In summary, our study introduces an innovative PTQ method for ProteinLMs, addressing specific quantization challenges and potentially leading to the development of more efficient ProteinLMs with significant implications for various protein-related applications.", "authors": ["Shuang Peng", "Fei Yang", "Ning Sun", "Sheng Chen", "Yanfeng Jiang", "Aimin Pan"], "categories": ["cs.LG", "cs.AI", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-10-30", "url": "https://arxiv.org/abs/2310.19624", "pdf_url": "https://arxiv.org/pdf/2310.19624v1", "arxiv_id": "2310.19624", "doi": "10.1109/BIBM58861.2023.10385775", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Bioinformatics and Biomedicine", "quality_score": 0.0}
{"id": "634c2cf0178fbc4edabe9241f2b824bf01875a929aeb821b964e6509992cfaf3", "sources": ["arxiv", "semantic_scholar"], "title": "GPCR-BERT: Interpreting Sequential Design of G Protein Coupled Receptors Using Protein Language Models", "abstract": "With the rise of Transformers and Large Language Models (LLMs) in Chemistry and Biology, new avenues for the design and understanding of therapeutics have opened up to the scientific community. Protein sequences can be modeled as language and can take advantage of recent advances in LLMs, specifically with the abundance of our access to the protein sequence datasets. In this paper, we developed the GPCR-BERT model for understanding the sequential design of G Protein-Coupled Receptors (GPCRs). GPCRs are the target of over one-third of FDA-approved pharmaceuticals. However, there is a lack of comprehensive understanding regarding the relationship between amino acid sequence, ligand selectivity, and conformational motifs (such as NPxxY, CWxP, E/DRY). By utilizing the pre-trained protein model (Prot-Bert) and fine-tuning with prediction tasks of variations in the motifs, we were able to shed light on several relationships between residues in the binding pocket and some of the conserved motifs. To achieve this, we took advantage of attention weights, and hidden states of the model that are interpreted to extract the extent of contributions of amino acids in dictating the type of masked ones. The fine-tuned models demonstrated high accuracy in predicting hidden residues within the motifs. In addition, the analysis of embedding was performed over 3D structures to elucidate the higher-order interactions within the conformations of the receptors.", "authors": ["Seongwon Kim", "Parisa Mollaei", "Akshay Antony", "Rishikesh Magar", "Amir Barati Farimani"], "categories": ["cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology", "Medicine"], "published_date": "2023-10-30", "url": "https://arxiv.org/abs/2310.19915", "pdf_url": "https://arxiv.org/pdf/2310.19915v1", "arxiv_id": "2310.19915", "doi": "10.1021/acs.jcim.3c01706", "citation_count": 17, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Chemical Information and Modeling", "quality_score": 0.3138}
{"id": "06df6b82b7242860ccc174055d0dc1d0bd831a9a619d6d207ba5a3453961ea83", "sources": ["arxiv", "semantic_scholar"], "title": "Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation", "abstract": "Visual Word Sense Disambiguation (VWSD) is a novel challenging task with the goal of retrieving an image among a set of candidates, which better represents the meaning of an ambiguous word within a given context. In this paper, we make a substantial step towards unveiling this interesting task by applying a varying set of approaches. Since VWSD is primarily a text-image retrieval task, we explore the latest transformer-based methods for multimodal retrieval. Additionally, we utilize Large Language Models (LLMs) as knowledge bases to enhance the given phrases and resolve ambiguity related to the target word. We also study VWSD as a unimodal problem by converting to text-to-text and image-to-image retrieval, as well as question-answering (QA), to fully explore the capabilities of relevant models. To tap into the implicit knowledge of LLMs, we experiment with Chain-of-Thought (CoT) prompting to guide explainable answer generation. On top of all, we train a learn to rank (LTR) model in order to combine our different modules, achieving competitive ranking results. Extensive experiments on VWSD demonstrate valuable insights to effectively drive future directions.", "authors": ["Anastasia Kritharoula", "Maria Lymperaiou", "Giorgos Stamou"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-21", "url": "https://arxiv.org/abs/2310.14025", "pdf_url": "https://arxiv.org/pdf/2310.14025v1", "arxiv_id": "2310.14025", "doi": "10.18653/v1/2023.emnlp-main.807", "citation_count": 10, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.2603}
{"id": "c3b6fa685318d64bd0e2bc2f7af01c5b37567e5d261138e9c1c9961420714cfb", "sources": ["arxiv", "semantic_scholar"], "title": "ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model", "abstract": "Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. Here we report a generative model that predicts protein designs to meet complex nonlinear mechanical property-design objectives. Our model leverages deep knowledge on protein sequences from a pre-trained protein language model and maps mechanical unfolding responses to create novel proteins. Via full-atom molecular simulations for direct validation, we demonstrate that the designed proteins are novel, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, as well as the detailed unfolding force-separation curves. Our model offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, using mechanical features as target to enable the discovery of protein materials with superior mechanical properties.", "authors": ["Bo Ni", "David L. Kaplan", "Markus J. Buehler"], "categories": ["cond-mat.mtrl-sci", "cond-mat.mes-hall", "cs.CL", "cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Physics", "Biology", "Medicine"], "published_date": "2023-10-16", "url": "https://arxiv.org/abs/2310.10605", "pdf_url": "https://arxiv.org/pdf/2310.10605v3", "arxiv_id": "2310.10605", "doi": "10.48550/arXiv.2310.10605", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "89093eeb1c7ae6ee601b9244a24075254cf96f8a5d3a70ccfdc737cd8cd08467", "sources": ["arxiv", "semantic_scholar"], "title": "Joint Music and Language Attention Models for Zero-shot Music Tagging", "abstract": "Music tagging is a task to predict the tags of music recordings. However, previous music tagging research primarily focuses on close-set music tagging tasks which can not be generalized to new tags. In this work, we propose a zero-shot music tagging system modeled by a joint music and language attention (JMLA) model to address the open-set music tagging problem. The JMLA model consists of an audio encoder modeled by a pretrained masked autoencoder and a decoder modeled by a Falcon7B. We introduce preceiver resampler to convert arbitrary length audio into fixed length embeddings. We introduce dense attention connections between encoder and decoder layers to improve the information flow between the encoder and decoder layers. We collect a large-scale music and description dataset from the internet. We propose to use ChatGPT to convert the raw descriptions into formalized and diverse descriptions to train the JMLA models. Our proposed JMLA system achieves a zero-shot audio tagging accuracy of $ 64.82\\% $ on the GTZAN dataset, outperforming previous zero-shot systems and achieves comparable results to previous systems on the FMA and the MagnaTagATune datasets.", "authors": ["Xingjian Du", "Zhesong Yu", "Jiaju Lin", "Bilei Zhu", "Qiuqiang Kong"], "categories": ["cs.SD", "cs.CL", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2023-10-16", "url": "https://arxiv.org/abs/2310.10159", "pdf_url": "https://arxiv.org/pdf/2310.10159v1", "arxiv_id": "2310.10159", "doi": "10.1109/ICASSP48485.2024.10447760", "citation_count": 15, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "IEEE International Conference on Acoustics, Speech, and Signal Processing", "quality_score": 0.301}
{"id": "dcab335dc09cd6f12d3c1fd70d5d10ae35fe37845b8b858c30762f26f961bb00", "sources": ["arxiv", "semantic_scholar"], "title": "Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction", "abstract": "Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on experimental protein structure data and fail in scenarios where these data are unavailable. Predicted protein structures from AI tools (e.g., AlphaFold2) were utilized as alternatives. However, we observed that current practices, which simply employ accurately predicted structures during inference, suffer from notable degradation in prediction accuracy. While similar phenomena have been extensively studied in general fields (e.g., Computer Vision) as model robustness, their impact on protein property prediction remains unexplored. In this paper, we first investigate the reason behind the performance decrease when utilizing predicted structures, attributing it to the structure embedding bias from the perspective of structure representation learning. To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures. Extensive experiments have shown that our framework is model-agnostic and effective in improving the property prediction of both predicted structures and experimental structures. The benchmark datasets and codes will be released to benefit the community.", "authors": ["Yufei Huang", "Siyuan Li", "Jin Su", "Lirong Wu", "Odin Zhang", "Haitao Lin", "Jingqi Qi", "Zihan Liu", "Zhangyang Gao", "Yuyang Liu", "Jiangbin Zheng", "Stan. ZQ. Li"], "categories": ["cs.LG", "cs.AI", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-10-14", "url": "https://arxiv.org/abs/2310.11466", "pdf_url": "https://arxiv.org/pdf/2310.11466v2", "arxiv_id": "2310.11466", "doi": "10.48550/arXiv.2310.11466", "citation_count": 18, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "AAAI Conference on Artificial Intelligence", "quality_score": 0.3197}
{"id": "361b7e3af0b692812d098f5ae248a268fe6c998a9e7b05bfdea77af1c241e0aa", "sources": ["arxiv", "semantic_scholar"], "title": "Humans and language models diverge when predicting repeating text", "abstract": "Language models that are trained on the next-word prediction task have been shown to accurately model human behavior in word prediction and reading speed. In contrast with these findings, we present a scenario in which the performance of humans and LMs diverges. We collected a dataset of human next-word predictions for five stimuli that are formed by repeating spans of text. Human and GPT-2 LM predictions are strongly aligned in the first presentation of a text span, but their performance quickly diverges when memory (or in-context learning) begins to play a role. We traced the cause of this divergence to specific attention heads in a middle layer. Adding a power-law recency bias to these attention heads yielded a model that performs much more similarly to humans. We hope that this scenario will spur future work in bringing LMs closer to human behavior.", "authors": ["Aditya R. Vaidya", "Javier Turek", "Alexander G. Huth"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-10", "url": "https://arxiv.org/abs/2310.06408", "pdf_url": "https://arxiv.org/pdf/2310.06408v2", "arxiv_id": "2310.06408", "doi": "10.48550/arXiv.2310.06408", "citation_count": 15, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/HuthLab/lm-repeating-text", "venue": "Conference on Computational Natural Language Learning", "quality_score": 0.301}
{"id": "f31bb63467688df60672e649c8fb9063df8069b9bc9c5f6d833b2550b5b47fba", "sources": ["arxiv", "semantic_scholar"], "title": "Growing ecosystem of deep learning methods for modeling protein$\\unicode{x2013}$protein interactions", "abstract": "Numerous cellular functions rely on protein$\\unicode{x2013}$protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a promising approach for tackling this problem by exploiting both experimental data and basic biophysical knowledge about protein interactions. Here, we review the growing ecosystem of deep learning methods for modeling protein interactions, highlighting the diversity of these biophysically-informed models and their respective trade-offs. We discuss recent successes in using representation learning to capture complex features pertinent to predicting protein interactions and interaction sites, geometric deep learning to reason over protein structures and predict complex structures, and generative modeling to design de novo protein assemblies. We also outline some of the outstanding challenges and promising new directions. Opportunities abound to discover novel interactions, elucidate their physical mechanisms, and engineer binders to modulate their functions using deep learning and, ultimately, unravel how protein interactions orchestrate complex cellular behaviors.", "authors": ["Julia R. Rogers", "Gergő Nikolényi", "Mohammed AlQuraishi"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Biology", "Computer Science", "Medicine"], "published_date": "2023-10-10", "url": "https://arxiv.org/abs/2310.06725", "pdf_url": "https://arxiv.org/pdf/2310.06725v2", "arxiv_id": "2310.06725", "doi": "10.48550/arXiv.2310.06725", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.25}
{"id": "11968fb1f30519423fc8cd131f2541da67034c72ffcbd30765eaa38747acda38", "sources": ["arxiv", "semantic_scholar"], "title": "VQPL: Vector Quantized Protein Language", "abstract": "Is there a foreign language describing protein sequences and structures simultaneously? Protein structures, represented by continuous 3D points, have long posed a challenge due to the contrasting modeling paradigms of discrete sequences. To represent protein sequence-structure as discrete symbols, we propose a VQProteinformer to project residue types and structures into a discrete space, supervised by a reconstruction loss to ensure information preservation. The sequential latent codes of residues introduce a new quantized protein language, transforming the protein sequence-structure into a unified modality. We demonstrate the potential of the created protein language on predictive and generative tasks, which may not only advance protein research but also establish a connection between the protein-related and NLP-related fields. The proposed method will be continually improved to unify more protein modalities, including text and point cloud.", "authors": ["Zhangyang Gao", "Cheng Tan", "Stan Z. Li"], "categories": ["cs.CE"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-08", "url": "https://arxiv.org/abs/2310.04985", "pdf_url": "https://arxiv.org/pdf/2310.04985v1", "arxiv_id": "2310.04985", "doi": "10.48550/arXiv.2310.04985", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "6c4f17468cc3ae2785cd6725e7a910eaa7007408737bdc8613171652308449a8", "sources": ["arxiv", "semantic_scholar"], "title": "PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps", "abstract": "Developing and discovering new drugs is a complex and resource-intensive endeavor that often involves substantial costs, time investment, and safety concerns. A key aspect of drug discovery involves identifying novel drug-target (DT) interactions. Existing computational methods for predicting DT interactions have primarily focused on binary classification tasks, aiming to determine whether a DT pair interacts or not. However, protein-ligand interactions exhibit a continuum of binding strengths, known as binding affinity, presenting a persistent challenge for accurate prediction. In this study, we investigate various techniques employed in Drug Target Interaction (DTI) prediction and propose novel enhancements to enhance their performance. Our approaches include the integration of Protein Language Models (PLMs) and the incorporation of Contact Map information as an inductive bias within current models. Through extensive experimentation, we demonstrate that our proposed approaches outperform the baseline models considered in this study, presenting a compelling case for further development in this direction. We anticipate that the insights gained from this work will significantly narrow the search space for potential drugs targeting specific proteins, thereby accelerating drug discovery. Code and data for PGraphDTA are available at https://github.com/Yijia-Xiao/PgraphDTA/.", "authors": ["Rakesh Bal", "Yijia Xiao", "Wei Wang"], "categories": ["cs.LG", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-10-06", "url": "https://arxiv.org/abs/2310.04017", "pdf_url": "https://arxiv.org/pdf/2310.04017v3", "arxiv_id": "2310.04017", "doi": "10.48550/arXiv.2310.04017", "citation_count": 6, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Yijia-Xiao/PgraphDTA/", "venue": "arXiv.org", "quality_score": 0.2113}
{"id": "9f71c553ac494a20e1db34707694b9703cf41a23d0caddf2dbf6621c16f4c76c", "sources": ["arxiv", "semantic_scholar"], "title": "InstructProtein: Aligning Human and Protein Language via Knowledge Instruction", "abstract": "Large Language Models (LLMs) have revolutionized the field of natural language processing, but they fall short in comprehending biological sequences such as proteins. To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein languages: (i) taking a protein sequence as input to predict its textual function description and (ii) using natural language to prompt protein sequence generation. To achieve this, we first pre-train an LLM on both protein and natural language corpora, enabling it to comprehend individual languages. Then supervised instruction tuning is employed to facilitate the alignment of these two distinct languages. Herein, we introduce a knowledge graph-based instruction generation framework to construct a high-quality instruction dataset, addressing annotation imbalance and instruction deficits in existing protein-text corpus. In particular, the instructions inherit the structural relations between proteins and function annotations in knowledge graphs, which empowers our model to engage in the causal modeling of protein functions, akin to the chain-of-thought processes in natural languages. Extensive experiments on bidirectional protein-text generation tasks show that InstructProtein outperforms state-of-the-art LLMs by large margins. Moreover, InstructProtein serves as a pioneering step towards text-based protein function prediction and sequence design, effectively bridging the gap between protein and human language understanding.", "authors": ["Zeyuan Wang", "Qiang Zhang", "Keyan Ding", "Ming Qin", "Xiang Zhuang", "Xiaotong Li", "Huajun Chen"], "categories": ["q-bio.BM", "cs.CL"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-10-05", "url": "https://arxiv.org/abs/2310.03269", "pdf_url": "https://arxiv.org/pdf/2310.03269v1", "arxiv_id": "2310.03269", "doi": "10.48550/arXiv.2310.03269", "citation_count": 40, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.4032}
{"id": "cfa49c929a45cf3d611e30bbaa3596c403ce7fe03f7ea34732e0a8cb8e9e2c3b", "sources": ["arxiv", "semantic_scholar"], "title": "CrysFormer: Protein Structure Prediction via 3d Patterson Maps and Partial Structure Attention", "abstract": "Determining the structure of a protein has been a decades-long open question. A protein's three-dimensional structure often poses nontrivial computation costs, when classical simulation algorithms are utilized. Advances in the transformer neural network architecture -- such as AlphaFold2 -- achieve significant improvements for this problem, by learning from a large dataset of sequence information and corresponding protein structures. Yet, such methods only focus on sequence information; other available prior knowledge, such as protein crystallography and partial structure of amino acids, could be potentially utilized. To the best of our knowledge, we propose the first transformer-based model that directly utilizes protein crystallography and partial structure information to predict the electron density maps of proteins. Via two new datasets of peptide fragments (2-residue and 15-residue) , we demonstrate our method, dubbed \\texttt{CrysFormer}, can achieve accurate predictions, based on a much smaller dataset size and with reduced computation costs.", "authors": ["Chen Dun", "Qiutai Pan", "Shikai Jin", "Ria Stevens", "Mitchell D. Miller", "George N. Phillips,", "Anastasios Kyrillidis"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-05", "url": "https://arxiv.org/abs/2310.03899", "pdf_url": "https://arxiv.org/pdf/2310.03899v1", "arxiv_id": "2310.03899", "doi": "10.48550/arXiv.2310.03899", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "3ae1a41de90b8fd376c39a07f3979c4f1402250234232ce12ad8a5dab07ad991", "sources": ["arxiv", "semantic_scholar"], "title": "All Languages Matter: On the Multilingual Safety of Large Language Models", "abstract": "Safety lies at the core of developing and deploying large language models (LLMs). However, previous safety benchmarks only concern the safety in one language, e.g. the majority language in the pretraining data such as English. In this work, we build the first multilingual safety benchmark for LLMs, XSafety, in response to the global deployment of LLMs in practice. XSafety covers 14 kinds of commonly used safety issues across 10 languages that span several language families. We utilize XSafety to empirically study the multilingual safety for 4 widely-used LLMs, including both close-API and open-source models. Experimental results show that all LLMs produce significantly more unsafe responses for non-English queries than English ones, indicating the necessity of developing safety alignment for non-English languages. In addition, we propose several simple and effective prompting methods to improve the multilingual safety of ChatGPT by evoking safety knowledge and improving cross-lingual generalization of safety alignment. Our prompting method can significantly reduce the ratio of unsafe responses from 19.1% to 9.7% for non-English queries. We release our data at https://github.com/Jarviswang94/Multilingual_safety_benchmark.", "authors": ["Wenxuan Wang", "Zhaopeng Tu", "Chang Chen", "Youliang Yuan", "Jen-tse Huang", "Wenxiang Jiao", "Michael R. Lyu"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-10-02", "url": "https://arxiv.org/abs/2310.00905", "pdf_url": "https://arxiv.org/pdf/2310.00905v2", "arxiv_id": "2310.00905", "doi": "10.48550/arXiv.2310.00905", "citation_count": 50, "influential_citation_count": 5, "has_code": true, "code_url": "https://github.com/Jarviswang94/Multilingual_safety_benchmark", "venue": "arXiv.org", "quality_score": 0.4269}
{"id": "65372dce4caf433ef4defe378813c06c16895b8a33e6bb0ad84a3d05a759c0b7", "sources": ["arxiv", "semantic_scholar"], "title": "PB-LLM: Partially Binarized Large Language Models", "abstract": "This paper explores network binarization, a radical form of quantization, compressing model weights to a single bit, specifically for Large Language Models (LLMs) compression. Due to previous binarization methods collapsing LLMs, we propose a novel approach, Partially-Binarized LLM (PB-LLM), which can achieve extreme low-bit quantization while maintaining the linguistic reasoning capacity of quantized LLMs. Specifically, our exploration first uncovers the ineffectiveness of naive applications of existing binarization algorithms and highlights the imperative role of salient weights in achieving low-bit quantization. Thus, PB-LLM filters a small ratio of salient weights during binarization, allocating them to higher-bit storage, i.e., partially-binarization. PB-LLM is extended to recover the capacities of quantized LMMs, by analyzing from the perspective of post-training quantization (PTQ) and quantization-aware training (QAT). Under PTQ, combining the concepts from GPTQ, we reconstruct the binarized weight matrix guided by the Hessian matrix and successfully recover the reasoning capacity of PB-LLM in low-bit. Under QAT, we freeze the salient weights during training, explore the derivation of optimal scaling factors crucial for minimizing the quantization error, and propose a scaling mechanism based on this derived scaling strategy for residual binarized weights. Those explorations and the developed methodologies significantly contribute to rejuvenating the performance of low-bit quantized LLMs and present substantial advancements in the field of network binarization for LLMs.The code is available at https://github.com/hahnyuan/BinaryLLM.", "authors": ["Yuzhang Shang", "Zhihang Yuan", "Qiang Wu", "Zhen Dong"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-09-29", "url": "https://arxiv.org/abs/2310.00034", "pdf_url": "https://arxiv.org/pdf/2310.00034v2", "arxiv_id": "2310.00034", "doi": "10.48550/arXiv.2310.00034", "citation_count": 95, "influential_citation_count": 15, "has_code": true, "code_url": "https://github.com/hahnyuan/BinaryLLM", "venue": "International Conference on Learning Representations", "quality_score": 0.6021}
{"id": "7230ecfbb022cebc7c7f39de0dc235be982dbc84969c15e212e0c203da79972c", "sources": ["arxiv", "semantic_scholar"], "title": "pLMFPPred: a novel approach for accurate prediction of functional peptides integrating embedding from pre-trained protein language model and imbalanced learning", "abstract": "Functional peptides have the potential to treat a variety of diseases. Their good therapeutic efficacy and low toxicity make them ideal therapeutic agents. Artificial intelligence-based computational strategies can help quickly identify new functional peptides from collections of protein sequences and discover their different functions.Using protein language model-based embeddings (ESM-2), we developed a tool called pLMFPPred (Protein Language Model-based Functional Peptide Predictor) for predicting functional peptides and identifying toxic peptides. We also introduced SMOTE-TOMEK data synthesis sampling and Shapley value-based feature selection techniques to relieve data imbalance issues and reduce computational costs. On a validated independent test set, pLMFPPred achieved accuracy, Area under the curve - Receiver Operating Characteristics, and F1-Score values of 0.974, 0.99, and 0.974, respectively. Comparative experiments show that pLMFPPred outperforms current methods for predicting functional peptides.The experimental results suggest that the proposed method (pLMFPPred) can provide better performance in terms of Accuracy, Area under the curve - Receiver Operating Characteristics, and F1-Score than existing methods. pLMFPPred has achieved good performance in predicting functional peptides and represents a new computational method for predicting functional peptides.", "authors": ["Zebin Ma", "Yonglin Zou", "Xiaobin Huang", "Wenjin Yan", "Hao Xu", "Jiexin Yang", "Ying Zhang", "Jinqi Huang"], "categories": ["q-bio.QM", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-09-25", "url": "https://arxiv.org/abs/2309.14404", "pdf_url": "https://arxiv.org/pdf/2309.14404v1", "arxiv_id": "2309.14404", "doi": "10.48550/arXiv.2309.14404", "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1747}
{"id": "d2dd2d2d7a18e63289156f8dcf91630610e899794fd94a3681e763db652b77af", "sources": ["arxiv", "semantic_scholar"], "title": "Speaker attribution in German parliamentary debates with QLoRA-adapted large language models", "abstract": "The growing body of political texts opens up new opportunities for rich insights into political dynamics and ideologies but also increases the workload for manual analysis. Automated speaker attribution, which detects who said what to whom in a speech event and is closely related to semantic role labeling, is an important processing step for computational text analysis. We study the potential of the large language model family Llama 2 to automate speaker attribution in German parliamentary debates from 2017-2021. We fine-tune Llama 2 with QLoRA, an efficient training strategy, and observe our approach to achieve competitive performance in the GermEval 2023 Shared Task On Speaker Attribution in German News Articles and Parliamentary Debates. Our results shed light on the capabilities of large language models in automating speaker attribution, revealing a promising avenue for computational analysis of political discourse and the development of semantic role labeling systems.", "authors": ["Tobias Bornheim", "Niklas Grieger", "Patrick Gustav Blaneck", "Stephan Bialonski"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-09-18", "url": "https://arxiv.org/abs/2309.09902", "pdf_url": "https://arxiv.org/pdf/2309.09902v2", "arxiv_id": "2309.09902", "doi": "10.21248/jlcl.37.2024.244", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal for Language Technology and Computational Linguistics", "quality_score": 0.1193}
{"id": "1228aed58d0055d19b8c959ff70dd01706e62c39d913f12f1e0e3648a1b174a6", "sources": ["arxiv", "semantic_scholar"], "title": "Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models", "abstract": "We introduce Jais and Jais-chat, new state-of-the-art Arabic-centric foundation and instruction-tuned open generative large language models (LLMs). The models are based on the GPT-3 decoder-only architecture and are pretrained on a mixture of Arabic and English texts, including source code in various programming languages. With 13 billion parameters, they demonstrate better knowledge and reasoning capabilities in Arabic than any existing open Arabic and multilingual models by a sizable margin, based on extensive evaluation. Moreover, the models are competitive in English compared to English-centric open models of similar size, despite being trained on much less English data. We provide a detailed description of the training, the tuning, the safety alignment, and the evaluation of the models. We release two open versions of the model -- the foundation Jais model, and an instruction-tuned Jais-chat variant -- with the aim of promoting research on Arabic LLMs. Available at https://huggingface.co/inception-mbzuai/jais-13b-chat", "authors": ["Neha Sengupta", "Sunil Kumar Sahu", "Bokang Jia", "Satheesh Katipomu", "Haonan Li", "Fajri Koto", "William Marshall", "Gurpreet Gosal", "Cynthia Liu", "Zhiming Chen", "Osama Mohammed Afzal", "Samta Kamboj", "Onkar Pandit", "Rahul Pal", "Lalit Pradhan", "Zain Muhammad Mujahid", "Massa Baali", "Xudong Han", "Sondos Mahmoud Bsharat", "Alham Fikri Aji", "Zhiqiang Shen", "Zhengzhong Liu", "Natalia Vassilieva", "Joel Hestness", "Andy Hock", "Andrew Feldman", "Jonathan Lee", "Andrew Jackson", "Hector Xuguang Ren", "Preslav Nakov", "Timothy Baldwin", "Eric Xing"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-08-30", "url": "https://arxiv.org/abs/2308.16149", "pdf_url": "https://arxiv.org/pdf/2308.16149v2", "arxiv_id": "2308.16149", "doi": "10.48550/arXiv.2308.16149", "citation_count": 81, "influential_citation_count": 8, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4785}
{"id": "b4998bcc11ee75a6f15470d1055606546eff53b29cf64efe7465db3b36b9531a", "sources": ["arxiv", "semantic_scholar"], "title": "Atom-by-atom protein generation and beyond with language models", "abstract": "Protein language models learn powerful representations directly from sequences of amino acids. However, they are constrained to generate proteins with only the set of amino acids represented in their vocabulary. In contrast, chemical language models learn atom-level representations of smaller molecules that include every atom, bond, and ring. In this work, we show that chemical language models can learn atom-level representations of proteins enabling protein generation unconstrained to the standard genetic code and far beyond it. In doing so, we show that language models can generate entire proteins atom by atom -- effectively learning the multiple hierarchical layers of molecular information that define proteins from their primary sequence to their secondary, and tertiary structure. We demonstrate language models are able to explore beyond protein space -- generating proteins with modified sidechains that form unnatural amino acids. Even further, we find that language models can explore chemical space and protein space simultaneously and generate novel examples of protein-drug conjugates. The results demonstrate the potential for biomolecular design at the atom level using language models.", "authors": ["Daniel Flam-Shepherd", "Kevin Zhu", "Alán Aspuru-Guzik"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2023-08-16", "url": "https://arxiv.org/abs/2308.09482", "pdf_url": "https://arxiv.org/pdf/2308.09482v1", "arxiv_id": "2308.09482", "doi": "10.48550/arXiv.2308.09482", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "165d61fc97b194ebbd7908388675d1dc3d0baa5a9a690e0a7c6d1b30fdd73a13", "sources": ["arxiv", "semantic_scholar"], "title": "PEvoLM: Protein Sequence Evolutionary Information Language Model", "abstract": "With the exponential increase of the protein sequence databases over time, multiple-sequence alignment (MSA) methods, like PSI-BLAST, perform exhaustive and time-consuming database search to retrieve evolutionary information. The resulting position-specific scoring matrices (PSSMs) of such search engines represent a crucial input to many machine learning (ML) models in the field of bioinformatics and computational biology. A protein sequence is a collection of contiguous tokens or characters called amino acids (AAs). The analogy to natural language allowed us to exploit the recent advancements in the field of Natural Language Processing (NLP) and therefore transfer NLP state-of-the-art algorithms to bioinformatics. This research presents an Embedding Language Model (ELMo), converting a protein sequence to a numerical vector representation. While the original ELMo trained a 2-layer bidirectional Long Short-Term Memory (LSTMs) network following a two-path architecture, one for the forward and the second for the backward pass, by merging the idea of PSSMs with the concept of transfer-learning, this work introduces a novel bidirectional language model (bi-LM) with four times less free parameters and using rather a single path for both passes. The model was trained not only on predicting the next AA but also on the probability distribution of the next AA derived from similar, yet different sequences as summarized in a PSSM, simultaneously for multi-task learning, hence learning evolutionary information of protein sequences as well. The network architecture and the pre-trained model are made available as open source under the permissive MIT license on GitHub at https://github.com/issararab/PEvoLM.", "authors": ["Issar Arab"], "categories": ["q-bio.QM", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-08-16", "url": "https://arxiv.org/abs/2308.08578", "pdf_url": "https://arxiv.org/pdf/2308.08578v1", "arxiv_id": "2308.08578", "doi": "10.1109/CIBCB56990.2023.10264890", "citation_count": 2, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/issararab/PEvoLM", "venue": "IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology", "quality_score": 0.1193}
{"id": "79ee2d23699659e8f8dfd6574dadff8595d3f1d64fc3fbe285910f5670d64158", "sources": ["arxiv", "semantic_scholar"], "title": "Pairing interacting protein sequences using masked language modeling", "abstract": "Predicting which proteins interact together from amino-acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments, such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate the problem of pairing interacting partners among the paralogs of two protein families in a differentiable way. We introduce a method called DiffPALM that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context. MSA Transformer encodes coevolution between functionally or structurally coupled amino acids. We show that it captures inter-chain coevolution, while it was trained on single-chain data, which means that it can be used out-of-distribution. Relying on MSA Transformer without fine-tuning, DiffPALM outperforms existing coevolution-based pairing methods on difficult benchmarks of shallow multiple sequence alignments extracted from ubiquitous prokaryotic protein datasets. It also outperforms an alternative method based on a state-of-the-art protein language model trained on single sequences. Paired alignments of interacting protein sequences are a crucial ingredient of supervised deep learning methods to predict the three-dimensional structure of protein complexes. DiffPALM substantially improves the structure prediction of some eukaryotic protein complexes by AlphaFold-Multimer, without significantly deteriorating any of those we tested. It also achieves competitive performance with using orthology-based pairing.", "authors": ["Umberto Lupo", "Damiano Sgarbossa", "Anne-Florence Bitbol"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Biology", "Computer Science", "Medicine"], "published_date": "2023-08-14", "url": "https://arxiv.org/abs/2308.07136", "pdf_url": "https://arxiv.org/pdf/2308.07136v1", "arxiv_id": "2308.07136", "doi": "10.1073/pnas.2311887121", "citation_count": 25, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "bioRxiv", "quality_score": 0.3537}
{"id": "21f4235e78a1427494d2450af10a9541f7dbcf443ccf0d7c985287a6da7a244b", "sources": ["arxiv", "semantic_scholar"], "title": "FFF: Fragments-Guided Flexible Fitting for Building Complete Protein Structures", "abstract": "Cryo-electron microscopy (cryo-EM) is a technique for reconstructing the 3-dimensional (3D) structure of biomolecules (especially large protein complexes and molecular assemblies). As the resolution increases to the near-atomic scale, building protein structures de novo from cryo-EM maps becomes possible. Recently, recognition-based de novo building methods have shown the potential to streamline this process. However, it cannot build a complete structure due to the low signal-to-noise ratio (SNR) problem. At the same time, AlphaFold has led to a great breakthrough in predicting protein structures. This has inspired us to combine fragment recognition and structure prediction methods to build a complete structure. In this paper, we propose a new method named FFF that bridges protein structure prediction and protein structure recognition with flexible fitting. First, a multi-level recognition network is used to capture various structural features from the input 3D cryo-EM map. Next, protein structural fragments are generated using pseudo peptide vectors and a protein sequence alignment method based on these extracted features. Finally, a complete structural model is constructed using the predicted protein fragments via flexible fitting. Based on our benchmark tests, FFF outperforms the baseline methods for building complete protein structures.", "authors": ["Weijie Chen", "Xinyan Wang", "Yuhang Wang"], "categories": ["cs.CV", "cs.AI", "q-bio.BM", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-08-07", "url": "https://arxiv.org/abs/2308.03654", "pdf_url": "https://arxiv.org/pdf/2308.03654v1", "arxiv_id": "2308.03654", "doi": "10.48550/arXiv.2308.03654", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "0a642f9f6efbfc66378e8980e6f5b9942da1ba7d30574abde9f6f6d54e154e66", "sources": ["arxiv", "semantic_scholar"], "title": "Turkish Native Language Identification V2", "abstract": "This paper presents the first application of Native Language Identification (NLI) for the Turkish language. NLI is the task of automatically identifying an individual's native language (L1) based on their writing or speech in a non-native language (L2). While most NLI research has focused on L2 English, our study extends this scope to L2 Turkish by analyzing a corpus of texts written by native speakers of Albanian, Arabic and Persian. We leverage a cleaned version of the Turkish Learner Corpus and demonstrate the effectiveness of syntactic features, comparing a structural Part-of-Speech n-gram model to a hybrid model that retains function words. Our models achieve promising results, and we analyze the most predictive features to reveal L1-specific transfer effects. We make our data and code publicly available for further study.", "authors": ["Ahmet Yavuz Uluslu", "Gerold Schneider"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-07-27", "url": "https://arxiv.org/abs/2307.14850", "pdf_url": "https://arxiv.org/pdf/2307.14850v6", "arxiv_id": "2307.14850", "doi": "10.48550/arXiv.2307.14850", "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Natural Language and Speech Processing", "quality_score": 0.0753}
{"id": "4688455352a2350cb465f83bacb075b7c6a9c5b5c763f6fb34dbcf797ee6fbc5", "sources": ["arxiv", "semantic_scholar"], "title": "Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models", "abstract": "Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible. This review aims to offer a comprehensive, systematic, and indispensable set of methodological components, including TDA and NLP, for protein engineering and to facilitate their future development.", "authors": ["Yuchi Qiu", "Guo-Wei Wei"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology", "Medicine", "Computer Science"], "published_date": "2023-07-27", "url": "https://arxiv.org/abs/2307.14587", "pdf_url": "https://arxiv.org/pdf/2307.14587v1", "arxiv_id": "2307.14587", "doi": "10.1093/bib/bbad289", "citation_count": 58, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.4427}
{"id": "94ee3581db609718775287ea45a7d4826c8c10bb02973a8493afe0f5c420d1b8", "sources": ["arxiv", "semantic_scholar"], "title": "Predicting Relative Populations of Protein Conformations without a Physics Engine Using AlphaFold2", "abstract": "This paper presents a novel approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' single ground state conformations and is limited in its ability to predict fold switching and the effects of mutations on conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different conformations of proteins and even accurately predict changes in those populations induced by mutations by subsampling multiple sequence alignments. We tested our method against NMR experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with accuracies in excess of 80%. Our method offers a fast and cost-effective way to predict protein conformations and their relative populations at even single point mutation resolution, making it a useful tool for pharmacology, analyzing NMR data, and studying the effects of evolution.", "authors": ["Gabriel Monteiro da Silva", "Jennifer Y. Cui", "David C. Dalgarno", "George P. Lisi", "Brenda M. Rubenstein"], "categories": ["physics.bio-ph", "physics.chem-ph", "q-bio.BM"], "fields_of_study": ["Medicine", "Physics", "Biology"], "published_date": "2023-07-26", "url": "https://arxiv.org/abs/2307.14470", "pdf_url": "https://arxiv.org/pdf/2307.14470v1", "arxiv_id": "2307.14470", "doi": null, "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2258}
{"id": "b3b0ab95b1e40b071c2aebb956d54dc75c2f07564e335dfa5c473c89063e313b", "sources": ["arxiv", "semantic_scholar"], "title": "DeepGATGO: A Hierarchical Pretraining-Based Graph-Attention Model for Automatic Protein Function Prediction", "abstract": "Automatic protein function prediction (AFP) is classified as a large-scale multi-label classification problem aimed at automating protein enrichment analysis to eliminate the current reliance on labor-intensive wet-lab methods. Currently, popular methods primarily combine protein-related information and Gene Ontology (GO) terms to generate final functional predictions. For example, protein sequences, structural information, and protein-protein interaction networks are integrated as prior knowledge to fuse with GO term embeddings and generate the ultimate prediction results. However, these methods are limited by the difficulty in obtaining structural information or network topology information, as well as the accuracy of such data. Therefore, more and more methods that only use protein sequences for protein function prediction have been proposed, which is a more reliable and computationally cheaper approach. However, the existing methods fail to fully extract feature information from protein sequences or label data because they do not adequately consider the intrinsic characteristics of the data itself. Therefore, we propose a sequence-based hierarchical prediction method, DeepGATGO, which processes protein sequences and GO term labels hierarchically, and utilizes graph attention networks (GATs) and contrastive learning for protein function prediction. Specifically, we compute embeddings of the sequence and label data using pre-trained models to reduce computational costs and improve the embedding accuracy. Then, we use GATs to dynamically extract the structural information of non-Euclidean data, and learn general features of the label dataset with contrastive learning by constructing positive and negative example samples. Experimental results demonstrate that our proposed model exhibits better scalability in GO term enrichment analysis on large-scale datasets.", "authors": ["Zihao Li", "Changkun Jiang", "Jianqiang Li"], "categories": ["q-bio.QM", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-07-24", "url": "https://arxiv.org/abs/2307.13004", "pdf_url": "https://arxiv.org/pdf/2307.13004v1", "arxiv_id": "2307.13004", "doi": "10.48550/arXiv.2307.13004", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "f3aab11a568876ce9f21e28c56cc7d916b42434cbed2ab7b91381f260de2dd75", "sources": ["arxiv", "semantic_scholar"], "title": "A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks", "abstract": "We evaluate four state-of-the-art instruction-tuned large language models (LLMs) -- ChatGPT, Flan-T5 UL2, Tk-Instruct, and Alpaca -- on a set of 13 real-world clinical and biomedical natural language processing (NLP) tasks in English, such as named-entity recognition (NER), question-answering (QA), relation extraction (RE), etc. Our overall results demonstrate that the evaluated LLMs begin to approach performance of state-of-the-art models in zero- and few-shot scenarios for most tasks, and particularly well for the QA task, even though they have never seen examples from these tasks before. However, we observed that the classification and RE tasks perform below what can be achieved with a specifically trained model for the medical field, such as PubMedBERT. Finally, we noted that no LLM outperforms all the others on all the studied tasks, with some models being better suited for certain tasks than others.", "authors": ["Yanis Labrak", "Mickael Rouvier", "Richard Dufour"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-07-22", "url": "https://arxiv.org/abs/2307.12114", "pdf_url": "https://arxiv.org/pdf/2307.12114v3", "arxiv_id": "2307.12114", "doi": "10.48550/arXiv.2307.12114", "citation_count": 63, "influential_citation_count": 5, "has_code": false, "code_url": null, "venue": "International Conference on Language Resources and Evaluation", "quality_score": 0.4515}
{"id": "e09c4ec93ac0d80e35f185502401934500279dc4fd67d293757fcca9dbd9e961", "sources": ["arxiv", "semantic_scholar"], "title": "Introduction to Protein Structure", "abstract": "While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. Within the living cell, protein molecules perform specific functions, typically by interacting with other proteins, DNA, RNA or small molecules. They take on a specific three dimensional structure, encoded by its amino acid sequence, which allows them to function within the cell. Hence, the understanding of a protein's function is tightly coupled to its sequence and its three dimensional structure. Before going into protein structure analysis and prediction, and protein folding and dynamics, here, we give a short and concise introduction into the basics of protein structures.", "authors": ["Annika Jacobsen", "Erik van Dijk", "Halima Mouhib", "Bas Stringer", "Olga Ivanova", "Jose Gavaldá-Garciá", "Laura Hoekstra", "K. Anton Feenstra", "Sanne Abeln"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2023-07-05", "url": "https://arxiv.org/abs/2307.02169", "pdf_url": "https://arxiv.org/pdf/2307.02169v2", "arxiv_id": "2307.02169", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "b67040e6728c6049ff97178b0f8d58e3edb2eda84dc343b385566d4b379b4c1f", "sources": ["arxiv", "semantic_scholar"], "title": "Monte Carlo for Protein Structures", "abstract": "While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. In the previous chapter \"Molecular Dynamics\" we have considered protein simulations from a dynamical point of view, using Newton's laws. In the current Chapter, we first take a step back and return to the bare minimum needed to simulate proteins, and show that proteins may be simulated in a more simple fashion, using the partition function directly. This means we do not have to calculate explicit forces, velocities, moments and do not even consider time explicitly. Instead, we will rely on the fact that for most systems we will want to simulate, the system is in a dynamic equilibrium; and that we want to find the most stable states in such systems by determining the relative stabilities between those states.", "authors": ["Juami H. M. van Gils", "Maurits Dijkstra", "Halima Mouhib", "Arriën Symon Rauh", "Jocelyne Vreede", "K. Anton Feenstra", "Sanne Abeln"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2023-07-05", "url": "https://arxiv.org/abs/2307.02177", "pdf_url": "https://arxiv.org/pdf/2307.02177v2", "arxiv_id": "2307.02177", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "8803f9c3723342877cd7b90741b917c21c128cf98987c89e96ad46ad28585d5b", "sources": ["arxiv", "semantic_scholar"], "title": "Introduction to Protein Folding", "abstract": "While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. In this chapter we explore basic physical and chemical concepts required to understand protein folding. We introduce major (de)stabilising factors of folded protein structures such as the hydrophobic effect and backbone entropy. In addition, we consider different states along the folding pathway, as well as natively disordered proteins and aggregated protein states. In this chapter, an intuitive understanding is provided about the protein folding process, to prepare for the next chapter on the thermodynamics of protein folding. In particular, it is emphasized that protein folding is a stochastic process and that proteins unfold and refold in a dynamic equilibrium. The effect of temperature on the stability of the folded and unfolded states is also explained.", "authors": ["Juami H. M. van Gils", "Erik van Dijk", "Ali May", "Halima Mouhib", "Jochem Bijlard", "Annika Jacobsen", "Isabel Houtkamp", "K. Anton Feenstra", "Sanne Abeln"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2023-07-05", "url": "https://arxiv.org/abs/2307.02174", "pdf_url": "https://arxiv.org/pdf/2307.02174v2", "arxiv_id": "2307.02174", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "0aa4313a168da4d589fcc11b16703456d3f1ee1dbe52620ac3b49dd933fc74ec", "sources": ["arxiv", "semantic_scholar"], "title": "Thermodynamics of Protein Folding", "abstract": "While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. In the previous chapter, \"Introduction to Protein Folding\", we introduced the concept of free energy and the protein folding landscape. Here, we provide a deeper, more formal underpinning of free energy in terms of the entropy and enthalpy; to this end, we will first need to better define the meaning of equilibrium, entropy and enthalpy. When we understand these concepts, we will come back for a more quantitative explanation of protein folding and dynamics. We will discuss the influence of temperature on the free energy landscape, and the difference between microstates and macrostates.", "authors": ["Juami H. M. van Gils", "Halima Mouhib", "Erik van Dijk", "Maurits Dijkstra", "Isabel Houtkamp", "Arthur Goetzee", "Sanne Abeln", "K. Anton Feenstra"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2023-07-05", "url": "https://arxiv.org/abs/2307.02175", "pdf_url": "https://arxiv.org/pdf/2307.02175v2", "arxiv_id": "2307.02175", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "822191eb0e475024e980d71ca2c9754eb00c498a83d38f5529a96b725354c8be", "sources": ["arxiv", "semantic_scholar"], "title": "Structural Property Prediction", "abstract": "While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. Some structural properties of proteins that are closely linked to their function may be easier (or much faster) to predict from sequence than the complete tertiary structure; for example, secondary structure, surface accessibility, flexibility, disorder, interface regions or hydrophobic patches. Serving as building blocks for the native protein fold, these structural properties also contain important structural and functional information not apparent from the amino acid sequence. Here, we will first give an introduction into the application of machine learning for structural property prediction, and explain the concepts of cross-validation and benchmarking. Next, we will review various methods that incorporate knowledge of these concepts to predict those structural properties, such as secondary structure, surface accessibility, disorder and flexibility, and aggregation.", "authors": ["Maurits Dijkstra", "Punto Bawono", "Isabel Houtkamp", "Jose Gavaldá-Garciá", "Mascha Okounev", "Robbin Bouwmeester", "Bas Stringer", "Jaap Heringa", "Sanne Abeln", "K. Anton Feenstra", "Juami H. M. van Gils"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2023-07-05", "url": "https://arxiv.org/abs/2307.02172", "pdf_url": "https://arxiv.org/pdf/2307.02172v2", "arxiv_id": "2307.02172", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "29aaefc8e021637a00efd7fff11e81207b59d9f4d9aa4a02983744fc1ab594a2", "sources": ["arxiv", "semantic_scholar"], "title": "ALBERTI, a Multilingual Domain Specific Language Model for Poetry Analysis", "abstract": "The computational analysis of poetry is limited by the scarcity of tools to automatically analyze and scan poems. In a multilingual settings, the problem is exacerbated as scansion and rhyme systems only exist for individual languages, making comparative studies very challenging and time consuming. In this work, we present \\textsc{Alberti}, the first multilingual pre-trained large language model for poetry. Through domain-specific pre-training (DSP), we further trained multilingual BERT on a corpus of over 12 million verses from 12 languages. We evaluated its performance on two structural poetry tasks: Spanish stanza type classification, and metrical pattern prediction for Spanish, English and German. In both cases, \\textsc{Alberti} outperforms multilingual BERT and other transformers-based models of similar sizes, and even achieves state-of-the-art results for German when compared to rule-based systems, demonstrating the feasibility and effectiveness of DSP in the poetry domain.", "authors": ["Javier de la Rosa", "Álvaro Pérez Pozo", "Salvador Ros", "Elena González-Blanco"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-07-03", "url": "https://arxiv.org/abs/2307.01387", "pdf_url": "https://arxiv.org/pdf/2307.01387v1", "arxiv_id": "2307.01387", "doi": "10.48550/arXiv.2307.01387", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2386}
{"id": "c714534d49d494626999c2b5bef1b85eda6b63f907c37e0f72de4408c5c243a0", "sources": ["arxiv", "semantic_scholar"], "title": "Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning", "abstract": "Protein-DNA interaction is critical for life activities such as replication, transcription, and splicing. Identifying protein-DNA binding residues is essential for modeling their interaction and downstream studies. However, developing accurate and efficient computational methods for this task remains challenging. Improvements in this area have the potential to drive novel applications in biotechnology and drug design. In this study, we propose a novel approach called CLAPE, which combines a pre-trained protein language model and the contrastive learning method to predict DNA binding residues. We trained the CLAPE-DB model on the protein-DNA binding sites dataset and evaluated the model performance and generalization ability through various experiments. The results showed that the AUC values of the CLAPE-DB model on the two benchmark datasets reached 0.871 and 0.881, respectively, indicating superior performance compared to other existing models. CLAPE-DB showed better generalization ability and was specific to DNA-binding sites. In addition, we trained CLAPE on different protein-ligand binding sites datasets, demonstrating that CLAPE is a general framework for binding sites prediction. To facilitate the scientific community, the benchmark datasets and codes are freely available at https://github.com/YAndrewL/clape.", "authors": ["Yufan Liu", "Boxue Tian"], "categories": ["q-bio.BM", "q-bio.QM"], "fields_of_study": ["Computer Science", "Medicine", "Biology"], "published_date": "2023-06-28", "url": "https://arxiv.org/abs/2306.15912", "pdf_url": "https://arxiv.org/pdf/2306.15912v1", "arxiv_id": "2306.15912", "doi": "10.1093/bib/bbad488", "citation_count": 61, "influential_citation_count": 7, "has_code": true, "code_url": "https://github.com/YAndrewL/clape", "venue": null, "quality_score": 0.4515}
{"id": "f75160157dcd663bc2a9ff5731e270fa8bc08480d6ea61b4a875280fa8dff6d8", "sources": ["arxiv", "semantic_scholar"], "title": "WizardCoder: Empowering Code Large Language Models with Evol-Instruct", "abstract": "Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated exceptional performance in code-related tasks. However, most existing models are solely pre-trained on extensive raw code data without instruction fine-tuning. In this paper, we introduce WizardCoder, which empowers Code LLMs with complex instruction fine-tuning, by adapting the Evol-Instruct method to the domain of code. Through comprehensive experiments on four prominent code generation benchmarks, namely HumanEval, HumanEval+, MBPP, and DS-1000, we unveil the exceptional capabilities of our model. It surpasses all other open-source Code LLMs by a substantial margin. Moreover, our model even outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on HumanEval and HumanEval+. Our code, model weights, and data are public at https://github.com/nlpxucan/WizardLM", "authors": ["Ziyang Luo", "Can Xu", "Pu Zhao", "Qingfeng Sun", "Xiubo Geng", "Wenxiang Hu", "Chongyang Tao", "Jing Ma", "Qingwei Lin", "Daxin Jiang"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-14", "url": "https://arxiv.org/abs/2306.08568", "pdf_url": "https://arxiv.org/pdf/2306.08568v2", "arxiv_id": "2306.08568", "doi": null, "citation_count": 977, "influential_citation_count": 121, "has_code": true, "code_url": "https://github.com/nlpxucan/WizardLM", "venue": "International Conference on Learning Representations", "quality_score": 1.0}
{"id": "9db460d1af736c051401613b8615638ff279f21b69b4179ca548436666064249", "sources": ["arxiv", "semantic_scholar"], "title": "A Comprehensive Review of State-of-The-Art Methods for Java Code Generation from Natural Language Text", "abstract": "Java Code Generation consists in generating automatically Java code from a Natural Language Text. This NLP task helps in increasing programmers' productivity by providing them with immediate solutions to the simplest and most repetitive tasks. Code generation is a challenging task because of the hard syntactic rules and the necessity of a deep understanding of the semantic aspect of the programming language. Many works tried to tackle this task using either RNN-based, or Transformer-based models. The latter achieved remarkable advancement in the domain and they can be divided into three groups: (1) encoder-only models, (2) decoder-only models, and (3) encoder-decoder models. In this paper, we provide a comprehensive review of the evolution and progress of deep learning models in Java code generation task. We focus on the most important methods and present their merits and limitations, as well as the objective functions used by the community. In addition, we provide a detailed description of datasets and evaluation metrics used in the literature. Finally, we discuss results of different models on CONCODE dataset, then propose some future directions.", "authors": ["Jessica López Espejel", "Mahaman Sanoussi Yahaya Alassan", "El Mehdi Chouham", "Walid Dahhane", "El Hassane Ettifouri"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-06-10", "url": "https://arxiv.org/abs/2306.06371", "pdf_url": "https://arxiv.org/pdf/2306.06371v1", "arxiv_id": "2306.06371", "doi": "10.1016/j.nlp.2023.100013", "citation_count": 17, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Natural Language Processing Journal", "quality_score": 0.3138}
{"id": "8071b56548c07b7216e56ef0bac05ee8b9de4ffdf5b5d21634663515949a078d", "sources": ["arxiv", "semantic_scholar"], "title": "Multi-level Protein Representation Learning for Blind Mutational Effect Prediction", "abstract": "Directed evolution plays an indispensable role in protein engineering that revises existing protein sequences to attain new or enhanced functions. Accurately predicting the effects of protein variants necessitates an in-depth understanding of protein structure and function. Although large self-supervised language models have demonstrated remarkable performance in zero-shot inference using only protein sequences, these models inherently do not interpret the spatial characteristics of protein structures, which are crucial for comprehending protein folding stability and internal molecular interactions. This paper introduces a novel pre-training framework that cascades sequential and geometric analyzers for protein primary and tertiary structures. It guides mutational directions toward desired traits by simulating natural selection on wild-type proteins and evaluates the effects of variants based on their fitness to perform the function. We assess the proposed approach using a public database and two new databases for a variety of variant effect prediction tasks, which encompass a diverse set of proteins and assays from different taxa. The prediction results achieve state-of-the-art performance over other zero-shot learning methods for both single-site mutations and deep mutations.", "authors": ["Yang Tan", "Bingxin Zhou", "Yuanhong Jiang", "Yu Guang Wang", "Liang Hong"], "categories": ["q-bio.QM", "cs.AI"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-06-08", "url": "https://arxiv.org/abs/2306.04899", "pdf_url": "https://arxiv.org/pdf/2306.04899v1", "arxiv_id": "2306.04899", "doi": "10.48550/arXiv.2306.04899", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "63c3cfddf3d9d87820a32183a90cf09eafbcb096448027be5e58cb8551352036", "sources": ["arxiv", "semantic_scholar"], "title": "Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation", "abstract": "The field of protein folding research has been greatly advanced by deep learning methods, with AlphaFold2 (AF2) demonstrating exceptional performance and atomic-level precision. As co-evolution is integral to protein structure prediction, AF2's accuracy is significantly influenced by the depth of multiple sequence alignment (MSA), which requires extensive exploration of a large protein database for similar sequences. However, not all protein sequences possess abundant homologous families, and consequently, AF2's performance can degrade on such queries, at times failing to produce meaningful results. To address this, we introduce a novel generative language model, MSA-Augmenter, which leverages protein-specific attention mechanisms and large-scale MSAs to generate useful, novel protein sequences not currently found in databases. These sequences supplement shallow MSAs, enhancing the accuracy of structural property predictions. Our experiments on CASP14 demonstrate that MSA-Augmenter can generate de novo sequences that retain co-evolutionary information from inferior MSAs, thereby improving protein structure prediction quality on top of strong AF2.", "authors": ["Le Zhang", "Jiayang Chen", "Tao Shen", "Yu Li", "Siqi Sun"], "categories": ["q-bio.QM", "cs.CE", "cs.LG", "q-bio.BM"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2023-06-02", "url": "https://arxiv.org/abs/2306.01824", "pdf_url": "https://arxiv.org/pdf/2306.01824v1", "arxiv_id": "2306.01824", "doi": "10.48550/arXiv.2306.01824", "citation_count": 10, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2603}
{"id": "826208b068406d914385cecfe2da016183938b219b28a5e1e965aff0d675800f", "sources": ["arxiv", "semantic_scholar"], "title": "AF2-Mutation: Adversarial Sequence Mutations against AlphaFold2 on Protein Tertiary Structure Prediction", "abstract": "Deep learning-based approaches, such as AlphaFold2 (AF2), have significantly advanced protein tertiary structure prediction, achieving results comparable to real biological experimental methods. While AF2 has shown limitations in predicting the effects of mutations, its robustness against sequence mutations remains to be determined. Starting with the wild-type (WT) sequence, we investigate adversarial sequences generated via an evolutionary approach, which AF2 predicts to be substantially different from WT. Our experiments on CASP14 reveal that by modifying merely three residues in the protein sequence using a combination of replacement, deletion, and insertion strategies, the alteration in AF2's predictions, as measured by the Local Distance Difference Test (lDDT), reaches 46.61. Moreover, when applied to a specific protein, SPNS2, our proposed algorithm successfully identifies biologically meaningful residues critical to protein structure determination and potentially indicates alternative conformations, thus significantly expediting the experimental process.", "authors": ["Zhongju Yuan", "Tao Shen", "Sheng Xu", "Leiye Yu", "Ruobing Ren", "Siqi Sun"], "categories": ["q-bio.BM", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-05-15", "url": "https://arxiv.org/abs/2305.08929", "pdf_url": "https://arxiv.org/pdf/2305.08929v1", "arxiv_id": "2305.08929", "doi": "10.15212/AMM-2024-0047", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Acta Materia Medica", "quality_score": 0.1505}
{"id": "ca66abe7fd1aecb6eef2a36463c4ba909d3ab02c2865600c8f029e17545e799e", "sources": ["arxiv", "semantic_scholar"], "title": "A Latent Diffusion Model for Protein Structure Generation", "abstract": "Proteins are complex biomolecules that perform a variety of crucial functions within living organisms. Designing and generating novel proteins can pave the way for many future synthetic biology applications, including drug discovery. However, it remains a challenging computational task due to the large modeling space of protein structures. In this study, we propose a latent diffusion model that can reduce the complexity of protein modeling while flexibly capturing the distribution of natural protein structures in a condensed latent space. Specifically, we propose an equivariant protein autoencoder that embeds proteins into a latent space and then uses an equivariant diffusion model to learn the distribution of the latent protein representations. Experimental results demonstrate that our method can effectively generate novel protein backbone structures with high designability and efficiency. The code will be made publicly available at https://github.com/divelab/AIRS/tree/main/OpenProt/LatentDiff", "authors": ["Cong Fu", "Keqiang Yan", "Limei Wang", "Wing Yee Au", "Michael McThrow", "Tao Komikado", "Koji Maruhashi", "Kanji Uchino", "Xiaoning Qian", "Shuiwang Ji"], "categories": ["q-bio.BM", "cs.AI", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2023-05-06", "url": "https://arxiv.org/abs/2305.04120", "pdf_url": "https://arxiv.org/pdf/2305.04120v2", "arxiv_id": "2305.04120", "doi": "10.48550/arXiv.2305.04120", "citation_count": 53, "influential_citation_count": 6, "has_code": true, "code_url": "https://github.com/divelab/AIRS/tree/main/OpenProt/LatentDiff", "venue": "LOG IN", "quality_score": 0.4331}
{"id": "5e658926c61771765f5f8a4b540e071e33e1584a0f9082dc6201cce81957c39f", "sources": ["arxiv", "semantic_scholar"], "title": "WizardLM: Empowering large pre-trained language models to follow complex instructions", "abstract": "Training large language models (LLMs) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming and labor-intensive. Moreover, humans may struggle to produce high-complexity instructions. In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans. Starting with an initial set of instructions, we use our proposed Evol-Instruct to rewrite them step by step into more complex instructions. Then, we mix all generated instruction data to fine-tune LLaMA. We call the resulting model WizardLM. Human evaluations on a complexity-balanced test bed and Vicuna's testset show that instructions from Evol-Instruct are superior to human-created ones. By analyzing the human evaluation results of the high complexity part, we demonstrate that outputs from our WizardLM are preferred to outputs from OpenAI ChatGPT. In GPT-4 automatic evaluation, WizardLM achieves more than 90\\% capacity of ChatGPT on 17 out of 29 skills. Even though WizardLM still lags behind ChatGPT in some aspects, our findings suggest that fine-tuning with AI-evolved instructions is a promising direction for enhancing LLMs. Our code and data are public at https://github.com/nlpxucan/WizardLM", "authors": ["Can Xu", "Qingfeng Sun", "Kai Zheng", "Xiubo Geng", "Pu Zhao", "Jiazhan Feng", "Chongyang Tao", "Qingwei Lin", "Daxin Jiang"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2023-04-24", "url": "https://arxiv.org/abs/2304.12244", "pdf_url": "https://arxiv.org/pdf/2304.12244v3", "arxiv_id": "2304.12244", "doi": null, "citation_count": 1242, "influential_citation_count": 157, "has_code": true, "code_url": "https://github.com/nlpxucan/WizardLM", "venue": "International Conference on Learning Representations", "quality_score": 1.0}
{"id": "0ed196163285eab0467f7a0d998ecc47a8db0eca79106e7654b36e96ef588789", "sources": ["arxiv", "semantic_scholar"], "title": "Learning to Plan with Natural Language", "abstract": "Large Language Models (LLMs) have shown remarkable performance in various basic natural language tasks. For completing the complex task, we still need a plan for the task to guide LLMs to generate the specific solutions step by step. LLMs can directly generate task plans, but these plans may still contain factual errors or are incomplete. A high-quality task plan contains correct step-by-step solutions for solving all situations and behavioral instructions for avoiding mistakes. To obtain it, we propose the Learning to Plan method, which involves two phases: (1) In the first learning task plan phase, it iteratively updates the task plan with new step-by-step solutions and behavioral instructions, which are obtained by prompting LLMs to derive from training error feedback. (2) In the subsequent test phase, the LLM uses the learned task plan to guide the inference of LLM on the test set. We demonstrate the effectiveness of our method on the five different reasoning type tasks (8 datasets). Further, our analysis experiment shows that the task plan learned by one LLM can directly guide another LLM to improve its performance, which reveals a new transfer learning paradigm. We release the code at \\url{https://github.com/Eureka6174/LearnNLPlan}", "authors": ["Yiduo Guo", "Yaobo Liang", "Chenfei Wu", "Wenshan Wu", "Dongyan Zhao", "Nan Duan"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-04-20", "url": "https://arxiv.org/abs/2304.10464", "pdf_url": "https://arxiv.org/pdf/2304.10464v4", "arxiv_id": "2304.10464", "doi": "10.18653/v1/2024.findings-emnlp.589", "citation_count": 6, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/Eureka6174/LearnNLPlan}", "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.2113}
{"id": "d91dab8c48e77153416e842c12a9f91acf7a94b2d2814c97d8a50815c3bdffa8", "sources": ["arxiv", "semantic_scholar"], "title": "CodeKGC: Code Language Model for Generative Knowledge Graph Construction", "abstract": "Current generative knowledge graph construction approaches usually fail to capture structural knowledge by simply flattening natural language into serialized texts or a specification language. However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structural prediction and reasoning tasks. Intuitively, we address the task of generative knowledge graph construction with code language model: given a code-format natural language input, the target is to generate triples which can be represented as code completion tasks. Specifically, we develop schema-aware prompts that effectively utilize the semantic structure within the knowledge graph. As code inherently possesses structure, such as class and function definitions, it serves as a useful model for prior semantic structural knowledge. Furthermore, we employ a rationale-enhanced generation method to boost the performance. Rationales provide intermediate steps, thereby improving knowledge extraction abilities. Experimental results indicate that the proposed approach can obtain better performance on benchmark datasets compared with baselines. Code and datasets are available in https://github.com/zjunlp/DeepKE/tree/main/example/llm.", "authors": ["Zhen Bi", "Jing Chen", "Yinuo Jiang", "Feiyu Xiong", "Wei Guo", "Huajun Chen", "Ningyu Zhang"], "categories": ["cs.CL", "cs.AI", "cs.IR", "cs.LG", "cs.SE"], "fields_of_study": ["Computer Science"], "published_date": "2023-04-18", "url": "https://arxiv.org/abs/2304.09048", "pdf_url": "https://arxiv.org/pdf/2304.09048v2", "arxiv_id": "2304.09048", "doi": "10.1145/3641850", "citation_count": 79, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/zjunlp/DeepKE/tree/main/example/llm", "venue": null, "quality_score": 0.4758}
{"id": "8e730bc2f932cdbe012acb9476304cc107bfaf3f1d08784b10395d3a91cf0771", "sources": ["arxiv", "semantic_scholar"], "title": "TemPL: A Novel Deep Learning Model for Zero-Shot Prediction of Protein Stability and Activity Based on Temperature-Guided Language Modeling", "abstract": "We introduce TemPL, a novel deep learning approach for zero-shot prediction of protein stability and activity, harnessing temperature-guided language modeling. By assembling an extensive dataset of 96 million sequence-host bacterial strain optimal growth temperatures (OGTs) and ΔTm data for point mutations under consistent experimental conditions, we effectively compared TemPL with state-of-the-art models. Notably, TemPL demonstrated superior performance in predicting protein stability. An ablation study was conducted to elucidate the influence of OGT prediction and language modeling modules on TemPL's performance, revealing the importance of integrating both components. Consequently, TemPL offers considerable promise for protein engineering applications, facilitating the design of mutation sequences with enhanced stability and activity.", "authors": ["Pan Tan", "Mingchen Li", "Liang Zhang", "Zhiqiang Hu", "Liang Hong"], "categories": ["q-bio.QM"], "fields_of_study": ["Biology"], "published_date": "2023-04-07", "url": "https://arxiv.org/abs/2304.03780", "pdf_url": "https://arxiv.org/pdf/2304.03780v5", "arxiv_id": "2304.03780", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "857a4a4ac4a085a6807b455b474a9dce1e8cdda74ab273b84807156e790b8bc4", "sources": ["arxiv", "semantic_scholar"], "title": "EigenFold: Generative Protein Structure Prediction with Diffusion Models", "abstract": "Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensembles and flexibility that underlie biological function. Towards this goal, we develop EigenFold, a diffusion generative modeling framework for sampling a distribution of structures from a given protein sequence. We define a diffusion process that models the structure as a system of harmonic oscillators and which naturally induces a cascading-resolution generative process along the eigenmodes of the system. On recent CAMEO targets, EigenFold achieves a median TMScore of 0.84, while providing a more comprehensive picture of model uncertainty via the ensemble of sampled structures relative to existing methods. We then assess EigenFold's ability to model and predict conformational heterogeneity for fold-switching proteins and ligand-induced conformational change. Code is available at https://github.com/bjing2016/EigenFold.", "authors": ["Bowen Jing", "Ezra Erives", "Peter Pao-Huang", "Gabriele Corso", "Bonnie Berger", "Tommi Jaakkola"], "categories": ["q-bio.BM", "cs.LG", "physics.bio-ph"], "fields_of_study": ["Medicine", "Computer Science", "Biology", "Physics"], "published_date": "2023-04-05", "url": "https://arxiv.org/abs/2304.02198", "pdf_url": "https://arxiv.org/pdf/2304.02198v1", "arxiv_id": "2304.02198", "doi": "10.48550/arXiv.2304.02198", "citation_count": 113, "influential_citation_count": 8, "has_code": true, "code_url": "https://github.com/bjing2016/EigenFold", "venue": "arXiv.org", "quality_score": 0.5142}
{"id": "2dd310656c8fb3999ce1aaf4b7290e0a58c6fafbe028bf8144cad36411718ed9", "sources": ["arxiv", "semantic_scholar"], "title": "ProtFIM: Fill-in-Middle Protein Sequence Design via Protein Language Models", "abstract": "Protein language models (pLMs), pre-trained via causal language modeling on protein sequences, have been a promising tool for protein sequence design. In real-world protein engineering, there are many cases where the amino acids in the middle of a protein sequence are optimized while maintaining other residues. Unfortunately, because of the left-to-right nature of pLMs, existing pLMs modify suffix residues by prompting prefix residues, which are insufficient for the infilling task that considers the whole surrounding context. To find the more effective pLMs for protein engineering, we design a new benchmark, Secondary structureE InFilling rEcoveRy, SEIFER, which approximates infilling sequence design scenarios. With the evaluation of existing models on the benchmark, we reveal the weakness of existing language models and show that language models trained via fill-in-middle transformation, called ProtFIM, are more appropriate for protein engineering. Also, we prove that ProtFIM generates protein sequences with decent protein representations through exhaustive experiments and visualizations.", "authors": ["Youhan Lee", "Hasun Yu"], "categories": ["cs.LG", "cs.AI", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-03-29", "url": "https://arxiv.org/abs/2303.16452", "pdf_url": "https://arxiv.org/pdf/2303.16452v1", "arxiv_id": "2303.16452", "doi": "10.48550/arXiv.2303.16452", "citation_count": 3, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "915e222b61c0bef59e854d3fd62e4291518687154163da3e2ec16d7959482f1b", "sources": ["arxiv", "semantic_scholar"], "title": "A Systematic Study of Joint Representation Learning on Protein Sequences and Structures", "abstract": "Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein functions. Recent sequence representation learning methods based on Protein Language Models (PLMs) excel in sequence-based tasks, but their direct adaptation to tasks involving protein structures remains a challenge. In contrast, structure-based methods leverage 3D structural information with graph neural networks and geometric pre-training methods show potential in function prediction tasks, but still suffers from the limited number of available structures. To bridge this gap, our study undertakes a comprehensive exploration of joint protein representation learning by integrating a state-of-the-art PLM (ESM-2) with distinct structure encoders (GVP, GearNet, CDConv). We introduce three representation fusion strategies and explore different pre-training techniques. Our method achieves significant improvements over existing sequence- and structure-based methods, setting new state-of-the-art for function annotation. This study underscores several important design choices for fusing protein sequence and structure information. Our implementation is available at https://github.com/DeepGraphLearning/ESM-GearNet.", "authors": ["Zuobai Zhang", "Chuanrui Wang", "Minghao Xu", "Vijil Chenthamarakshan", "Aurélie Lozano", "Payel Das", "Jian Tang"], "categories": ["q-bio.QM", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2023-03-11", "url": "https://arxiv.org/abs/2303.06275", "pdf_url": "https://arxiv.org/pdf/2303.06275v2", "arxiv_id": "2303.06275", "doi": null, "citation_count": 55, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/DeepGraphLearning/ESM-GearNet", "venue": null, "quality_score": 0.437}
{"id": "265864d7ea56e774037ffd50ec4cdd100527f4b142b37cf6a55cc23f531d3ab6", "sources": ["arxiv", "semantic_scholar"], "title": "Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters", "abstract": "After the recent ground-breaking advances in protein structure prediction, one of the remaining challenges in protein machine learning is to reliably predict distributions of structural states. Parametric models of fluctuations are difficult to fit due to complex covariance structures between degrees of freedom in the protein chain, often causing models to either violate local or global structural constraints. In this paper, we present a new strategy for modelling protein densities in internal coordinates, which uses constraints in 3D space to induce covariance structure between the internal degrees of freedom. We illustrate the potential of the procedure by constructing a variational autoencoder with full covariance output induced by the constraints implied by the conditional mean in 3D, and demonstrate that our approach makes it possible to scale density models of internal coordinates to full protein backbones in two settings: 1) a unimodal setting for proteins exhibiting small fluctuations and limited amounts of available data, and 2) a multimodal setting for larger conformational changes in a high data regime.", "authors": ["Marloes Arts", "Jes Frellsen", "Wouter Boomsma"], "categories": ["cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-02-27", "url": "https://arxiv.org/abs/2302.13711", "pdf_url": "https://arxiv.org/pdf/2302.13711v3", "arxiv_id": "2302.13711", "doi": "10.48550/arXiv.2302.13711", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1193}
{"id": "e711c63d02730493c9bccf6ebcd9a4514e362de50633fb6e19e0b0a613ddccc2", "sources": ["arxiv", "semantic_scholar"], "title": "Retrieved Sequence Augmentation for Protein Representation Learning", "abstract": "Protein language models have excelled in a variety of tasks, ranging from structure prediction to protein engineering. However, proteins are highly diverse in functions and structures, and current state-of-the-art models including the latest version of AlphaFold rely on Multiple Sequence Alignments (MSA) to feed in the evolutionary knowledge. Despite their success, heavy computational overheads, as well as the de novo and orphan proteins remain great challenges in protein representation learning. In this work, we show that MSAaugmented models inherently belong to retrievalaugmented methods. Motivated by this finding, we introduce Retrieved Sequence Augmentation(RSA) for protein representation learning without additional alignment or pre-processing. RSA links query protein sequences to a set of sequences with similar structures or properties in the database and combines these sequences for downstream prediction. We show that protein language models benefit from the retrieval enhancement on both structure prediction and property prediction tasks, with a 5% improvement on MSA Transformer on average while being 373 times faster. In addition, we show that our model can transfer to new protein domains better and outperforms MSA Transformer on de novo protein prediction. Our study fills a much-encountered gap in protein prediction and brings us a step closer to demystifying the domain knowledge needed to understand protein sequences. Code is available on https://github.com/HKUNLP/RSA.", "authors": ["Chang Ma", "Haiteng Zhao", "Lin Zheng", "Jiayi Xin", "Qintong Li", "Lijun Wu", "Zhihong Deng", "Yang Lu", "Qi Liu", "Lingpeng Kong"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-02-24", "url": "https://arxiv.org/abs/2302.12563", "pdf_url": "https://arxiv.org/pdf/2302.12563v1", "arxiv_id": "2302.12563", "doi": "10.1101/2023.02.22.529597", "citation_count": 16, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/HKUNLP/RSA", "venue": "bioRxiv", "quality_score": 0.3076}
{"id": "332cd44edf4a08a6edb142e70bbfa5061a9e02b59af2dfdcdc8282dacfe0378f", "sources": ["arxiv", "semantic_scholar"], "title": "Semantic Importance-Aware Communications Using Pre-trained Language Models", "abstract": "This letter proposes a semantic importance-aware communication (SIAC) scheme using pre-trained language models (e.g., ChatGPT, BERT, etc.). Specifically, we propose a cross-layer design with a pre-trained language model embedded in/connected by the cross-layer manager. The pre-trained language model is utilized to quantify the semantic importance of data frames. Based on the quantified semantic importance, we investigate semantic importance-aware power allocation. Unlike existing deep joint source-channel coding (Deep-JSCC)-based semantic communication schemes, SIAC can be directly embedded into current communication systems by only introducing a cross-layer manager. Our experimental results show that the proposed SIAC scheme can achieve lower semantic loss than existing equal-priority communications.", "authors": ["Shuaishuai Guo", "Yanhu Wang", "Shujing Li", "Nasir Saeed"], "categories": ["eess.SP"], "fields_of_study": ["Engineering", "Computer Science"], "published_date": "2023-02-12", "url": "https://arxiv.org/abs/2302.07142", "pdf_url": "https://arxiv.org/pdf/2302.07142v2", "arxiv_id": "2302.07142", "doi": "10.1109/LCOMM.2023.3293805", "citation_count": 72, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "IEEE Communications Letters", "quality_score": 0.4658}
{"id": "351e7cd29bedcfe9e36e020f68c303011cc060dca1e0bcf48418d1fdc335610e", "sources": ["arxiv", "semantic_scholar"], "title": "NodeCoder: a graph-based machine learning platform to predict active sites of modeled protein structures", "abstract": "While accurate protein structure predictions are now available for nearly every observed protein sequence, predicted structures lack much of the functional context offered by experimental structure determination. We address this gap with NodeCoder, a task-independent platform that maps residue-based datasets onto 3D protein structures, embeds the resulting structural feature into a contact network, and models residue classification tasks with a Graph Convolutional Network (GCN). We demonstrate the versatility of this strategy by modeling six separate tasks, with some labels derived from other experimental structure studies (ligand, peptide, ion, and nucleic acid binding sites) and other labels derived from annotation databases (post-translational modification and transmembrane regions). Moreover, A NodeCoder model trained to identify ligand binding site residues was able to outperform P2Rank, a widely-used software developed specifically for ligand binding site detection. NodeCoder is available as an open-source python package at https://pypi.org/project/NodeCoder/.", "authors": ["Nasim Abdollahi", "Seyed Ali Madani Tonekaboni", "Jay Huang", "Bo Wang", "Stephen MacKinnon"], "categories": ["q-bio.QM"], "fields_of_study": ["Biology"], "published_date": "2023-02-07", "url": "https://arxiv.org/abs/2302.03590", "pdf_url": "https://arxiv.org/pdf/2302.03590v1", "arxiv_id": "2302.03590", "doi": null, "citation_count": 12, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": null, "quality_score": 0.2785}
{"id": "37c266d9e09e2439b108e4d14fc54ecc89eba6d781351cd51062cfddd7e323df", "sources": ["arxiv", "semantic_scholar"], "title": "Structure-informed Language Models Are Protein Designers", "abstract": "This paper demonstrates that language models are strong structure-based protein designers. We present LM-Design, a generic approach to reprogramming sequence-based protein language models (pLMs), that have learned massive sequential evolutionary knowledge from the universe of natural protein sequences, to acquire an immediate capability to design preferable protein sequences for given folds. We conduct a structural surgery on pLMs, where a lightweight structural adapter is implanted into pLMs and endows it with structural awareness. During inference, iterative refinement is performed to effectively optimize the generated protein sequences. Experiments show that LM-Design improves the state-of-the-art results by a large margin, leading to up to 4% to 12% accuracy gains in sequence recovery (e.g., 55.65%/56.63% on CATH 4.2/4.3 single-chain benchmarks, and >60% when designing protein complexes). We provide extensive and in-depth analyses, which verify that LM-Design can (1) indeed leverage both structural and sequential knowledge to accurately handle structurally non-deterministic regions, (2) benefit from scaling data and model size, and (3) generalize to other proteins (e.g., antibodies and de novo proteins)", "authors": ["Zaixiang Zheng", "Yifan Deng", "Dongyu Xue", "Yi Zhou", "Fei YE", "Quanquan Gu"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-02-03", "url": "https://arxiv.org/abs/2302.01649", "pdf_url": "https://arxiv.org/pdf/2302.01649v2", "arxiv_id": "2302.01649", "doi": "10.1101/2023.02.03.526917", "citation_count": 135, "influential_citation_count": 18, "has_code": false, "code_url": null, "venue": "bioRxiv", "quality_score": 0.6394}
{"id": "1a03220774dedc43bd6a335c63763e0b440f4b53783242963ef4211bee9c7e61", "sources": ["arxiv", "semantic_scholar"], "title": "ExplainableFold: Understanding AlphaFold Prediction with Explainable AI", "abstract": "This paper presents ExplainableFold, an explainable AI framework for protein structure prediction. Despite the success of AI-based methods such as AlphaFold in this field, the underlying reasons for their predictions remain unclear due to the black-box nature of deep learning models. To address this, we propose a counterfactual learning framework inspired by biological principles to generate counterfactual explanations for protein structure prediction, enabling a dry-lab experimentation approach. Our experimental results demonstrate the ability of ExplainableFold to generate high-quality explanations for AlphaFold's predictions, providing near-experimental understanding of the effects of amino acids on 3D protein structure. This framework has the potential to facilitate a deeper understanding of protein structures.", "authors": ["Juntao Tan", "Yongfeng Zhang"], "categories": ["cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-27", "url": "https://arxiv.org/abs/2301.11765", "pdf_url": "https://arxiv.org/pdf/2301.11765v2", "arxiv_id": "2301.11765", "doi": "10.1145/3580305.3599337", "citation_count": 17, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Knowledge Discovery and Data Mining", "quality_score": 0.3138}
{"id": "9a3dfa7c8dff84582d41500dae10b4885994cbad359cbcb6d45a2f144d971f03", "sources": ["arxiv", "semantic_scholar"], "title": "DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraints", "abstract": "Have you ever been troubled by the complexity and computational cost of SE(3) protein structure modeling and been amazed by the simplicity and power of language modeling? Recent work has shown promise in simplifying protein structures as sequences of protein angles; therefore, language models could be used for unconstrained protein backbone generation. Unfortunately, such simplification is unsuitable for the constrained protein inpainting problem, where the model needs to recover masked structures conditioned on unmasked ones, as it dramatically increases the computing cost of geometric constraints. To overcome this dilemma, we suggest inserting a hidden \\textbf{a}tomic \\textbf{d}irection \\textbf{s}pace (\\textbf{ADS}) upon the language model, converting invariant backbone angles into equivalent direction vectors and preserving the simplicity, called Seq2Direct encoder ($\\text{Enc}_{s2d}$). Geometric constraints could be efficiently imposed on the newly introduced direction space. A Direct2Seq decoder ($\\text{Dec}_{d2s}$) with mathematical guarantees is also introduced to develop a \\textbf{SDS} ($\\text{Enc}_{s2d}$+$\\text{Dec}_{d2s}$) model. We apply the SDS model as the denoising neural network during the conditional diffusion process, resulting in a constrained generative model--\\textbf{DiffSDS}. Extensive experiments show that the plug-and-play ADS could transform the language model into a strong structural model without loss of simplicity. More importantly, the proposed DiffSDS outperforms previous strong baselines by a large margin on the task of protein inpainting.", "authors": ["Zhangyang Gao", "Cheng Tan", "Stan Z. Li"], "categories": ["q-bio.QM", "cs.AI", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2023-01-22", "url": "https://arxiv.org/abs/2301.09642", "pdf_url": "https://arxiv.org/pdf/2301.09642v1", "arxiv_id": "2301.09642", "doi": "10.48550/arXiv.2301.09642", "citation_count": 23, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3451}
{"id": "8d5732ad139fa97e20570796913359595778cd656df554dd79b7d96f76376d7c", "sources": ["arxiv", "semantic_scholar"], "title": "Beating the Best: Improving on AlphaFold2 at Protein Structure Prediction", "abstract": "The goal of Protein Structure Prediction (PSP) problem is to predict a protein's 3D structure (confirmation) from its amino acid sequence. The problem has been a 'holy grail' of science since the Noble prize-winning work of Anfinsen demonstrated that protein conformation was determined by sequence. A recent and important step towards this goal was the development of AlphaFold2, currently the best PSP method. AlphaFold2 is probably the highest profile application of AI to science. Both AlphaFold2 and RoseTTAFold (another impressive PSP method) have been published and placed in the public domain (code & models). Stacking is a form of ensemble machine learning ML in which multiple baseline models are first learnt, then a meta-model is learnt using the outputs of the baseline level model to form a model that outperforms the base models. Stacking has been successful in many applications. We developed the ARStack PSP method by stacking AlphaFold2 and RoseTTAFold. ARStack significantly outperforms AlphaFold2. We rigorously demonstrate this using two sets of non-homologous proteins, and a test set of protein structures published after that of AlphaFold2 and RoseTTAFold. As more high quality prediction methods are published it is likely that ensemble methods will increasingly outperform any single method.", "authors": ["Abbi Abdel-Rehim", "Oghenejokpeme Orhobor", "Hang Lou", "Hao Ni", "Ross D. King"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-01-18", "url": "https://arxiv.org/abs/2301.07568", "pdf_url": "https://arxiv.org/pdf/2301.07568v2", "arxiv_id": "2301.07568", "doi": "10.48550/arXiv.2301.07568", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "a0c86642b0e6fe1acba01927179c2770a0f3882b5f4fe8764d543f3b58a73767", "sources": ["arxiv", "semantic_scholar"], "title": "Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling", "abstract": "As opposed to scaling-up protein language models (PLMs), we seek improving performance via protein-specific optimization. Although the proportionality between the language model size and the richness of its learned representations is validated, we prioritize accessibility and pursue a path of data-efficient, cost-reduced, and knowledge-guided optimization. Through over twenty experiments ranging from masking, architecture, and pre-training data, we derive insights from protein-specific experimentation into building a model that interprets the language of life, optimally. We present Ankh, the first general-purpose PLM trained on Google's TPU-v4 surpassing the state-of-the-art performance with fewer parameters (<10% for pre-training, <7% for inference, and <30% for the embedding dimension). We provide a representative range of structure and function benchmarks where Ankh excels. We further provide a protein variant generation analysis on High-N and One-N input data scales where Ankh succeeds in learning protein evolutionary conservation-mutation trends and introducing functional diversity while retaining key structural-functional characteristics. We dedicate our work to promoting accessibility to research innovation via attainable resources.", "authors": ["Ahmed Elnaggar", "Hazem Essam", "Wafaa Salah-Eldin", "Walid Moustafa", "Mohamed Elkerdawy", "Charlotte Rochereau", "Burkhard Rost"], "categories": ["cs.LG", "cs.CL", "cs.DC", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-01-16", "url": "https://arxiv.org/abs/2301.06568", "pdf_url": "https://arxiv.org/pdf/2301.06568v1", "arxiv_id": "2301.06568", "doi": "10.1101/2023.01.16.524265", "citation_count": 164, "influential_citation_count": 20, "has_code": false, "code_url": null, "venue": "bioRxiv", "quality_score": 0.6611}
{"id": "575636c973e45a311c58f9bcec1f8853f7e94918eff7795368104aeb715e43e6", "sources": ["arxiv", "semantic_scholar"], "title": "Language Cognition and Language Computation -- Human and Machine Language Understanding", "abstract": "Language understanding is a key scientific issue in the fields of cognitive and computer science. However, the two disciplines differ substantially in the specific research questions. Cognitive science focuses on analyzing the specific mechanism of the brain and investigating the brain's response to language; few studies have examined the brain's language system as a whole. By contrast, computer scientists focus on the efficiency of practical applications when choosing research questions but may ignore the most essential laws of language. Given these differences, can a combination of the disciplines offer new insights for building intelligent language models and studying language cognitive mechanisms? In the following text, we first review the research questions, history, and methods of language understanding in cognitive and computer science, focusing on the current progress and challenges. We then compare and contrast the research of language understanding in cognitive and computer sciences. Finally, we review existing work that combines insights from language cognition and language computation and offer prospects for future development trends.", "authors": ["Shaonan Wang", "Nai Ding", "Nan Lin", "Jiajun Zhang", "Chengqing Zong"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2023-01-12", "url": "https://arxiv.org/abs/2301.04788", "pdf_url": "https://arxiv.org/pdf/2301.04788v1", "arxiv_id": "2301.04788", "doi": "10.48550/arXiv.2301.04788", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "953e7e550921c206f7c2c18ac0082d82baeaa66969cbdc7f28b88ce0b9a59774", "sources": ["arxiv", "semantic_scholar"], "title": "On the Robustness of AlphaFold: A COVID-19 Case Study", "abstract": "Protein folding neural networks (PFNNs) such as AlphaFold predict remarkably accurate structures of proteins compared to other approaches. However, the robustness of such networks has heretofore not been explored. This is particularly relevant given the broad social implications of such technologies and the fact that biologically small perturbations in the protein sequence do not generally lead to drastic changes in the protein structure. In this paper, we demonstrate that AlphaFold does not exhibit such robustness despite its high accuracy. This raises the challenge of detecting and quantifying the extent to which these predicted protein structures can be trusted. To measure the robustness of the predicted structures, we utilize (i) the root-mean-square deviation (RMSD) and (ii) the Global Distance Test (GDT) similarity measure between the predicted structure of the original sequence and the structure of its adversarially perturbed version. We prove that the problem of minimally perturbing protein sequences to fool protein folding neural networks is NP-complete. Based on the well-established BLOSUM62 sequence alignment scoring matrix, we generate adversarial protein sequences and show that the RMSD between the predicted protein structure and the structure of the original sequence are very large when the adversarial changes are bounded by (i) 20 units in the BLOSUM62 distance, and (ii) five residues (out of hundreds or thousands of residues) in the given protein sequence. In our experimental evaluation, we consider 111 COVID-19 proteins in the Universal Protein resource (UniProt), a central resource for protein data managed by the European Bioinformatics Institute, Swiss Institute of Bioinformatics, and the US Protein Information Resource. These result in an overall GDT similarity test score average of around 34%, demonstrating a substantial drop in the performance of AlphaFold.", "authors": ["Ismail Alkhouri", "Sumit Jha", "Andre Beckus", "George Atia", "Alvaro Velasquez", "Rickard Ewetz", "Arvind Ramanathan", "Susmit Jha"], "categories": ["cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-01-10", "url": "https://arxiv.org/abs/2301.04093", "pdf_url": "https://arxiv.org/pdf/2301.04093v2", "arxiv_id": "2301.04093", "doi": "10.48550/arXiv.2301.04093", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "928f3f4525621e5db803a031ef35d62857b6857ad121282b651261ce42314057", "sources": ["arxiv", "semantic_scholar"], "title": "Reprogramming Pretrained Language Models for Protein Sequence Representation Learning", "abstract": "Machine Learning-guided solutions for protein learning tasks have made significant headway in recent years. However, success in scientific discovery tasks is limited by the accessibility of well-defined and labeled in-domain data. To tackle the low-data constraint, recent adaptions of deep learning models pretrained on millions of protein sequences have shown promise; however, the construction of such domain-specific large-scale model is computationally expensive. Here, we propose Representation Learning via Dictionary Learning (R2DL), an end-to-end representation learning framework in which we reprogram deep models for alternate-domain tasks that can perform well on protein property prediction with significantly fewer training samples. R2DL reprograms a pretrained English language model to learn the embeddings of protein sequences, by learning a sparse linear mapping between English and protein sequence vocabulary embeddings. Our model can attain better accuracy and significantly improve the data efficiency by up to $10^5$ times over the baselines set by pretrained and standard supervised methods. To this end, we reprogram an off-the-shelf pre-trained English language transformer and benchmark it on a set of protein physicochemical prediction tasks (secondary structure, stability, homology, stability) as well as on a biomedically relevant set of protein function prediction tasks (antimicrobial, toxicity, antibody affinity).", "authors": ["Ria Vinod", "Pin-Yu Chen", "Payel Das"], "categories": ["cs.LG", "cs.CL", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2023-01-05", "url": "https://arxiv.org/abs/2301.02120", "pdf_url": "https://arxiv.org/pdf/2301.02120v1", "arxiv_id": "2301.02120", "doi": "10.48550/arXiv.2301.02120", "citation_count": 15, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Digital Discovery", "quality_score": 0.301}
{"id": "a2050f76f86ba35126c48e4a7275cf589261d7d749ce50a1fac194d248e0eebf", "sources": ["arxiv", "semantic_scholar"], "title": "Protein Structure Prediction until CASP15", "abstract": "In Dec 2020, the results of AlphaFold2 were presented at CASP14, sparking a revolution in the field of protein structure predictions. For the first time, a purely computational method could challenge experimental accuracy for structure prediction of single protein domains. The code of AlphaFold2 was released in the summer of 2021, and since then, it has been shown that it can be used to accurately predict the structure of most (ordered) proteins and many protein-protein interactions. It has also sparked an explosion of development in the field, improving AI-based methods to predict protein complexes, disordered regions, and protein design. Here I will review some of the inventions sparked by the release of AlphaFold.", "authors": ["Arne Elofsson"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2022-12-15", "url": "https://arxiv.org/abs/2212.07702", "pdf_url": "https://arxiv.org/pdf/2212.07702v1", "arxiv_id": "2212.07702", "doi": null, "citation_count": 2, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1505}
{"id": "f6a47fd22a360d472371af672a4dd292223f8b1f700f20e1a559e343b5b68308", "sources": ["arxiv", "semantic_scholar"], "title": "Prompting Is Programming: A Query Language for Large Language Models", "abstract": "Large language models have demonstrated outstanding performance on a wide range of tasks such as question answering and code generation. On a high level, given an input, a language model can be used to automatically complete the sequence in a statistically-likely way. Based on this, users prompt these models with language instructions or examples, to implement a variety of downstream tasks. Advanced prompting methods can even imply interaction between the language model, a user, and external tools such as calculators. However, to obtain state-of-the-art performance or adapt language models for specific tasks, complex task- and model-specific programs have to be implemented, which may still require ad-hoc interaction. Based on this, we present the novel idea of Language Model Programming (LMP). LMP generalizes language model prompting from pure text prompts to an intuitive combination of text prompting and scripting. Additionally, LMP allows constraints to be specified over the language model output. This enables easy adaption to many tasks while abstracting language model internals and providing high-level semantics. To enable LMP, we implement LMQL(short for Language Model Query Language), which leverages the constraints and control flow from an LMP prompt to generate an efficient inference procedure that minimizes the number of expensive calls to the underlying language model. We show that LMQL can capture a wide range of state-of-the-art prompting methods in an intuitive way, especially facilitating interactive flows that are challenging to implement with existing high-level APIs. Our evaluation shows that we retain or increase the accuracy on several downstream tasks, while also significantly reducing the required amount of computation or cost in the case of pay-to-use APIs (26-85% cost savings).", "authors": ["Luca Beurer-Kellner", "Marc Fischer", "Martin Vechev"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-12-12", "url": "https://arxiv.org/abs/2212.06094", "pdf_url": "https://arxiv.org/pdf/2212.06094v3", "arxiv_id": "2212.06094", "doi": "10.1145/3591300", "citation_count": 185, "influential_citation_count": 9, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.5674}
{"id": "8554e1c04943e8622214fd3d8a51082be48fd5911202bbcbeeaccace9ab7fcc3", "sources": ["arxiv", "semantic_scholar"], "title": "Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks", "abstract": "Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area. However, its efficacy is largely constrained due to the limited quantity of structural data. Meanwhile, protein language models trained on substantial 1D sequences have shown burgeoning capabilities with scale in a broad range of applications. Several previous studies consider combining these different protein modalities to promote the representation power of geometric neural networks, but fail to present a comprehensive understanding of their benefits. In this work, we integrate the knowledge learned by well-trained protein language models into several state-of-the-art geometric networks and evaluate a variety of protein representation learning benchmarks, including protein-protein interface prediction, model quality assessment, protein-protein rigid-body docking, and binding affinity prediction. Our findings show an overall improvement of 20% over baselines. Strong evidence indicates that the incorporation of protein language models' knowledge enhances geometric networks' capacity by a significant margin and can be generalized to complex tasks.", "authors": ["Fang Wu", "Lirong Wu", "Dragomir Radev", "Jinbo Xu", "Stan Z. Li"], "categories": ["cs.LG", "cs.CE", "q-bio.QM"], "fields_of_study": ["Medicine", "Computer Science", "Biology"], "published_date": "2022-12-07", "url": "https://arxiv.org/abs/2212.03447", "pdf_url": "https://arxiv.org/pdf/2212.03447v2", "arxiv_id": "2212.03447", "doi": "10.1038/s42003-023-05133-1", "citation_count": 57, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "Communications Biology", "quality_score": 0.4409}
{"id": "2e6d2e27a38c18fc2ef05ab6864b2d28c1370cd326e316afef3112731963912e", "sources": ["arxiv", "semantic_scholar"], "title": "SOLD: Sinhala Offensive Language Dataset", "abstract": "The widespread of offensive content online, such as hate speech and cyber-bullying, is a global phenomenon. This has sparked interest in the artificial intelligence (AI) and natural language processing (NLP) communities, motivating the development of various systems trained to detect potentially harmful content automatically. These systems require annotated datasets to train the machine learning (ML) models. However, with a few notable exceptions, most datasets on this topic have dealt with English and a few other high-resource languages. As a result, the research in offensive language identification has been limited to these languages. This paper addresses this gap by tackling offensive language identification in Sinhala, a low-resource Indo-Aryan language spoken by over 17 million people in Sri Lanka. We introduce the Sinhala Offensive Language Dataset (SOLD) and present multiple experiments on this dataset. SOLD is a manually annotated dataset containing 10,000 posts from Twitter annotated as offensive and not offensive at both sentence-level and token-level, improving the explainability of the ML models. SOLD is the first large publicly available offensive language dataset compiled for Sinhala. We also introduce SemiSOLD, a larger dataset containing more than 145,000 Sinhala tweets, annotated following a semi-supervised approach.", "authors": ["Tharindu Ranasinghe", "Isuri Anuradha", "Damith Premasiri", "Kanishka Silva", "Hansi Hettiarachchi", "Lasitha Uyangodage", "Marcos Zampieri"], "categories": ["cs.CL", "cs.AI", "cs.LG", "cs.SI"], "fields_of_study": ["Computer Science"], "published_date": "2022-12-01", "url": "https://arxiv.org/abs/2212.00851", "pdf_url": "https://arxiv.org/pdf/2212.00851v2", "arxiv_id": "2212.00851", "doi": "10.1007/s10579-024-09723-1", "citation_count": 17, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Language Resources and Evaluation", "quality_score": 0.3138}
{"id": "5ae73fbfbaf7540d294d33e044e1ccaf4312a5f11a528739894023a584701e35", "sources": ["arxiv", "semantic_scholar"], "title": "Protein Language Models and Structure Prediction: Connection and Progression", "abstract": "The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding. Recent advances have proved the power of language models (LMs) in processing the protein sequence databases, which inherit the advantages of attention networks and capture useful information in learning representations for proteins. The past two years have witnessed remarkable success in tertiary protein structure prediction (PSP), including evolution-based and single-sequence-based PSP. It seems that instead of using energy-based models and sampling procedures, protein language model (pLM)-based pipelines have emerged as mainstream paradigms in PSP. Despite the fruitful progress, the PSP community needs a systematic and up-to-date survey to help bridge the gap between LMs in the natural language processing (NLP) and PSP domains and introduce their methodologies, advancements and practical applications. To this end, in this paper, we first introduce the similarities between protein and human languages that allow LMs extended to pLMs, and applied to protein databases. Then, we systematically review recent advances in LMs and pLMs from the perspectives of network architectures, pre-training strategies, applications, and commonly-used protein databases. Next, different types of methods for PSP are discussed, particularly how the pLM-based architectures function in the process of protein folding. Finally, we identify challenges faced by the PSP community and foresee promising research directions along with the advances of pLMs. This survey aims to be a hands-on guide for researchers to understand PSP methods, develop pLMs and tackle challenging problems in this field for practical purposes.", "authors": ["Bozhen Hu", "Jun Xia", "Jiangbin Zheng", "Cheng Tan", "Yufei Huang", "Yongjie Xu", "Stan Z. Li"], "categories": ["q-bio.QM", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2022-11-30", "url": "https://arxiv.org/abs/2211.16742", "pdf_url": "https://arxiv.org/pdf/2211.16742v1", "arxiv_id": "2211.16742", "doi": "10.48550/arXiv.2211.16742", "citation_count": 48, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4225}
{"id": "a56754ee70f93f68585f5d2416bd38792228ba9c3fc24f1eff8db4d3dadbecb3", "sources": ["arxiv", "semantic_scholar"], "title": "An Overview of Indian Spoken Language Recognition from Machine Learning Perspective", "abstract": "Automatic spoken language identification (LID) is a very important research field in the era of multilingual voice-command-based human-computer interaction (HCI). A front-end LID module helps to improve the performance of many speech-based applications in the multilingual scenario. India is a populous country with diverse cultures and languages. The majority of the Indian population needs to use their respective native languages for verbal interaction with machines. Therefore, the development of efficient Indian spoken language recognition systems is useful for adapting smart technologies in every section of Indian society. The field of Indian LID has started gaining momentum in the last two decades, mainly due to the development of several standard multilingual speech corpora for the Indian languages. Even though significant research progress has already been made in this field, to the best of our knowledge, there are not many attempts to analytically review them collectively. In this work, we have conducted one of the very first attempts to present a comprehensive review of the Indian spoken language recognition research field. In-depth analysis has been presented to emphasize the unique challenges of low-resource and mutual influences for developing LID systems in the Indian contexts. Several essential aspects of the Indian LID research, such as the detailed description of the available speech corpora, the major research contributions, including the earlier attempts based on statistical modeling to the recent approaches based on different neural network architectures, and the future research trends are discussed. This review work will help assess the state of the present Indian LID research by any active researcher or any research enthusiasts from related fields.", "authors": ["Spandan Dey", "Md Sahidullah", "Goutam Saha"], "categories": ["cs.CL", "cs.SD", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2022-11-30", "url": "https://arxiv.org/abs/2212.03812", "pdf_url": "https://arxiv.org/pdf/2212.03812v1", "arxiv_id": "2212.03812", "doi": "10.1145/3523179", "citation_count": 38, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 21, Issue 6 November 2022, Article No 128", "quality_score": 0.3978}
{"id": "b9228f7f478df02b4b96c0d3ea6880d2d7a0e81ff75eebc59f812b450d6f8fad", "sources": ["arxiv", "semantic_scholar"], "title": "Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction", "abstract": "A central problem in computational biophysics is protein structure prediction, i.e., finding the optimal folding of a given amino acid sequence. This problem has been studied in a classical abstract model, the HP model, where the protein is modeled as a sequence of H (hydrophobic) and P (polar) amino acids on a lattice. The objective is to find conformations maximizing H-H contacts. It is known that even in this reduced setting, the problem is intractable (NP-hard). In this work, we apply deep reinforcement learning (DRL) to the two-dimensional HP model. We can obtain the conformations of best known energies for benchmark HP sequences with lengths from 20 to 50. Our DRL is based on a deep Q-network (DQN). We find that a DQN based on long short-term memory (LSTM) architecture greatly enhances the RL learning ability and significantly improves the search process. DRL can sample the state space efficiently, without the need of manual heuristics. Experimentally we show that it can find multiple distinct best-known solutions per trial. This study demonstrates the effectiveness of deep reinforcement learning in the HP model for protein folding.", "authors": ["Kaiyuan Yang", "Houjing Huang", "Olafs Vandans", "Adithya Murali", "Fujia Tian", "Roland H. C. Yap", "Liang Dai"], "categories": ["cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2022-11-27", "url": "https://arxiv.org/abs/2211.14939", "pdf_url": "https://arxiv.org/pdf/2211.14939v2", "arxiv_id": "2211.14939", "doi": "10.1016/j.physa.2022.128395", "citation_count": 14, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.294}
{"id": "b9a2b2e3d509350fb9f5331a15305d87b1d032893a9d1e34c5f5f8682b99f543", "sources": ["arxiv", "semantic_scholar"], "title": "Protein language model rescue mutations highlight variant effects and structure in clinically relevant genes", "abstract": "Despite being self-supervised, protein language models have shown remarkable performance in fundamental biological tasks such as predicting impact of genetic variation on protein structure and function. The effectiveness of these models on diverse set of tasks suggests that they learn meaningful representations of fitness landscape that can be useful for downstream clinical applications. Here, we interrogate the use of these language models in characterizing known pathogenic mutations in curated, medically actionable genes through an exhaustive search of putative compensatory mutations on each variant's genetic background. Systematic analysis of the predicted effects of these compensatory mutations reveal unappreciated structural features of proteins that are missed by other structure predictors like AlphaFold. While deep mutational scan experiments provide an unbiased estimate of the mutational landscape, we encourage the community to generate and curate rescue mutation experiments to inform the design of more sophisticated co-masking strategies and leverage large language models more effectively for downstream clinical prediction tasks.", "authors": ["Onuralp Soylemez", "Pablo Cordero"], "categories": ["cs.LG", "q-bio.GN"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2022-11-18", "url": "https://arxiv.org/abs/2211.10000", "pdf_url": "https://arxiv.org/pdf/2211.10000v1", "arxiv_id": "2211.10000", "doi": "10.48550/arXiv.2211.10000", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "b3fdc3af0edaf9bf6983d3aaaf40e26128a215450371297214352fb232c3b4e1", "sources": ["arxiv", "semantic_scholar"], "title": "Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer", "abstract": "Outcome prediction is crucial for head and neck cancer patients as it can provide prognostic information for early treatment planning. Radiomics methods have been widely used for outcome prediction from medical images. However, these methods are limited by their reliance on intractable manual segmentation of tumor regions. Recently, deep learning methods have been proposed to perform end-to-end outcome prediction so as to remove the reliance on manual segmentation. Unfortunately, without segmentation masks, these methods will take the whole image as input, such that makes them difficult to focus on tumor regions and potentially unable to fully leverage the prognostic information within the tumor regions. In this study, we propose a radiomics-enhanced deep multi-task framework for outcome prediction from PET/CT images, in the context of HEad and neCK TumOR segmentation and outcome prediction challenge (HECKTOR 2022). In our framework, our novelty is to incorporate radiomics as an enhancement to our recently proposed Deep Multi-task Survival model (DeepMTS). The DeepMTS jointly learns to predict the survival risk scores of patients and the segmentation masks of tumor regions. Radiomics features are extracted from the predicted tumor regions and combined with the predicted survival risk scores for final outcome prediction, through which the prognostic information in tumor regions can be further leveraged. Our method achieved a C-index of 0.681 on the testing set, placing the 2nd on the leaderboard with only 0.00068 lower in C-index than the 1st place.", "authors": ["Mingyuan Meng", "Lei Bi", "Dagan Feng", "Jinman Kim"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2022-11-10", "url": "https://arxiv.org/abs/2211.05409", "pdf_url": "https://arxiv.org/pdf/2211.05409v1", "arxiv_id": "2211.05409", "doi": "10.1007/978-3-031-27420-6_14", "citation_count": 20, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Head and Neck Tumor Segmentation and Outcome Prediction (HECKTOR 2022), pp.135-143", "quality_score": 0.3306}
{"id": "8027e5dae72ae373ff3ff3eac87cd7f973aa54cf2fd4a1b4b019dfe87a4adfab", "sources": ["arxiv", "semantic_scholar"], "title": "AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages", "abstract": "In recent years, multilingual pre-trained language models have gained prominence due to their remarkable performance on numerous downstream Natural Language Processing tasks (NLP). However, pre-training these large multilingual language models requires a lot of training data, which is not available for African Languages. Active learning is a semi-supervised learning algorithm, in which a model consistently and dynamically learns to identify the most beneficial samples to train itself on, in order to achieve better optimization and performance on downstream tasks. Furthermore, active learning effectively and practically addresses real-world data scarcity. Despite all its benefits, active learning, in the context of NLP and especially multilingual language models pretraining, has received little consideration. In this paper, we present AfroLM, a multilingual language model pretrained from scratch on 23 African languages (the largest effort to date) using our novel self-active learning framework. Pretrained on a dataset significantly (14x) smaller than existing baselines, AfroLM outperforms many multilingual pretrained language models (AfriBERTa, XLMR-base, mBERT) on various NLP downstream tasks (NER, text classification, and sentiment analysis). Additional out-of-domain sentiment analysis experiments show that \\textbf{AfroLM} is able to generalize well across various domains. We release the code source, and our datasets used in our framework at https://github.com/bonaventuredossou/MLM_AL.", "authors": ["Bonaventure F. P. Dossou", "Atnafu Lambebo Tonja", "Oreen Yousuf", "Salomey Osei", "Abigail Oppong", "Iyanuoluwa Shode", "Oluwabusayo Olufunke Awoyomi", "Chris Chinenye Emezue"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-11-07", "url": "https://arxiv.org/abs/2211.03263", "pdf_url": "https://arxiv.org/pdf/2211.03263v2", "arxiv_id": "2211.03263", "doi": "10.48550/arXiv.2211.03263", "citation_count": 68, "influential_citation_count": 7, "has_code": true, "code_url": "https://github.com/bonaventuredossou/MLM_AL", "venue": null, "quality_score": 0.4597}
{"id": "1fe2bea2342f79b2ae649d34ef8d0ce3991dc5a64ebe2eee570334eb25f78125", "sources": ["arxiv", "semantic_scholar"], "title": "An Efficient MCMC Approach to Energy Function Optimization in Protein Structure Prediction", "abstract": "Protein structure prediction is a critical problem linked to drug design, mutation detection, and protein synthesis, among other applications. To this end, evolutionary data has been used to build contact maps which are traditionally minimized as energy functions via gradient descent based schemes like the L-BFGS algorithm. In this paper we present what we call the Alternating Metropolis-Hastings (AMH) algorithm, which (a) significantly improves the performance of traditional MCMC methods, (b) is inherently parallelizable allowing significant hardware acceleration using GPU, and (c) can be integrated with the L-BFGS algorithm to improve its performance. The algorithm shows an improvement in energy of found structures of 8.17% to 61.04% (average 38.9%) over traditional MH and 0.53% to 17.75% (average 8.9%) over traditional MH with intermittent noisy restarts, tested across 9 proteins from recent CASP competitions. We go on to map the Alternating MH algorithm to a GPGPU which improves sampling rate by 277x and improves simulation time to a low energy protein prediction by 7.5x to 26.5x over CPU. We show that our approach can be incorporated into state-of-the-art protein prediction pipelines by applying it to both trRosetta2's energy function and the distogram component of Alphafold1's energy function. Finally, we note that specially designed probabilistic computers (or p-computers) can provide even better performance than GPUs for MCMC algorithms like the one discussed here.", "authors": ["Lakshmi A. Ghantasala", "Risi Jaiswal", "Supriyo Datta"], "categories": ["q-bio.BM", "q-bio.QM", "stat.CO"], "fields_of_study": ["Biology", "Mathematics"], "published_date": "2022-11-06", "url": "https://arxiv.org/abs/2211.03193", "pdf_url": "https://arxiv.org/pdf/2211.03193v1", "arxiv_id": "2211.03193", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "66e98fba6657123729e6031fe5592c346fb3e218530283766a1e6cfb985321ec", "sources": ["arxiv", "semantic_scholar"], "title": "Autoregressive Structured Prediction with Language Models", "abstract": "Recent years have seen a paradigm shift in NLP towards using pretrained language models ({PLM}) for a wide range of tasks. However, there are many difficult design decisions to represent structures (e.g. tagged text, coreference chains) in a way such that they can be captured by PLMs. Prior work on structured prediction with PLMs typically flattens the structured output into a sequence, which limits the quality of structural information being learned and leads to inferior performance compared to classic discriminative models. In this work, we describe an approach to model structures as sequences of actions in an autoregressive manner with PLMs, allowing in-structure dependencies to be learned without any loss. Our approach achieves the new state-of-the-art on all the structured prediction tasks we looked at, namely, named entity recognition, end-to-end relation extraction, and coreference resolution.", "authors": ["Tianyu Liu", "Yuchen Jiang", "Nicholas Monath", "Ryan Cotterell", "Mrinmaya Sachan"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2022-10-26", "url": "https://arxiv.org/abs/2210.14698", "pdf_url": "https://arxiv.org/pdf/2210.14698v2", "arxiv_id": "2210.14698", "doi": "10.48550/arXiv.2210.14698", "citation_count": 67, "influential_citation_count": 8, "has_code": false, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.4771}
{"id": "7c1eb008433700fce8ab94972e924dd55c24f2a227dab29a7a14cae5c69b996c", "sources": ["arxiv", "semantic_scholar"], "title": "AlphaFold Distillation for Protein Design", "abstract": "Inverse protein folding, the process of designing sequences that fold into a specific 3D structure, is crucial in bio-engineering and drug discovery. Traditional methods rely on experimentally resolved structures, but these cover only a small fraction of protein sequences. Forward folding models like AlphaFold offer a potential solution by accurately predicting structures from sequences. However, these models are too slow for integration into the optimization loop of inverse folding models during training. To address this, we propose using knowledge distillation on folding model confidence metrics, such as pTM or pLDDT scores, to create a faster and end-to-end differentiable distilled model. This model can then be used as a structure consistency regularizer in training the inverse folding model. Our technique is versatile and can be applied to other design tasks, such as sequence-based protein infilling. Experimental results show that our method outperforms non-regularized baselines, yielding up to 3% improvement in sequence recovery and up to 45% improvement in protein diversity while maintaining structural consistency in generated sequences. Code is available at https://github.com/IBM/AFDistill", "authors": ["Igor Melnyk", "Aurelie Lozano", "Payel Das", "Vijil Chenthamarakshan"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2022-10-05", "url": "https://arxiv.org/abs/2210.03488", "pdf_url": "https://arxiv.org/pdf/2210.03488v2", "arxiv_id": "2210.03488", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/IBM/AFDistill", "venue": null, "quality_score": 0.0753}
{"id": "2b72f59ff4c5089a88f1c8b82c101ffc6b6d4d161d2be6003bcb104a729d4f7c", "sources": ["arxiv", "semantic_scholar"], "title": "State-specific protein-ligand complex structure prediction with a multi-scale deep generative model", "abstract": "The binding complexes formed by proteins and small molecule ligands are ubiquitous and critical to life. Despite recent advancements in protein structure prediction, existing algorithms are so far unable to systematically predict the binding ligand structures along with their regulatory effects on protein folding. To address this discrepancy, we present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures solely using protein sequence and ligand molecular graph inputs. NeuralPLexer adopts a deep generative model to sample the 3D structures of the binding complex and their conformational changes at an atomistic resolution. The model is based on a diffusion process that incorporates essential biophysical constraints and a multi-scale geometric deep learning system to iteratively sample residue-level contact maps and all heavy-atom coordinates in a hierarchical manner. NeuralPLexer achieves state-of-the-art performance compared to all existing methods on benchmarks for both protein-ligand blind docking and flexible binding site structure recovery. Moreover, owing to its specificity in sampling both ligand-free-state and ligand-bound-state ensembles, NeuralPLexer consistently outperforms AlphaFold2 in terms of global protein structure accuracy on both representative structure pairs with large conformational changes (average TM-score=0.93) and recently determined ligand-binding proteins (average TM-score=0.89). Case studies reveal that the predicted conformational variations are consistent with structure determination experiments for important targets, including human KRAS$^\\textrm{G12C}$, ketol-acid reductoisomerase, and purine GPCRs. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.", "authors": ["Zhuoran Qiao", "Weili Nie", "Arash Vahdat", "Thomas F. Miller", "Anima Anandkumar"], "categories": ["q-bio.QM", "cs.LG", "q-bio.BM"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2022-09-30", "url": "https://arxiv.org/abs/2209.15171", "pdf_url": "https://arxiv.org/pdf/2209.15171v2", "arxiv_id": "2209.15171", "doi": "10.1038/s42256-024-00792-z", "citation_count": 169, "influential_citation_count": 11, "has_code": false, "code_url": null, "venue": "Nature Machine Intelligence", "quality_score": 0.5576}
{"id": "caa7f07ef1d6135be75f8c56eda3e5217f6a29c2530fafe4f6f16ca552f260c2", "sources": ["arxiv", "semantic_scholar"], "title": "Secondary Protein Structure Prediction Using Neural Networks", "abstract": "In this paper we experiment with using neural network structures to predict a protein's secondary structure (α helix positions) from only its primary structure (amino acid sequence). We implement a fully connected neural network (FCNN) and preform three experiments using that FCNN. Firstly, we do a cross-species comparison of models trained and tested on mouse and human datasets. Secondly, we test the impact of varying the length of protein sequence we input into the model. Thirdly, we compare custom error functions designed to focus on the center of the input window. At the end of paper we propose a alternative, recurrent neural network model which can be applied to the problem.", "authors": ["Sidharth Malhotra", "Robin Walters"], "categories": ["cs.LG", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2022-08-24", "url": "https://arxiv.org/abs/2208.11248", "pdf_url": "https://arxiv.org/pdf/2208.11248v1", "arxiv_id": "2208.11248", "doi": "10.48550/arXiv.2208.11248", "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "4f78825126c131154fd8aca23ba41f9de2155fe52d5e9c3b3f22d9abd682ef79", "sources": ["arxiv", "semantic_scholar"], "title": "HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative", "abstract": "AI-based protein structure prediction pipelines, such as AlphaFold2, have achieved near-experimental accuracy. These advanced pipelines mainly rely on Multiple Sequence Alignments (MSAs) as inputs to learn the co-evolution information from the homologous sequences. Nonetheless, searching MSAs from protein databases is time-consuming, usually taking dozens of minutes. Consequently, we attempt to explore the limits of fast protein structure prediction by using only primary sequences of proteins. HelixFold-Single is proposed to combine a large-scale protein language model with the superior geometric learning capability of AlphaFold2. Our proposed method, HelixFold-Single, first pre-trains a large-scale protein language model (PLM) with thousands of millions of primary sequences utilizing the self-supervised learning paradigm, which will be used as an alternative to MSAs for learning the co-evolution information. Then, by combining the pre-trained PLM and the essential components of AlphaFold2, we obtain an end-to-end differentiable model to predict the 3D coordinates of atoms from only the primary sequence. HelixFold-Single is validated in datasets CASP14 and CAMEO, achieving competitive accuracy with the MSA-based methods on the targets with large homologous families. Furthermore, HelixFold-Single consumes much less time than the mainstream pipelines for protein structure prediction, demonstrating its potential in tasks requiring many predictions. The code of HelixFold-Single is available at https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/protein_folding/helixfold-single, and we also provide stable web services on https://paddlehelix.baidu.com/app/drug/protein-single/forecast.", "authors": ["Xiaomin Fang", "Fan Wang", "Lihang Liu", "Jingzhou He", "Dayong Lin", "Yingfei Xiang", "Xiaonan Zhang", "Hua Wu", "Hui Li", "Le Song"], "categories": ["q-bio.BM", "cs.AI", "cs.LG", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2022-07-28", "url": "https://arxiv.org/abs/2207.13921", "pdf_url": "https://arxiv.org/pdf/2207.13921v3", "arxiv_id": "2207.13921", "doi": "10.1038/s42256-023-00721-6", "citation_count": 86, "influential_citation_count": 3, "has_code": true, "code_url": "https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/protein_folding/helixfold-single", "venue": "Nature Machine Intelligence", "quality_score": 0.4849}
{"id": "2bb21b2f708c23272a1c18ea295e5c5a1aa7b94533f2d5c360474eae18905564", "sources": ["arxiv", "semantic_scholar"], "title": "End-to-End Spoken Language Understanding: Performance analyses of a voice command task in a low resource setting", "abstract": "Spoken Language Understanding (SLU) is a core task in most human-machine interaction systems. With the emergence of smart homes, smart phones and smart speakers, SLU has become a key technology for the industry. In a classical SLU approach, an Automatic Speech Recognition (ASR) module transcribes the speech signal into a textual representation from which a Natural Language Understanding (NLU) module extracts semantic information. Recently End-to-End SLU (E2E SLU) based on Deep Neural Networks has gained momentum since it benefits from the joint optimization of the ASR and the NLU parts, hence limiting the cascade of error effect of the pipeline architecture. However, little is known about the actual linguistic properties used by E2E models to predict concepts and intents from speech input. In this paper, we present a study identifying the signal features and other linguistic properties used by an E2E model to perform the SLU task. The study is carried out in the application domain of a smart home that has to handle non-English (here French) voice commands. The results show that a good E2E SLU performance does not always require a perfect ASR capability. Furthermore, the results show the superior capabilities of the E2E model in handling background noise and syntactic variation compared to the pipeline model. Finally, a finer-grained analysis suggests that the E2E model uses the pitch information of the input signal to identify voice command concepts. The results and methodology outlined in this paper provide a springboard for further analyses of E2E models in speech processing.", "authors": ["Thierry Desot", "François Portet", "Michel Vacher"], "categories": ["cs.CL", "cs.SD", "eess.AS"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2022-07-17", "url": "https://arxiv.org/abs/2207.08179", "pdf_url": "https://arxiv.org/pdf/2207.08179v1", "arxiv_id": "2207.08179", "doi": "10.1016/j.csl.2022.101369", "citation_count": 16, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Computer Speech and Language", "quality_score": 0.3076}
{"id": "76dacb0235a6eaf4be22d94b32c4afbf5af19054e49d21cc0d897a6fa28462b7", "sources": ["arxiv", "semantic_scholar"], "title": "AlphaFold predicts the most complex protein knot and composite protein knots", "abstract": "The computer artificial intelligence system AlphaFold has recently predicted previously unknown three-dimensional structures of thousands of proteins. Focusing on the subset with high-confidence scores, we algorithmically analyze these predictions for cases where the protein backbone exhibits rare topological complexity, i.e. knotting. Amongst others, we discovered a $7_1$-knot, the most topologically complex knot ever found in a protein, as well several 6-crossing composite knots comprised of two methyltransferase or carbonic anhydrase domains, each containing a simple trefoil knot. These deeply embedded composite knots occur evidently by gene duplication and interconnection of knotted dimers. Finally, we report two new five-crossing knots including the first $5_1$-knot. Our list of analyzed structures forms the basis for future experimental studies to confirm these novel knotted topologies and to explore their complex folding mechanisms.", "authors": ["Maarten A. Brems", "Robert Runkel", "Todd O. Yeates", "Peter Virnau"], "categories": ["q-bio.BM", "cond-mat.soft", "physics.bio-ph"], "fields_of_study": ["Biology", "Physics", "Medicine"], "published_date": "2022-07-15", "url": "https://arxiv.org/abs/2207.07410", "pdf_url": "https://arxiv.org/pdf/2207.07410v1", "arxiv_id": "2207.07410", "doi": "10.1002/pro.4380", "citation_count": 42, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "Protein Science", "quality_score": 0.4084}
{"id": "1e9420f34df72fc29b6463fbf7b111fd7575d7e8431b3b873f5310596dc46efd", "sources": ["arxiv", "semantic_scholar"], "title": "Linguistically inspired roadmap for building biologically reliable protein language models", "abstract": "Deep neural-network-based language models (LMs) are increasingly applied to large-scale protein sequence data to predict protein function. However, being largely black-box models and thus challenging to interpret, current protein LM approaches do not contribute to a fundamental understanding of sequence-function mappings, hindering rule-based biotherapeutic drug development. We argue that guidance drawn from linguistics, a field specialized in analytical rule extraction from natural language data, can aid with building more interpretable protein LMs that are more likely to learn relevant domain-specific rules. Differences between protein sequence data and linguistic sequence data require the integration of more domain-specific knowledge in protein LMs compared to natural language LMs. Here, we provide a linguistics-based roadmap for protein LM pipeline choices with regard to training data, tokenization, token embedding, sequence embedding, and model interpretation. Incorporating linguistic ideas into protein LMs enables the development of next-generation interpretable machine-learning models with the potential of uncovering the biological mechanisms underlying sequence-function relationships.", "authors": ["Mai Ha Vu", "Rahmad Akbar", "Philippe A. Robert", "Bartlomiej Swiatczak", "Victor Greiff", "Geir Kjetil Sandve", "Dag Trygve Truslew Haug"], "categories": ["q-bio.QM", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2022-07-03", "url": "https://arxiv.org/abs/2207.00982", "pdf_url": "https://arxiv.org/pdf/2207.00982v2", "arxiv_id": "2207.00982", "doi": "10.1038/s42256-023-00637-1", "citation_count": 48, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Nature Machine Intelligence", "quality_score": 0.4225}
{"id": "055ed6de90a53dd4dc89635e983bb10c1039828119bb9a1527f4e1ae4d3a5c16", "sources": ["arxiv", "semantic_scholar"], "title": "ProGen2: Exploring the Boundaries of Protein Language Models", "abstract": "Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial intelligence-driven protein design. However, we lack a sufficient understanding of how very large-scale models and data play a role in effective protein model development. We introduce a suite of protein language models, named ProGen2, that are scaled up to 6.4B parameters and trained on different sequence datasets drawn from over a billion proteins from genomic, metagenomic, and immune repertoire databases. ProGen2 models show state-of-the-art performance in capturing the distribution of observed evolutionary sequences, generating novel viable sequences, and predicting protein fitness without additional finetuning. As large model sizes and raw numbers of protein sequences continue to become more widely accessible, our results suggest that a growing emphasis needs to be placed on the data distribution provided to a protein sequence model. We release the ProGen2 models and code at https://github.com/salesforce/progen.", "authors": ["Erik Nijkamp", "Jeffrey Ruffolo", "Eli N. Weinstein", "Nikhil Naik", "Ali Madani"], "categories": ["cs.LG", "q-bio.QM"], "fields_of_study": ["Computer Science", "Medicine", "Biology"], "published_date": "2022-06-27", "url": "https://arxiv.org/abs/2206.13517", "pdf_url": "https://arxiv.org/pdf/2206.13517v1", "arxiv_id": "2206.13517", "doi": "10.48550/arXiv.2206.13517", "citation_count": 530, "influential_citation_count": 52, "has_code": true, "code_url": "https://github.com/salesforce/progen", "venue": "Cell Systems", "quality_score": 0.8621}
{"id": "b84657d4b8ae6a629d749eeed309fe6cf77b9dcb0042d8da28d6aee22af300fc", "sources": ["arxiv", "semantic_scholar"], "title": "PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction", "abstract": "Proteins are essential component of human life and their structures are important for function and mechanism analysis. Recent work has shown the potential of AI-driven methods for protein structure prediction. However, the development of new models is restricted by the lack of dataset and benchmark training procedure. To the best of our knowledge, the existing open source datasets are far less to satisfy the needs of modern protein sequence-structure related research. To solve this problem, we present the first million-level protein structure prediction dataset with high coverage and diversity, named as PSP. This dataset consists of 570k true structure sequences (10TB) and 745k complementary distillation sequences (15TB). We provide in addition the benchmark training procedure for SOTA protein structure prediction model on this dataset. We validate the utility of this dataset for training by participating CAMEO contest in which our model won the first place. We hope our PSP dataset together with the training benchmark can enable a broader community of AI/biology researchers for AI-driven protein related research.", "authors": ["Sirui Liu", "Jun Zhang", "Haotian Chu", "Min Wang", "Boxin Xue", "Ningxi Ni", "Jialiang Yu", "Yuhao Xie", "Zhenyu Chen", "Mengyun Chen", "Yuan Liu", "Piya Patra", "Fan Xu", "Jie Chen", "Zidong Wang", "Lijiang Yang", "Fan Yu", "Lei Chen", "Yi Qin Gao"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2022-06-24", "url": "https://arxiv.org/abs/2206.12240", "pdf_url": "https://arxiv.org/pdf/2206.12240v1", "arxiv_id": "2206.12240", "doi": "10.48550/arXiv.2206.12240", "citation_count": 15, "influential_citation_count": 1, "has_code": true, "code_url": null, "venue": "arXiv.org", "quality_score": 0.301}
{"id": "d9e27626bba2df65bbc41302170cdfb76829433c486970b3aa145965af4ffacb", "sources": ["arxiv", "semantic_scholar"], "title": "Transformer Neural Networks Attending to Both Sequence and Structure for Protein Prediction Tasks", "abstract": "The increasing number of protein sequences decoded from genomes is opening up new avenues of research on linking protein sequence to function with transformer neural networks. Recent research has shown that the number of known protein sequences supports learning useful, task-agnostic sequence representations via transformers. In this paper, we posit that learning joint sequence-structure representations yields better representations for function-related prediction tasks. We propose a transformer neural network that attends to both sequence and tertiary structure. We show that such joint representations are more powerful than sequence-based representations only, and they yield better performance on superfamily membership across various metrics.", "authors": ["Anowarul Kabir", "Amarda Shehu"], "categories": ["cs.LG", "cs.AI", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2022-06-17", "url": "https://arxiv.org/abs/2206.11057", "pdf_url": "https://arxiv.org/pdf/2206.11057v1", "arxiv_id": "2206.11057", "doi": "10.48550/arXiv.2206.11057", "citation_count": 5, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1945}
{"id": "3bd8f8baac2439a350656586bfb06ef230bacf1705d0e80a15c74988beb289c6", "sources": ["arxiv", "semantic_scholar"], "title": "Exploring evolution-aware & -free protein language models as protein function predictors", "abstract": "Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions. In particular, AlphaFold, a ground-breaking AI system, could potentially reshape structural biology. However, the utility of the PLM module in AlphaFold, Evoformer, has not been explored beyond structure prediction. In this paper, we investigate the representation ability of three popular PLMs: ESM-1b (single sequence), MSA-Transformer (multiple sequence alignment) and Evoformer (structural), with a special focus on Evoformer. Specifically, we aim to answer the following key questions: (i) Does the Evoformer trained as part of AlphaFold produce representations amenable to predicting protein function? (ii) If yes, can Evoformer replace ESM-1b and MSA-Transformer? (ii) How much do these PLMs rely on evolution-related protein data? In this regard, are they complementary to each other? We compare these models by empirical study along with new insights and conclusions. All code and datasets for reproducibility are available at https://github.com/elttaes/Revisiting-PLMs.", "authors": ["Mingyang Hu", "Fajie Yuan", "Kevin K. Yang", "Fusong Ju", "Jin Su", "Hui Wang", "Fei Yang", "Qiuyang Ding"], "categories": ["q-bio.QM", "cs.AI"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2022-06-14", "url": "https://arxiv.org/abs/2206.06583", "pdf_url": "https://arxiv.org/pdf/2206.06583v2", "arxiv_id": "2206.06583", "doi": "10.52202/068431-2817", "citation_count": 62, "influential_citation_count": 4, "has_code": true, "code_url": "https://github.com/elttaes/Revisiting-PLMs", "venue": "Neural Information Processing Systems", "quality_score": 0.4498}
{"id": "82cc587f4dc12a868f8ca0764538eef20414236f8b86bd0bc041c7473b53acfb", "sources": ["arxiv", "semantic_scholar"], "title": "DeepStruct: Pretraining of Language Models for Structure Prediction", "abstract": "We introduce a method for improving the structural understanding abilities of language models. Unlike previous approaches that finetune the models with task-specific augmentation, we pretrain language models on a collection of task-agnostic corpora to generate structures from text. Our structure pretraining enables zero-shot transfer of the learned knowledge that models have about the structure tasks. We study the performance of this approach on 28 datasets, spanning 10 structure prediction tasks including open information extraction, joint entity and relation extraction, named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, factual probe, intent detection, and dialogue state tracking. We further enhance the pretraining with the task-specific training sets. We show that a 10B parameter language model transfers non-trivially to most tasks and obtains state-of-the-art performance on 21 of 28 datasets that we evaluate.", "authors": ["Chenguang Wang", "Xiao Liu", "Zui Chen", "Haoyun Hong", "Jie Tang", "Dawn Song"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-05-21", "url": "https://arxiv.org/abs/2205.10475", "pdf_url": "https://arxiv.org/pdf/2205.10475v2", "arxiv_id": "2205.10475", "doi": "10.48550/arXiv.2205.10475", "citation_count": 98, "influential_citation_count": 14, "has_code": false, "code_url": null, "venue": "Findings", "quality_score": 0.588}
{"id": "ac8c5bc0efc7a95f643f2d8c0a98cb29c345aea56493a49a7d90c5780c27ac4e", "sources": ["arxiv", "semantic_scholar"], "title": "MAS2HP: A Multi Agent System to Predict Protein Structure in 2D HP model", "abstract": "Protein Structure Prediction (PSP) is an unsolved problem in the field of computational biology. The problem of protein structure prediction is about predicting the native conformation of a protein, while its sequence of amino acids is known. Regarding processing limitations of current computer systems, all-atom simulations for proteins are typically unpractical; several reduced models of proteins have been proposed. Additionally, due to intrinsic hardness of calculations even in reduced models, many computational methods mainly based on artificial intelligence have been proposed to solve the problem. Agent-based modeling is a relatively new method for modeling systems composed of interacting items. In this paper we proposed a new approach for protein structure prediction by using agent-based modeling (ABM) in two dimensional hydrophobic-hydrophilic model. We broke the whole process of protein structure prediction into two steps: the first step, which was introduced in our previous paper, is about biasing the linear sequence to gain a primary energy, and the next step, which will be explained in this paper, is about using ABM with a predefined set of rules, to find the best conformation in the least possible amount of time and steps. This method was implemented in NETLOGO. We have tested this algorithm on several benchmark sequences ranging from 20 to 50-mers in two dimensional Hydrophobic-Hydrophilic lattice models. Comparing to the result of the other algorithms, our method is capable of finding the best known conformations in a significantly shorter time. A major problem in PSP simulation is that as the sequence length increases the time consumed to predict a valid structure will exponentially increase. In contrast, by using MAS2HP the effect of increase in sequence length on spent time has changed from exponentially to linear.", "authors": ["Hossein Parineh", "Nasser Mozayani"], "categories": ["q-bio.BM", "cs.AI"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2022-05-11", "url": "https://arxiv.org/abs/2205.08451", "pdf_url": "https://arxiv.org/pdf/2205.08451v4", "arxiv_id": "2205.08451", "doi": "10.48550/arXiv.2205.08451", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "db34d37c5d59e4651841394a2935b510ab7537a24b3591a96c15a77b5c796ea8", "sources": ["arxiv", "semantic_scholar"], "title": "Training Language Models with Language Feedback", "abstract": "Pretrained language models often do not perform tasks in ways that are in line with our preferences, e.g., generating offensive text or factually incorrect summaries. Recent work approaches the above issue by learning from a simple form of human evaluation: comparisons between pairs of model-generated task outputs. Comparison feedback conveys limited information about human preferences per human evaluation. Here, we propose to learn from natural language feedback, which conveys more information per human evaluation. We learn from language feedback on model outputs using a three-step learning algorithm. First, we condition the language model on the initial output and feedback to generate many refinements. Second, we choose the refinement with the highest similarity to the feedback. Third, we finetune a language model to maximize the likelihood of the chosen refinement given the input. In synthetic experiments, we first evaluate whether language models accurately incorporate feedback to produce refinements, finding that only large language models (175B parameters) do so. Using only 100 samples of human-written feedback, our learning algorithm finetunes a GPT-3 model to roughly human-level summarization ability.", "authors": ["Jérémy Scheurer", "Jon Ander Campos", "Jun Shern Chan", "Angelica Chen", "Kyunghyun Cho", "Ethan Perez"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-04-29", "url": "https://arxiv.org/abs/2204.14146", "pdf_url": "https://arxiv.org/pdf/2204.14146v4", "arxiv_id": "2204.14146", "doi": null, "citation_count": 58, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.4427}
{"id": "d386a7f6260fb3421362df1754a181ac0f75e63eea99f4c4548dd1620aabb2b3", "sources": ["arxiv", "semantic_scholar"], "title": "Graph neural networks and attention-based CNN-LSTM for protein classification", "abstract": "This paper focuses on three critical problems on protein classification. Firstly, Carbohydrate-active enzyme (CAZyme) classification can help people to understand the properties of enzymes. However, one CAZyme may belong to several classes. This leads to Multi-label CAZyme classification. Secondly, to capture information from the secondary structure of protein, protein classification is modeled as graph classification problem. Thirdly, compound-protein interactions prediction employs graph learning for compound with sequential embedding for protein. This can be seen as classification task for compound-protein pairs. This paper proposes three models for protein classification. Firstly, this paper proposes a Multi-label CAZyme classification model using CNN-LSTM with Attention mechanism. Secondly, this paper proposes a variational graph autoencoder based subspace learning model for protein graph classification. Thirdly, this paper proposes graph isomorphism networks (GIN) and Attention-based CNN-LSTM for compound-protein interactions prediction, as well as comparing GIN with graph convolution networks (GCN) and graph attention networks (GAT) in this task. The proposed models are effective for protein classification. Source code and data are available at https://github.com/zshicode/GNN-AttCL-protein. Besides, this repository collects and collates the benchmark datasets with respect to above problems, including CAZyme classification, enzyme protein graph classification, compound-protein interactions prediction, drug-target affinities prediction and drug-drug interactions prediction. Hence, the usage for evaluation by benchmark datasets can be more conveniently.", "authors": ["Zhuangwei Shi", "Bo Li"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2022-04-20", "url": "https://arxiv.org/abs/2204.09486", "pdf_url": "https://arxiv.org/pdf/2204.09486v2", "arxiv_id": "2204.09486", "doi": "10.48550/arXiv.2204.09486", "citation_count": 8, "influential_citation_count": 0, "has_code": true, "code_url": "https://github.com/zshicode/GNN-AttCL-protein", "venue": "arXiv.org", "quality_score": 0.2386}
{"id": "95fbc6a0fe6094d23f413dc423a6a3fe5dd54fdda0527743bfd2b0d25b37ff02", "sources": ["arxiv", "semantic_scholar"], "title": "Generative power of a protein language model trained on multiple sequence alignments", "abstract": "Computational models starting from large ensembles of evolutionarily related protein sequences capture a representation of protein families and learn constraints associated to protein structure and function. They thus open the possibility for generating novel sequences belonging to protein families. Protein language models trained on multiple sequence alignments, such as MSA Transformer, are highly attractive candidates to this end. We propose and test an iterative method that directly employs the masked language modeling objective to generate sequences using MSA Transformer. We demonstrate that the resulting sequences score as well as natural sequences, for homology, coevolution and structure-based measures. For large protein families, our synthetic sequences have similar or better properties compared to sequences generated by Potts models, including experimentally-validated ones. Moreover, for small protein families, our generation method based on MSA Transformer outperforms Potts models. Our method also more accurately reproduces the higher-order statistics and the distribution of sequences in sequence space of natural data than Potts models. MSA Transformer is thus a strong candidate for protein sequence generation and protein design.", "authors": ["Damiano Sgarbossa", "Umberto Lupo", "Anne-Florence Bitbol"], "categories": ["q-bio.BM", "cs.LG", "q-bio.QM"], "fields_of_study": ["Biology", "Medicine", "Computer Science"], "published_date": "2022-04-14", "url": "https://arxiv.org/abs/2204.07110", "pdf_url": "https://arxiv.org/pdf/2204.07110v2", "arxiv_id": "2204.07110", "doi": "10.7554/eLife.79854", "citation_count": 44, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "bioRxiv", "quality_score": 0.4133}
{"id": "45a5297b58f3db93814cd1adef0c8c65521003b1185581e274fcfb2f8a12b71a", "sources": ["arxiv", "semantic_scholar"], "title": "Inferring Rewards from Language in Context", "abstract": "In classic instruction following, language like \"I'd like the JetBlue flight\" maps to actions (e.g., selecting that flight). However, language also conveys information about a user's underlying reward function (e.g., a general preference for JetBlue), which can allow a model to carry out desirable actions in new contexts. We present a model that infers rewards from language pragmatically: reasoning about how speakers choose utterances not only to elicit desired actions, but also to reveal information about their preferences. On a new interactive flight-booking task with natural language, our model more accurately infers rewards and predicts optimal actions in unseen environments, in comparison to past work that first maps language to actions (instruction following) and then maps actions to rewards (inverse reinforcement learning).", "authors": ["Jessy Lin", "Daniel Fried", "Dan Klein", "Anca Dragan"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2022-04-05", "url": "https://arxiv.org/abs/2204.02515", "pdf_url": "https://arxiv.org/pdf/2204.02515v1", "arxiv_id": "2204.02515", "doi": "10.48550/arXiv.2204.02515", "citation_count": 74, "influential_citation_count": 4, "has_code": true, "code_url": "https://github.com/jlin816/rewards-from-language", "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.4688}
{"id": "0ead8b510fc2ad2c40be361999912d3dd6efdb20bbb33d5e3cb11dc49fc2a124", "sources": ["arxiv", "semantic_scholar"], "title": "Protein language models trained on multiple sequence alignments learn phylogenetic relationships", "abstract": "Self-supervised neural language models with attention have recently been applied to biological sequence data, advancing structure, function and mutational effect prediction. Some protein language models, including MSA Transformer and AlphaFold's EvoFormer, take multiple sequence alignments (MSAs) of evolutionarily related proteins as inputs. Simple combinations of MSA Transformer's row attentions have led to state-of-the-art unsupervised structural contact prediction. We demonstrate that similarly simple, and universal, combinations of MSA Transformer's column attentions strongly correlate with Hamming distances between sequences in MSAs. Therefore, MSA-based language models encode detailed phylogenetic relationships. We further show that these models can separate coevolutionary signals encoding functional and structural constraints from phylogenetic correlations reflecting historical contingency. To assess this, we generate synthetic MSAs, either without or with phylogeny, from Potts models trained on natural MSAs. We find that unsupervised contact prediction is substantially more resilient to phylogenetic noise when using MSA Transformer versus inferred Potts models.", "authors": ["Umberto Lupo", "Damiano Sgarbossa", "Anne-Florence Bitbol"], "categories": ["q-bio.BM", "cs.LG", "q-bio.QM"], "fields_of_study": ["Medicine", "Biology", "Computer Science"], "published_date": "2022-03-29", "url": "https://arxiv.org/abs/2203.15465", "pdf_url": "https://arxiv.org/pdf/2203.15465v2", "arxiv_id": "2203.15465", "doi": "10.1038/s41467-022-34032-y", "citation_count": 61, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "bioRxiv", "quality_score": 0.4481}
{"id": "df53de26879e8e8126b1d017f6db71662b14b46d1510765e615bd12370f994d0", "sources": ["arxiv", "semantic_scholar"], "title": "Protein Representation Learning by Geometric Structure Pretraining", "abstract": "Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Existing approaches usually pretrain protein language models on a large number of unlabeled amino acid sequences and then finetune the models with some labeled data in downstream tasks. Despite the effectiveness of sequence-based approaches, the power of pretraining on known protein structures, which are available in smaller numbers only, has not been explored for protein property prediction, though protein structures are known to be determinants of protein function. In this paper, we propose to pretrain protein representations according to their 3D structures. We first present a simple yet effective encoder to learn the geometric features of a protein. We pretrain the protein graph encoder by leveraging multiview contrastive learning and different self-prediction tasks. Experimental results on both function prediction and fold classification tasks show that our proposed pretraining methods outperform or are on par with the state-of-the-art sequence-based methods, while using much less pretraining data. Our implementation is available at https://github.com/DeepGraphLearning/GearNet.", "authors": ["Zuobai Zhang", "Minghao Xu", "Arian Jamasb", "Vijil Chenthamarakshan", "Aurelie Lozano", "Payel Das", "Jian Tang"], "categories": ["cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2022-03-11", "url": "https://arxiv.org/abs/2203.06125", "pdf_url": "https://arxiv.org/pdf/2203.06125v5", "arxiv_id": "2203.06125", "doi": "10.48550/arXiv.2203.06125", "citation_count": 322, "influential_citation_count": 50, "has_code": true, "code_url": "https://github.com/DeepGraphLearning/GearNet", "venue": "International Conference on Learning Representations", "quality_score": 0.8538}
{"id": "e934605fdc528234ee6032420a05c41f9205aa8c079f4d8d5ddfb417b3cf7a43", "sources": ["arxiv", "semantic_scholar"], "title": "FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours", "abstract": "Protein structure prediction helps to understand gene translation and protein function, which is of growing interest and importance in structural biology. The AlphaFold model, which used transformer architecture to achieve atomic-level accuracy in protein structure prediction, was a significant breakthrough. However, training and inference of the AlphaFold model are challenging due to its high computation and memory cost. In this work, we present FastFold, an efficient implementation of AlphaFold for both training and inference. We propose Dynamic Axial Parallelism and Duality Async Operations to improve the scaling efficiency of model parallelism. Besides, AutoChunk is proposed to reduce memory cost by over 80% during inference by automatically determining the chunk strategy. Experimental results show that FastFold reduces overall training time from 11 days to 67 hours and achieves 7.5X - 9.5X speedup for long-sequence inference. Furthermore, we scale FastFold to 512 GPUs and achieve an aggregate throughput of 6.02 PetaFLOP/s with 90.1% parallel efficiency.", "authors": ["Shenggan Cheng", "Xuanlei Zhao", "Guangyang Lu", "Jiarui Fang", "Zhongming Yu", "Tian Zheng", "Ruidong Wu", "Xiwen Zhang", "Jian Peng", "Yang You"], "categories": ["cs.LG", "cs.AI", "cs.DC", "q-bio.QM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2022-03-02", "url": "https://arxiv.org/abs/2203.00854", "pdf_url": "https://arxiv.org/pdf/2203.00854v3", "arxiv_id": "2203.00854", "doi": "10.48550/arXiv.2203.00854", "citation_count": 42, "influential_citation_count": 7, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.4515}
{"id": "24c7f0ae69af37c4a0cc5f9d807581a91fc5079415a84673b31c7be5877b8bea", "sources": ["arxiv", "semantic_scholar"], "title": "Collective Variable for Metadynamics Derived from AlphaFold Output", "abstract": "AlphaFold is a neural-network-based tool for the prediction of 3D structures of protein. In CASP14, a blind structure prediction challenge, it performed significantly better than other competitors, which makes it the best available structure prediction tool. One of the outputs of AlphaFold is the probability profile of residue-residue distances. This makes it possible to score any conformation of the studied protein to express its compliance with the AlphaFold model. Here we show how this score can be used to drive protein folding simulation by metadynamics and parallel tempering metadynamics. By parallel tempering metadynamics, we simulated folding of a mini-protein Trp-cage beta hairpin and predicted their folding equilibria. We see the potential of AlphaFold-based collective variable in applications beyond structure prediction, such as in structure refinement or prediction of the outcome of a mutation.", "authors": ["Vojtěch Spiwok", "Martin Kurečka", "Aleš Křenek"], "categories": ["q-bio.BM"], "fields_of_study": ["Medicine", "Biology"], "published_date": "2022-02-17", "url": "https://arxiv.org/abs/2203.04848", "pdf_url": "https://arxiv.org/pdf/2203.04848v2", "arxiv_id": "2203.04848", "doi": "10.3389/fmolb.2022.878133", "citation_count": 12, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Frontiers in Molecular Biosciences", "quality_score": 0.2785}
{"id": "9b68f0a48b4de6449e9db2dcff8a084ac39980c916ed2f234f7bd5681af9ab6f", "sources": ["arxiv", "semantic_scholar"], "title": "Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer", "abstract": "Deep learning has contributed to major advances in the prediction of protein structure from sequence, a fundamental problem in structural bioinformatics. With predictions now approaching the accuracy of crystallographic resolution in some cases, and with accelerators like GPUs and TPUs making inference using large models rapid, fast genome-level structure prediction becomes an obvious aim. Leadership-class computing resources can be used to perform genome-scale protein structure prediction using state-of-the-art deep learning models, providing a wealth of new data for systems biology applications. Here we describe our efforts to efficiently deploy the AlphaFold2 program, for full-proteome structure prediction, at scale on the Oak Ridge Leadership Computing Facility's resources, including the Summit supercomputer. We performed inference to produce the predicted structures for 35,634 protein sequences, corresponding to three prokaryotic proteomes and one plant proteome, using under 4,000 total Summit node hours, equivalent to using the majority of the supercomputer for one hour. We also designed an optimized structure refinement that reduced the time for the relaxation stage of the AlphaFold pipeline by over 10X for longer sequences. We demonstrate the types of analyses that can be performed on proteome-scale collections of sequences, including a search for novel quaternary structures and implications for functional annotation.", "authors": ["Mu Gao", "Mark Coletti", "Russell B. Davidson", "Ryan Prout", "Subil Abraham", "Benjamin Hernandez", "Ada Sedova"], "categories": ["q-bio.QM", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2022-01-25", "url": "https://arxiv.org/abs/2201.10024", "pdf_url": "https://arxiv.org/pdf/2201.10024v1", "arxiv_id": "2201.10024", "doi": "10.1109/IPDPSW55747.2022.00045", "citation_count": 12, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum", "quality_score": 0.2785}
{"id": "e5a194a647ea91432c003704f76ca92fa7fe50a27ed755811cfd146d1a2363fd", "sources": ["arxiv", "semantic_scholar"], "title": "AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery: Efficient Discovery of a Novel Cyclin-dependent Kinase 20 (CDK20) Small Molecule Inhibitor", "abstract": "The AlphaFold computer program predicted protein structures for the whole human genome, which has been considered as a remarkable breakthrough both in artificial intelligence (AI) application and structural biology. Despite the varying confidence level, these predicted structures still could significantly contribute to structure-based drug design of novel targets, especially the ones with no or limited structural information. In this work, we successfully applied AlphaFold in our end-to-end AI-powered drug discovery engines constituted of a biocomputational platform PandaOmics and a generative chemistry platform Chemistry42, to identify a first-in-class hit molecule of a novel target without an experimental structure starting from target selection towards hit identification in a cost- and time-efficient manner. PandaOmics provided the targets of interest and Chemistry42 generated the molecules based on the AlphaFold predicted structure, and the selected molecules were synthesized and tested in biological assays. Through this approach, we identified a small molecule hit compound for CDK20 with a Kd value of 8.9 +/- 1.6 uM (n = 4) within 30 days from target selection and after only synthesizing 7 compounds. Based on the available data, the second round of AI-powered compound generation was conducted and through which, a more potent hit molecule, ISM042-2 048, was discovered with a Kd value of 210.0 +/- 42.4 nM (n = 2), within 30 days and after synthesizing 6 compounds from the discovery of the first hit ISM042-2-001. To the best of our knowledge, this is the first reported small molecule targeting CDK20 and more importantly, this work is the first demonstration of AlphaFold application in the hit identification process in early drug discovery.", "authors": ["Feng Ren", "Xiao Ding", "Min Zheng", "Mikhail Korzinkin", "Xin Cai", "Wei Zhu", "Alexey Mantsyzov", "Alex Aliper", "Vladimir Aladinskiy", "Zhongying Cao", "Shanshan Kong", "Xi Long", "Bonnie Hei Man Liu", "Yingtao Liu", "Vladimir Naumov", "Anastasia Shneyderman", "Ivan V. Ozerov", "Ju Wang", "Frank W. Pun", "Alan Aspuru-Guzik", "Michael Levitt", "Alex Zhavoronkov"], "categories": ["q-bio.BM", "cs.AI", "cs.LG", "q-bio.MN"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2022-01-21", "url": "https://arxiv.org/abs/2201.09647", "pdf_url": "https://arxiv.org/pdf/2201.09647v2", "arxiv_id": "2201.09647", "doi": null, "citation_count": 13, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2865}
{"id": "69c67ebf2c5650b810c92e6ce1ba0196c3f615a7ec74e281b9ea022b0d59ea38", "sources": ["arxiv", "semantic_scholar"], "title": "Controllable Protein Design with Language Models", "abstract": "The 21st century is presenting humankind with unprecedented environmental and medical challenges. The ability to design novel proteins tailored for specific purposes could transform our ability to respond timely to these issues. Recent advances in the field of artificial intelligence are now setting the stage to make this goal achievable. Protein sequences are inherently similar to natural languages: Amino acids arrange in a multitude of combinations to form structures that carry function, the same way as letters form words and sentences that carry meaning. Therefore, it is not surprising that throughout the history of Natural Language Processing (NLP), many of its techniques have been applied to protein research problems. In the last few years, we have witnessed revolutionary breakthroughs in the field of NLP. The implementation of Transformer pre-trained models has enabled text generation with human-like capabilities, including texts with specific properties such as style or subject. Motivated by its considerable success in NLP tasks, we expect dedicated Transformers to dominate custom protein sequence generation in the near future. Finetuning pre-trained models on protein families will enable the extension of their repertoires with novel sequences that could be highly divergent but still potentially functional. The combination of control tags such as cellular compartment or function will further enable the controllable design of novel protein functions. Moreover, recent model interpretability methods will allow us to open the 'black box' and thus enhance our understanding of folding principles. While early initiatives show the enormous potential of generative language models to design functional sequences, the field is still in its infancy. We believe that protein language models are a promising and largely unexplored field and discuss their foreseeable impact on protein design.", "authors": ["Noelia Ferruz", "Birte Höcker"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2022-01-18", "url": "https://arxiv.org/abs/2201.07338", "pdf_url": "https://arxiv.org/pdf/2201.07338v2", "arxiv_id": "2201.07338", "doi": "10.1038/s42256-022-00499-z", "citation_count": 204, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": "Nature Machine Intelligence", "quality_score": 0.5779}
{"id": "91de8db4d075ee748f03df8407735ac33125da2b43f8e9dfa7f311502d15e442", "sources": ["arxiv", "semantic_scholar"], "title": "Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents", "abstract": "Can world knowledge learned by large language models (LLMs) be used to act in interactive environments? In this paper, we investigate the possibility of grounding high-level tasks, expressed in natural language (e.g. \"make breakfast\"), to a chosen set of actionable steps (e.g. \"open fridge\"). While prior work focused on learning from explicit step-by-step examples of how to act, we surprisingly find that if pre-trained LMs are large enough and prompted appropriately, they can effectively decompose high-level tasks into mid-level plans without any further training. However, the plans produced naively by LLMs often cannot map precisely to admissible actions. We propose a procedure that conditions on existing demonstrations and semantically translates the plans to admissible actions. Our evaluation in the recent VirtualHome environment shows that the resulting method substantially improves executability over the LLM baseline. The conducted human evaluation reveals a trade-off between executability and correctness but shows a promising sign towards extracting actionable knowledge from language models. Website at https://huangwl18.github.io/language-planner", "authors": ["Wenlong Huang", "Pieter Abbeel", "Deepak Pathak", "Igor Mordatch"], "categories": ["cs.LG", "cs.AI", "cs.CL", "cs.CV", "cs.RO"], "fields_of_study": ["Computer Science"], "published_date": "2022-01-18", "url": "https://arxiv.org/abs/2201.07207", "pdf_url": "https://arxiv.org/pdf/2201.07207v2", "arxiv_id": "2201.07207", "doi": null, "citation_count": 1599, "influential_citation_count": 98, "has_code": false, "code_url": null, "venue": "International Conference on Machine Learning", "quality_score": 0.9978}
{"id": "339ba59de69abaf894180ddfbf4200e7b66a6c727c5e7f026ec11123a0121e7a", "sources": ["arxiv", "semantic_scholar"], "title": "Multimodal Pre-Training Model for Sequence-based Prediction of Protein-Protein Interaction", "abstract": "Protein-protein interactions (PPIs) are essentials for many biological processes where two or more proteins physically bind together to achieve their functions. Modeling PPIs is useful for many biomedical applications, such as vaccine design, antibody therapeutics, and peptide drug discovery. Pre-training a protein model to learn effective representation is critical for PPIs. Most pre-training models for PPIs are sequence-based, which naively adopt the language models used in natural language processing to amino acid sequences. More advanced works utilize the structure-aware pre-training technique, taking advantage of the contact maps of known protein structures. However, neither sequences nor contact maps can fully characterize structures and functions of the proteins, which are closely related to the PPI problem. Inspired by this insight, we propose a multimodal protein pre-training model with three modalities: sequence, structure, and function (S2F). Notably, instead of using contact maps to learn the amino acid-level rigid structures, we encode the structure feature with the topology complex of point clouds of heavy atoms. It allows our model to learn structural information about not only the backbones but also the side chains. Moreover, our model incorporates the knowledge from the functional description of proteins extracted from literature or manual annotations. Our experiments show that the S2F learns protein embeddings that achieve good performances on a variety of PPIs tasks, including cross-species PPI, antibody-antigen affinity prediction, antibody neutralization prediction for SARS-CoV-2, and mutation-driven binding affinity change prediction.", "authors": ["Yang Xue", "Zijing Liu", "Xiaomin Fang", "Fan Wang"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2021-12-09", "url": "https://arxiv.org/abs/2112.04814", "pdf_url": "https://arxiv.org/pdf/2112.04814v1", "arxiv_id": "2112.04814", "doi": null, "citation_count": 12, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2785}
{"id": "77fd0435e028f9da4011ab8c32f68c9619ba67ef53d966c04131d2c7c63c071f", "sources": ["arxiv", "semantic_scholar"], "title": "ParaFold: Paralleling AlphaFold for Large-Scale Predictions", "abstract": "AlphaFold predicts protein structures from the amino acid sequence at or near experimental resolution, solving the 50-year-old protein folding challenge, leading to progress by transforming large-scale genomics data into protein structures. AlphaFold will also greatly change the scientific research model from low-throughput to high-throughput manner. The AlphaFold framework is a mixture of two types of workloads: MSA construction based on CPUs and model inference on GPUs. The first CPU stage dominates the overall runtime, taking hours for a single protein due to the large database sizes and I/O bottlenecks. However, GPUs in this CPU stage remain idle, resulting in low GPU utilization and restricting the capacity of large-scale structure predictions. Therefore, we proposed ParaFold, an open-source parallel version of AlphaFold for high throughput protein structure predictions. ParaFold separates the CPU and GPU parts to enable large-scale structure predictions. ParaFold also effectively reduces the CPU and GPU runtime with two optimizations without compromising the quality of prediction results: using multi-threaded parallelism on CPUs and using optimized JAX compilation on GPUs. We evaluated ParaFold with three datasets of different size and protein lengths. We evaluated the accuracy and efficiency of optimizations on CPUs and GPUs, and showed the large-scale prediction capability by running ParaFold inferences of 19,704 small proteins in five hours on one NVIDIA DGX-2. Using the JAX compile optimization, ParaFold attained a 13.8X average speedup over AlphaFold. ParaFold offers a rapid and effective approach for high-throughput structure predictions, leveraging the predictive power by running on supercomputers, with shorter time, and at a lower cost. The development of ParaFold will greatly speed up high-throughput studies and render the protein \"structure-omics\" feasible.", "authors": ["Bozitao Zhong", "Xiaoming Su", "Minhua Wen", "Sichen Zuo", "Liang Hong", "James Lin"], "categories": ["q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2021-11-11", "url": "https://arxiv.org/abs/2111.06340", "pdf_url": "https://arxiv.org/pdf/2111.06340v2", "arxiv_id": "2111.06340", "doi": "10.1145/3503470.3503471", "citation_count": 43, "influential_citation_count": 4, "has_code": true, "code_url": null, "venue": null, "quality_score": 0.4109}
{"id": "9cd4b74dcd3244d7653f463cf3ddd1af173fa9c4b5178195de64946b6fd451dc", "sources": ["arxiv", "semantic_scholar"], "title": "Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model", "abstract": "Understanding protein sequences is vital and urgent for biology, healthcare, and medicine. Labeling approaches are expensive yet time-consuming, while the amount of unlabeled data is increasing quite faster than that of the labeled data due to low-cost, high-throughput sequencing methods. In order to extract knowledge from these unlabeled data, representation learning is of significant value for protein-related tasks and has great potential for helping us learn more about protein functions and structures. The key problem in the protein sequence representation learning is to capture the co-evolutionary information reflected by the inter-residue co-variation in the sequences. Instead of leveraging multiple sequence alignment as is usually done, we propose a novel method to capture this information directly by pre-training via a dedicated language model, i.e., Pairwise Masked Language Model (PMLM). In a conventional masked language model, the masked tokens are modeled by conditioning on the unmasked tokens only, but processed independently to each other. However, our proposed PMLM takes the dependency among masked tokens into consideration, i.e., the probability of a token pair is not equal to the product of the probability of the two tokens. By applying this model, the pre-trained encoder is able to generate a better representation for protein sequences. Our result shows that the proposed method can effectively capture the inter-residue correlations and improves the performance of contact prediction by up to 9% compared to the MLM baseline under the same setting. The proposed model also significantly outperforms the MSA baseline by more than 7% on the TAPE contact prediction benchmark when pre-trained on a subset of the sequence database which the MSA is generated from, revealing the potential of the sequence pre-training method to surpass MSA based methods in general.", "authors": ["Liang He", "Shizhuo Zhang", "Lijun Wu", "Huanhuan Xia", "Fusong Ju", "He Zhang", "Siyuan Liu", "Yingce Xia", "Jianwei Zhu", "Pan Deng", "Bin Shao", "Tao Qin", "Tie-Yan Liu"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2021-10-29", "url": "https://arxiv.org/abs/2110.15527", "pdf_url": "https://arxiv.org/pdf/2110.15527v1", "arxiv_id": "2110.15527", "doi": null, "citation_count": 36, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3921}
{"id": "97c4bdba865f46496ebc15b9fda3e20c5cf9ba1f4391acb8420e3eff2cf50e39", "sources": ["arxiv", "semantic_scholar"], "title": "An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C)", "abstract": "While we pay attention to the latest advances in clinical natural language processing (NLP), we can notice some resistance in the clinical and translational research community to adopt NLP models due to limited transparency, interpretability, and usability. In this study, we proposed an open natural language processing development framework. We evaluated it through the implementation of NLP algorithms for the National COVID Cohort Collaborative (N3C). Based on the interests in information extraction from COVID-19 related clinical notes, our work includes 1) an open data annotation process using COVID-19 signs and symptoms as the use case, 2) a community-driven ruleset composing platform, and 3) a synthetic text data generation workflow to generate texts for information extraction tasks without involving human subjects. The corpora were derived from texts from three different institutions (Mayo Clinic, University of Kentucky, University of Minnesota). The gold standard annotations were tested with a single institution's (Mayo) ruleset. This resulted in performances of 0.876, 0.706, and 0.694 in F-scores for Mayo, Minnesota, and Kentucky test datasets, respectively. The study as a consortium effort of the N3C NLP subgroup demonstrates the feasibility of creating a federated NLP algorithm development and benchmarking platform to enhance multi-institution clinical NLP study and adoption. Although we use COVID-19 as a use case in this effort, our framework is general enough to be applied to other domains of interest in clinical NLP.", "authors": ["Sijia Liu", "Andrew Wen", "Liwei Wang", "Huan He", "Sunyang Fu", "Robert Miller", "Andrew Williams", "Daniel Harris", "Ramakanth Kavuluru", "Mei Liu", "Noor Abu-el-rub", "Dalton Schutte", "Rui Zhang", "Masoud Rouhizadeh", "John D. Osborne", "Yongqun He", "Umit Topaloglu", "Stephanie S Hong", "Joel H Saltz", "Thomas Schaffter", "Emily Pfaff", "Christopher G. Chute", "Tim Duong", "Melissa A. Haendel", "Rafael Fuentes", "Peter Szolovits", "Hua Xu", "Hongfang Liu", "National COVID Cohort Collaborative", "Natural Language Processing", " Subgroup", "National COVID Cohort Collaborative"], "categories": ["cs.CL", "cs.IR"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2021-10-20", "url": "https://arxiv.org/abs/2110.10780", "pdf_url": "https://arxiv.org/pdf/2110.10780v3", "arxiv_id": "2110.10780", "doi": "10.1093/jamia/ocad134", "citation_count": 18, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.3197}
{"id": "a3ea58588a590709d489bb96a26e3287eb48130fc9366c4b000e6dbfe219bc56", "sources": ["arxiv", "semantic_scholar"], "title": "Application of Sequence Embedding in Protein Sequence-Based Predictions", "abstract": "In sequence-based predictions, conventionally an input sequence is represented by a multiple sequence alignment (MSA) or a representation derived from MSA, such as a position-specific scoring matrix. Recently, inspired by the development in natural language processing, several applications of sequence embedding have been observed. Here, we review different approaches of protein sequence embeddings and their applications including protein contact prediction, secondary structure, prediction, and function prediction.", "authors": ["Nabil Ibtehaz", "Daisuke Kihara"], "categories": ["q-bio.QM"], "fields_of_study": ["Biology"], "published_date": "2021-10-14", "url": "https://arxiv.org/abs/2110.07609", "pdf_url": "https://arxiv.org/pdf/2110.07609v1", "arxiv_id": "2110.07609", "doi": null, "citation_count": 15, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.301}
{"id": "0fb351eba72fa98ebf416d3a76608e2e7b4311180fd6ca522d53d590dc5d2888", "sources": ["arxiv", "semantic_scholar"], "title": "Is Attention always needed? A Case Study on Language Identification from Speech", "abstract": "Language Identification (LID) is a crucial preliminary process in the field of Automatic Speech Recognition (ASR) that involves the identification of a spoken language from audio samples. Contemporary systems that can process speech in multiple languages require users to expressly designate one or more languages prior to utilization. The LID task assumes a significant role in scenarios where ASR systems are unable to comprehend the spoken language in multilingual settings, leading to unsuccessful speech recognition outcomes. The present study introduces convolutional recurrent neural network (CRNN) based LID, designed to operate on the Mel-frequency Cepstral Coefficient (MFCC) characteristics of audio samples. Furthermore, we replicate certain state-of-the-art methodologies, specifically the Convolutional Neural Network (CNN) and Attention-based Convolutional Recurrent Neural Network (CRNN with attention), and conduct a comparative analysis with our CRNN-based approach. We conducted comprehensive evaluations on thirteen distinct Indian languages and our model resulted in over 98\\% classification accuracy. The LID model exhibits high-performance levels ranging from 97% to 100% for languages that are linguistically similar. The proposed LID model exhibits a high degree of extensibility to additional languages and demonstrates a strong resistance to noise, achieving 91.2% accuracy in a noisy setting when applied to a European Language (EU) dataset.", "authors": ["Atanu Mandal", "Santanu Pal", "Indranil Dutta", "Mahidas Bhattacharya", "Sudip Kumar Naskar"], "categories": ["cs.LG", "cs.CL", "cs.SD", "eess.AS", "eess.SP"], "fields_of_study": ["Computer Science", "Engineering"], "published_date": "2021-10-05", "url": "https://arxiv.org/abs/2110.03427", "pdf_url": "https://arxiv.org/pdf/2110.03427v3", "arxiv_id": "2110.03427", "doi": "10.1017/nlp.2024.22", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Social Science Research Network", "quality_score": 0.25}
{"id": "96a19c5b438ef8ec6c9d736475929dde3896228bf9d19bec5f9344b86a13bd01", "sources": ["arxiv", "semantic_scholar"], "title": "Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning", "abstract": "Though language model text embeddings have revolutionized NLP research, their ability to capture high-level semantic information, such as relations between entities in text, is limited. In this paper, we propose a novel contrastive learning framework that trains sentence embeddings to encode the relations in a graph structure. Given a sentence (unstructured text) and its graph, we use contrastive learning to impose relation-related structure on the token-level representations of the sentence obtained with a CharacterBERT (El Boukkouri et al.,2020) model. The resulting relation-aware sentence embeddings achieve state-of-the-art results on the relation extraction task using only a simple KNN classifier, thereby demonstrating the success of the proposed method. Additional visualization by a tSNE analysis shows the effectiveness of the learned representation space compared to baselines. Furthermore, we show that we can learn a different space for named entity recognition, again using a contrastive learning objective, and demonstrate how to successfully combine both representation spaces in an entity-relation task.", "authors": ["Christos Theodoropoulos", "James Henderson", "Andrei C. Coman", "Marie-Francine Moens"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2021-09-02", "url": "https://arxiv.org/abs/2109.00840", "pdf_url": "https://arxiv.org/pdf/2109.00840v2", "arxiv_id": "2109.00840", "doi": "10.18653/v1/2021.conll-1.27", "citation_count": 18, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Conference on Computational Natural Language Learning", "quality_score": 0.3197}
{"id": "1b03ddf9afc17cb0f5624526e4ce869ed782a872eea294f651be5a373f23eb48", "sources": ["arxiv", "semantic_scholar"], "title": "Modeling Protein Using Large-scale Pretrain Language Model", "abstract": "Protein is linked to almost every life process. Therefore, analyzing the biological structure and property of protein sequences is critical to the exploration of life, as well as disease detection and drug discovery. Traditional protein analysis methods tend to be labor-intensive and time-consuming. The emergence of deep learning models makes modeling data patterns in large quantities of data possible. Interdisciplinary researchers have begun to leverage deep learning methods to model large biological datasets, e.g. using long short-term memory and convolutional neural network for protein sequence classification. After millions of years of evolution, evolutionary information is encoded in protein sequences. Inspired by the similarity between natural language and protein sequences, we use large-scale language models to model evolutionary-scale protein sequences, encoding protein biology information in representation. Significant improvements are observed in both token-level and sequence-level tasks, demonstrating that our large-scale model can accurately capture evolution information from pretraining on evolutionary-scale individual sequences. Our code and model are available at https://github.com/THUDM/ProteinLM.", "authors": ["Yijia Xiao", "Jiezhong Qiu", "Ziang Li", "Chang-Yu Hsieh", "Jie Tang"], "categories": ["cs.LG", "cs.CL", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2021-08-17", "url": "https://arxiv.org/abs/2108.07435", "pdf_url": "https://arxiv.org/pdf/2108.07435v2", "arxiv_id": "2108.07435", "doi": null, "citation_count": 41, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/THUDM/ProteinLM", "venue": "arXiv.org", "quality_score": 0.4058}
{"id": "a55190c481c3d245c9750dca378a84382b9a3b52bf66896b377f790ed41f55a5", "sources": ["arxiv", "semantic_scholar"], "title": "AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models", "abstract": "Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT (Devlin et al., 2019). Few studies have been conducted to explore the design of architecture hyper-parameters in BERT, especially for the more efficient PLMs with tiny sizes, which are essential for practical deployment on resource-constrained devices. In this paper, we adopt the one-shot Neural Architecture Search (NAS) to automatically search architecture hyper-parameters. Specifically, we carefully design the techniques of one-shot learning and the search space to provide an adaptive and efficient development way of tiny PLMs for various latency constraints. We name our method AutoTinyBERT and evaluate its effectiveness on the GLUE and SQuAD benchmarks. The extensive experiments show that our method outperforms both the SOTA search-based baseline (NAS-BERT) and the SOTA distillation-based methods (such as DistilBERT, TinyBERT, MiniLM and MobileBERT). In addition, based on the obtained architectures, we propose a more efficient development method that is even faster than the development of a single PLM.", "authors": ["Yichun Yin", "Cheng Chen", "Lifeng Shang", "Xin Jiang", "Xiao Chen", "Qun Liu"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2021-07-29", "url": "https://arxiv.org/abs/2107.13686", "pdf_url": "https://arxiv.org/pdf/2107.13686v1", "arxiv_id": "2107.13686", "doi": "10.18653/v1/2021.acl-long.400", "citation_count": 52, "influential_citation_count": 6, "has_code": true, "code_url": "https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/AutoTinyBERT", "venue": "Annual Meeting of the Association for Computational Linguistics", "quality_score": 0.4311}
{"id": "9b6b8c235ec0bba8eb20494e2462915a47d369000b3931a7ffbe690f2b19d184", "sources": ["arxiv", "semantic_scholar"], "title": "gaBERT -- an Irish Language Model", "abstract": "The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community.", "authors": ["James Barry", "Joachim Wagner", "Lauren Cassidy", "Alan Cowap", "Teresa Lynn", "Abigail Walsh", "Mícheál J. Ó Meachair", "Jennifer Foster"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2021-07-27", "url": "https://arxiv.org/abs/2107.12930", "pdf_url": "https://arxiv.org/pdf/2107.12930v4", "arxiv_id": "2107.12930", "doi": "10.63317/2c485c2jd2yz", "citation_count": 21, "influential_citation_count": 3, "has_code": false, "code_url": null, "venue": "International Conference on Language Resources and Evaluation", "quality_score": 0.3356}
{"id": "315cd8f83a1bd08ca91fb430280a87acc68c3971b6987e57ae7d7dad9aafb99d", "sources": ["arxiv", "semantic_scholar"], "title": "Protein-RNA interaction prediction with deep learning: Structure matters", "abstract": "Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Due to the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods rely heavily on the sequence data, with only a small portion of the methods utilizing the structural information. Recently, AlphaFold has revolutionized the entire protein and biology field. Foreseeably, the protein-RNA interaction prediction will also be promoted significantly in the upcoming years. In this work, we give a thorough review of this field, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features, and models. We also point out the potential challenges and opportunities in this field. This survey summarizes the development of the RBP-RNA interaction field in the past and foresees its future development in the post-AlphaFold era.", "authors": ["Junkang Wei", "Siyuan Chen", "Licheng Zong", "Xin Gao", "Yu Li"], "categories": ["q-bio.BM", "cs.LG", "cs.NE"], "fields_of_study": ["Computer Science", "Medicine", "Biology"], "published_date": "2021-07-26", "url": "https://arxiv.org/abs/2107.12243", "pdf_url": "https://arxiv.org/pdf/2107.12243v2", "arxiv_id": "2107.12243", "doi": "10.1093/bib/bbab540", "citation_count": 79, "influential_citation_count": 4, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.4758}
{"id": "6b8d57645149a31349de2dffc4c7e38b87246b6c30d7f95530efdef59bd5219f", "sources": ["arxiv", "semantic_scholar"], "title": "Picard understanding Darmok: A Dataset and Model for Metaphor-Rich Translation in a Constructed Language", "abstract": "Tamarian, a fictional language introduced in the Star Trek episode Darmok, communicates meaning through utterances of metaphorical references, such as \"Darmok and Jalad at Tanagra\" instead of \"We should work together.\" This work assembles a Tamarian-English dictionary of utterances from the original episode and several follow-on novels, and uses this to construct a parallel corpus of 456 English-Tamarian utterances. A machine translation system based on a large language model (T5) is trained using this parallel corpus, and is shown to produce an accuracy of 76% when translating from English to Tamarian on known utterances.", "authors": ["Peter Jansen", "Jordan Boyd-Graber"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2021-07-16", "url": "https://arxiv.org/abs/2107.08146", "pdf_url": "https://arxiv.org/pdf/2107.08146v2", "arxiv_id": "2107.08146", "doi": "10.18653/v1/2022.flp-1.5", "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "ff282fac1d8adb608ecbc9ad87b37039c494723216eacafc288086ab6381f1bf", "sources": ["arxiv", "semantic_scholar"], "title": "DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction", "abstract": "How and where proteins interface with one another can ultimately impact the proteins' functions along with a range of other biological processes. As such, precise computational methods for protein interface prediction (PIP) come highly sought after as they could yield significant advances in drug discovery and design as well as protein function analysis. However, the traditional benchmark dataset for this task, Docking Benchmark 5 (DB5), contains only a modest 230 complexes for training, validating, and testing different machine learning algorithms. In this work, we expand on a dataset recently introduced for this task, the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for geometric deep learning of protein interfaces. The previous version of DIPS contains only the Cartesian coordinates and types of the atoms comprising a given protein complex, whereas DIPS-Plus now includes a plethora of new residue-level features including protrusion indices, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid, giving researchers a large, well-curated feature bank for training protein interface prediction methods. We demonstrate through rigorous benchmarks that training an existing state-of-the-art (SOTA) model for PIP on DIPS-Plus yields SOTA results, surpassing the performance of all other models trained on residue-level and atom-level encodings of protein complexes to date.", "authors": ["Alex Morehead", "Chen Chen", "Ada Sedova", "Jianlin Cheng"], "categories": ["q-bio.QM", "cs.LG", "q-bio.BM"], "fields_of_study": ["Medicine", "Biology", "Computer Science"], "published_date": "2021-06-06", "url": "https://arxiv.org/abs/2106.04362", "pdf_url": "https://arxiv.org/pdf/2106.04362v3", "arxiv_id": "2106.04362", "doi": "10.1038/s41597-023-02409-3", "citation_count": 29, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Scientific Data", "quality_score": 0.3693}
{"id": "5dbb4b09550a8cdcdc7e34a92d75c4e0c8114da7fd3ad441f18ba7f5627e88ec", "sources": ["arxiv", "semantic_scholar"], "title": "Investigating Math Word Problems using Pretrained Multilingual Language Models", "abstract": "In this paper, we revisit math word problems~(MWPs) from the cross-lingual and multilingual perspective. We construct our MWP solvers over pretrained multilingual language models using sequence-to-sequence model with copy mechanism. We compare how the MWP solvers perform in cross-lingual and multilingual scenarios. To facilitate the comparison of cross-lingual performance, we first adapt the large-scale English dataset MathQA as a counterpart of the Chinese dataset Math23K. Then we extend several English datasets to bilingual datasets through machine translation plus human annotation. Our experiments show that the MWP solvers may not be transferred to a different language even if the target expressions have the same operator set and constants. But for both cross-lingual and multilingual cases, it can be better generalized if problem types exist on both source language and target language.", "authors": ["Minghuan Tan", "Lei Wang", "Lingxiao Jiang", "Jing Jiang"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2021-05-19", "url": "https://arxiv.org/abs/2105.08928", "pdf_url": "https://arxiv.org/pdf/2105.08928v3", "arxiv_id": "2105.08928", "doi": "10.18653/v1/2022.mathnlp-1.2", "citation_count": 36, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.4225}
{"id": "9924ae9c2084d90d5563a150c8d59036764e1a07efe60ac93871a8ec1f556325", "sources": ["arxiv", "semantic_scholar"], "title": "Updated Standard Model Prediction for $K \\to πν\\barν$ and $ε_K$", "abstract": "The rare $K \\to πν\\barν$ decay modes and the parameter $ε_K$ that measures CP violation in Kaon mixing are sensitive probes of physics beyond the standard model. In this article we provide the updated standard-model prediction for the rare decay modes in detail, and summarise the status of standard-model prediction of $ε_K$. We find $\\text{BR}(K^+ \\to π^+ ν\\bar ν) = 7.73(61) \\times 10^{-11}$ and $\\text{BR}(K_L \\to π^0 ν\\bar ν) = 2.59(29) \\times 10^{-11}$. The uncertainties are dominated by parametric input.", "authors": ["Joachim Brod", "Martin Gorbahn", "Emmanuel Stamou"], "categories": ["hep-ph", "hep-ex"], "fields_of_study": ["Physics"], "published_date": "2021-05-06", "url": "https://arxiv.org/abs/2105.02868", "pdf_url": "https://arxiv.org/pdf/2105.02868v1", "arxiv_id": "2105.02868", "doi": "10.22323/1.391.0056", "citation_count": 34, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.386}
{"id": "8eb8cbbb603d164f56aaa277f0f28ca4a5c96e6463dcd16b185bd9b85c41ee57", "sources": ["arxiv", "semantic_scholar"], "title": "Markov State Models of protein-protein encounters", "abstract": "This chapter reviews how molecular dynamics simulations, experimental data, and Markov state models can synergize to map-out the mechanism of protein-protein association and dissociation.", "authors": ["Simon Olsson"], "categories": ["physics.bio-ph", "physics.chem-ph", "physics.comp-ph", "q-bio.BM"], "fields_of_study": ["Physics", "Biology"], "published_date": "2021-05-06", "url": "https://arxiv.org/abs/2105.02767", "pdf_url": "https://arxiv.org/pdf/2105.02767v1", "arxiv_id": "2105.02767", "doi": null, "citation_count": 4, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1747}
{"id": "bdfd894f4c463bc00bce4c23d6489ac88c02c99a005e613b883f1aab89690e7f", "sources": ["arxiv", "semantic_scholar"], "title": "HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish", "abstract": "BERT-based models are currently used for solving nearly all Natural Language Processing (NLP) tasks and most often achieve state-of-the-art results. Therefore, the NLP community conducts extensive research on understanding these models, but above all on designing effective and efficient training procedures. Several ablation studies investigating how to train BERT-like models have been carried out, but the vast majority of them concerned only the English language. A training procedure designed for English does not have to be universal and applicable to other especially typologically different languages. Therefore, this paper presents the first ablation study focused on Polish, which, unlike the isolating English language, is a fusional language. We design and thoroughly evaluate a pretraining procedure of transferring knowledge from multilingual to monolingual BERT-based models. In addition to multilingual model initialization, other factors that possibly influence pretraining are also explored, i.e. training objective, corpus size, BPE-Dropout, and pretraining length. Based on the proposed procedure, a Polish BERT-based language model -- HerBERT -- is trained. This model achieves state-of-the-art results on multiple downstream tasks.", "authors": ["Robert Mroczkowski", "Piotr Rybak", "Alina Wróblewska", "Ireneusz Gawlik"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2021-05-04", "url": "https://arxiv.org/abs/2105.01735", "pdf_url": "https://arxiv.org/pdf/2105.01735v1", "arxiv_id": "2105.01735", "doi": null, "citation_count": 100, "influential_citation_count": 6, "has_code": false, "code_url": null, "venue": "Workshop on Balto-Slavic Natural Language Processing", "quality_score": 0.5011}
{"id": "7f3b5d9e7b39b74aa82ccd2dc9cd3c8bf4cf6f03757ea85bea07fadf9aae7a3c", "sources": ["arxiv", "semantic_scholar"], "title": "An Automated Multiple-Choice Question Generation Using Natural Language Processing Techniques", "abstract": "Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data. Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are important words in a given lesson material. To validate that the system is not perverse, five lesson materials were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the teacher were compared to the auto-generated keywords and the result shows that the system was capable of extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a user-friendly interface for easy accessibility.", "authors": ["Chidinma A. Nwafor", "Ikechukwu E. Onyenwe"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2021-03-26", "url": "https://arxiv.org/abs/2103.14757", "pdf_url": "https://arxiv.org/pdf/2103.14757v1", "arxiv_id": "2103.14757", "doi": "10.5121/ijnlc.2021.10201", "citation_count": 34, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Journal on Natural Language Computing", "quality_score": 0.386}
{"id": "05752e39d929a66d892585069dc76fd1f3d0fa24ac56b004fc15c8fa5d28f33a", "sources": ["arxiv", "semantic_scholar"], "title": "Topical Language Generation using Transformers", "abstract": "Large-scale transformer-based language models (LMs) demonstrate impressive capabilities in open text generation. However, controlling the generated text's properties such as the topic, style, and sentiment is challenging and often requires significant changes to the model architecture or retraining and fine-tuning the model on new supervised data. This paper presents a novel approach for Topical Language Generation (TLG) by combining a pre-trained LM with topic modeling information. We cast the problem using Bayesian probability formulation with topic probabilities as a prior, LM probabilities as the likelihood, and topical language generation probability as the posterior. In learning the model, we derive the topic probability distribution from the user-provided document's natural structure. Furthermore, we extend our model by introducing new parameters and functions to influence the quantity of the topical features presented in the generated text. This feature would allow us to easily control the topical properties of the generated text. Our experimental results demonstrate that our model outperforms the state-of-the-art results on coherency, diversity, and fluency while being faster in decoding.", "authors": ["Rohola Zandie", "Mohammad H. Mahoor"], "categories": ["cs.CL", "cs.AI"], "fields_of_study": ["Computer Science"], "published_date": "2021-03-11", "url": "https://arxiv.org/abs/2103.06434", "pdf_url": "https://arxiv.org/pdf/2103.06434v1", "arxiv_id": "2103.06434", "doi": "10.1017/s1351324922000031", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Natural Language Engineering", "quality_score": 0.25}
{"id": "b8cc755d285058618cf4d1ebb8b2171f13e1b11486bc451ffd9469e08a167133", "sources": ["arxiv", "semantic_scholar"], "title": "Local sequence-structure relationships in proteins", "abstract": "We seek to understand the interplay between amino acid sequence and local structure in proteins. Are some amino acids unique in their ability to fit harmoniously into certain local structures? What is the role of sequence in sculpting the putative native state folds from myriad possible conformations? In order to address these questions, we represent the local structure of each C-alpha atom of a protein by just two angles, theta and mu, and we analyze a set of more than 4000 protein structures from the PDB. We use a hierarchical clustering scheme to divide the 20 amino acids into six distinct groups based on their similarity to each other in fitting local structural space. We present the results of a detailed analysis of patterns of amino acid specificity in adopting local structural conformations and show that the sequence-structure correlation is not very strong compared to a random assignment of sequence to structure. Yet, our analysis may be useful to determine an effective scoring rubric for quantifying the match of an amino acid to its putative local structure.", "authors": ["Tatjana Škrbić", "Amos Maritan", "Achille Giacometti", "Jayanth R. Banavar"], "categories": ["q-bio.BM", "cond-mat.soft", "cond-mat.stat-mech", "physics.bio-ph"], "fields_of_study": ["Medicine", "Biology", "Physics"], "published_date": "2021-01-27", "url": "https://arxiv.org/abs/2101.11724", "pdf_url": "https://arxiv.org/pdf/2101.11724v1", "arxiv_id": "2101.11724", "doi": "10.1002/pro.4032", "citation_count": 8, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Protein Science", "quality_score": 0.2386}
{"id": "825f0ed195d2e49fedc7adbfcdedb05ca807ee6efff89f8d6a2675ec96b34d69", "sources": ["arxiv", "semantic_scholar"], "title": "The IITM Earth System Model (IITM ESM)", "abstract": "Earth System Models (ESM) are important tools that allow us to understand and quantify the physical, chemical & biological mechanisms governing the rates of change of elements of the Earth System, comprising of the atmosphere, ocean, land, cryosphere and biosphere (terrestrial and marine) and related components. ESMs are essentially coupled numerical models which incorporate processes within and across the different Earth system components and are expressed as set of mathematical equations. ESMs are useful for enhancing our fundamental understanding of the climate system, its multi-scale variability, global and regional climatic phenomena and making projections of future climate change. In this chapter, we briefly describe the salient aspects of the Indian Institute of Tropical Meteorology ESM (IITM ESM), that has been developed recently at the IITM, Pune, India, for investigating long-term climate variability and change with focus on the South Asian monsoon.", "authors": ["R. Krishnan", "P. Swapna", "Ayantika Dey Choudhury", "Sandeep Narayansetti", "A. G. Prajeesh", "Manmeet Singh", "Aditi Modi", "Roxy Mathew", "Ramesh Vellore", "J. Jyoti", "T. P. Sabin", "J. Sanjay", "Sandip Ingle"], "categories": ["physics.ao-ph"], "fields_of_study": ["Physics"], "published_date": "2021-01-09", "url": "https://arxiv.org/abs/2101.03410", "pdf_url": "https://arxiv.org/pdf/2101.03410v1", "arxiv_id": "2101.03410", "doi": null, "citation_count": 11, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.2698}
{"id": "cc61b8e181b27b78cb4f982172db2360a71e231d508cd710368c15e5d2c6cbdb", "sources": ["arxiv", "semantic_scholar"], "title": "Universal Sentence Representation Learning with Conditional Masked Language Model", "abstract": "This paper presents a novel training method, Conditional Masked Language Modeling (CMLM), to effectively learn sentence representations on large scale unlabeled corpora. CMLM integrates sentence representation learning into MLM training by conditioning on the encoded vectors of adjacent sentences. Our English CMLM model achieves state-of-the-art performance on SentEval, even outperforming models learned using supervised signals. As a fully unsupervised learning method, CMLM can be conveniently extended to a broad range of languages and domains. We find that a multilingual CMLM model co-trained with bitext retrieval (BR) and natural language inference (NLI) tasks outperforms the previous state-of-the-art multilingual models by a large margin, e.g. 10% improvement upon baseline models on cross-lingual semantic search. We explore the same language bias of the learned representations, and propose a simple, post-training and model agnostic approach to remove the language identifying information from the representation while still retaining sentence semantics.", "authors": ["Ziyi Yang", "Yinfei Yang", "Daniel Cer", "Jax Law", "Eric Darve"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2020-12-28", "url": "https://arxiv.org/abs/2012.14388", "pdf_url": "https://arxiv.org/pdf/2012.14388v3", "arxiv_id": "2012.14388", "doi": "10.18653/v1/2021.emnlp-main.502", "citation_count": 64, "influential_citation_count": 9, "has_code": false, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.5}
{"id": "e8075105a3a55dca7d7d0708e25908ed15885be60e8ef1b36219ab675338bd3b", "sources": ["arxiv", "semantic_scholar"], "title": "Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks", "abstract": "Less than 1% of protein sequences are structurally and functionally annotated. Natural Language Processing (NLP) community has recently embraced self-supervised learning as a powerful approach to learn representations from unlabeled text, in large part due to the attention-based context-aware Transformer models. In this work we present a modification to the RoBERTa model by inputting during pre-training a mixture of binding and non-binding protein sequences (from STRING database). However, the sequence pairs have no label to indicate their binding status, as the model relies solely on Masked Language Modeling (MLM) objective during pre-training. After fine-tuning, such approach surpasses models trained on single protein sequences for protein-protein binding prediction, TCR-epitope binding prediction, cellular-localization and remote homology classification tasks. We suggest that the Transformer's attention mechanism contributes to protein binding site discovery. Furthermore, we compress protein sequences by 64% with the Byte Pair Encoding (BPE) vocabulary consisting of 10K subwords, each around 3-4 amino acids long. Finally, to expand the model input space to even larger proteins and multi-protein assemblies, we pre-train Longformer models that support 2,048 tokens. Further work in token-level classification for secondary structure prediction is needed. Code available at: https://github.com/PaccMann/paccmann_proteomics", "authors": ["Modestas Filipavicius", "Matteo Manica", "Joris Cadow", "Maria Rodriguez Martinez"], "categories": ["q-bio.BM", "cs.CL"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2020-12-05", "url": "https://arxiv.org/abs/2012.03084", "pdf_url": "https://arxiv.org/pdf/2012.03084v1", "arxiv_id": "2012.03084", "doi": null, "citation_count": 18, "influential_citation_count": 3, "has_code": true, "code_url": "https://github.com/PaccMann/paccmann_proteomics", "venue": "arXiv.org", "quality_score": 0.3197}
{"id": "3628ee50dcdadcb6df3b97d1f02ef663cb03389eccf8cac29b9b636a36987a93", "sources": ["arxiv", "semantic_scholar"], "title": "Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models", "abstract": "For protein sequence datasets, unlabeled data has greatly outpaced labeled data due to the high cost of wet-lab characterization. Recent deep-learning approaches to protein prediction have shown that pre-training on unlabeled data can yield useful representations for downstream tasks. However, the optimal pre-training strategy remains an open question. Instead of strictly borrowing from natural language processing (NLP) in the form of masked or autoregressive language modeling, we introduce a new pre-training task: directly predicting protein profiles derived from multiple sequence alignments. Using a set of five, standardized downstream tasks for protein models, we demonstrate that our pre-training task along with a multi-task objective outperforms masked language modeling alone on all five tasks. Our results suggest that protein sequence models may benefit from leveraging biologically-inspired inductive biases that go beyond existing language modeling techniques in NLP.", "authors": ["Pascal Sturmfels", "Jesse Vig", "Ali Madani", "Nazneen Fatema Rajani"], "categories": ["cs.LG", "q-bio.BM"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2020-12-01", "url": "https://arxiv.org/abs/2012.00195", "pdf_url": "https://arxiv.org/pdf/2012.00195v1", "arxiv_id": "2012.00195", "doi": null, "citation_count": 27, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3618}
{"id": "f64108a1d772a0e0eb9ccd0faf87cf879461ecba66eaedd28140cfb54f8472c5", "sources": ["arxiv", "semantic_scholar"], "title": "PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction", "abstract": "Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While it is known that protein structure directly impacts protein function, many functional prediction tasks use only protein sequence. In this work, we isolate protein structure to make functional annotations for proteins in the Protein Data Bank in order to study the expressiveness of different structure-based prediction schemes. We present PersGNN - an end-to-end trainable deep learning model that combines graph representation learning with topological data analysis to capture a complex set of both local and global structural features. While variations of these techniques have been successfully applied to proteins before, we demonstrate that our hybridized approach, PersGNN, outperforms either method on its own as well as a baseline neural network that learns from the same information. PersGNN achieves a 9.3% boost in area under the precision recall curve (AUPR) compared to the best individual model, as well as high F1 scores across different gene ontology categories, indicating the transferability of this approach.", "authors": ["Nicolas Swenson", "Aditi S. Krishnapriyan", "Aydin Buluc", "Dmitriy Morozov", "Katherine Yelick"], "categories": ["q-bio.BM", "cs.LG", "math.AT"], "fields_of_study": ["Computer Science", "Biology", "Mathematics"], "published_date": "2020-10-30", "url": "https://arxiv.org/abs/2010.16027", "pdf_url": "https://arxiv.org/pdf/2010.16027v1", "arxiv_id": "2010.16027", "doi": null, "citation_count": 20, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.3306}
{"id": "7c4c6536f9335766e1d8c5fa8533219c53bda72c83dc66ca7a4dff2370773510", "sources": ["arxiv", "semantic_scholar"], "title": "Discourse structure interacts with reference but not syntax in neural language models", "abstract": "Language models (LMs) trained on large quantities of text have been claimed to acquire abstract linguistic representations. Our work tests the robustness of these abstractions by focusing on the ability of LMs to learn interactions between different linguistic representations. In particular, we utilized stimuli from psycholinguistic studies showing that humans can condition reference (i.e. coreference resolution) and syntactic processing on the same discourse structure (implicit causality). We compared both transformer and long short-term memory LMs to find that, contrary to humans, implicit causality only influences LM behavior for reference, not syntax, despite model representations that encode the necessary discourse information. Our results further suggest that LM behavior can contradict not only learned representations of discourse but also syntactic agreement, pointing to shortcomings of standard language modeling.", "authors": ["Forrest Davis", "Marten van Schijndel"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2020-10-10", "url": "https://arxiv.org/abs/2010.04887", "pdf_url": "https://arxiv.org/pdf/2010.04887v1", "arxiv_id": "2010.04887", "doi": "10.18653/v1/2020.conll-1.32", "citation_count": 24, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Conference on Computational Natural Language Learning", "quality_score": 0.3495}
{"id": "6715eb9a92f183ed98070ccc830a72b151925ef17e87dd41cb02ee0fca179ebf", "sources": ["arxiv", "semantic_scholar"], "title": "Predictive Modeling of Anatomy with Genetic and Clinical Data", "abstract": "We present a semi-parametric generative model for predicting anatomy of a patient in subsequent scans following a single baseline image. Such predictive modeling promises to facilitate novel analyses in both voxel-level studies and longitudinal biomarker evaluation. We capture anatomical change through a combination of population-wide regression and a non-parametric model of the subject's health based on individual genetic and clinical indicators. In contrast to classical correlation and longitudinal analysis, we focus on predicting new observations from a single subject observation. We demonstrate prediction of follow-up anatomical scans in the ADNI cohort, and illustrate a novel analysis approach that compares a patient's scans to the predicted subject-specific healthy anatomical trajectory. The code is available at https://github.com/adalca/voxelorb.", "authors": ["Adrian V. Dalca", "Ramesh Sridharan", "Mert R. Sabuncu", "Polina Golland"], "categories": ["cs.CV"], "fields_of_study": ["Computer Science", "Medicine"], "published_date": "2020-10-09", "url": "https://arxiv.org/abs/2010.04757", "pdf_url": "https://arxiv.org/pdf/2010.04757v1", "arxiv_id": "2010.04757", "doi": "10.1007/978-3-319-24574-4_62", "citation_count": 7, "influential_citation_count": 1, "has_code": true, "code_url": "https://github.com/adalca/voxelorb", "venue": "International Conference on Medical Image Computing and Computer-Assisted Intervention", "quality_score": 0.2258}
{"id": "d58b9996833d4e39c3d5231db2003bb7501bf1d7a92fc6c450a3d3a83d31e77d", "sources": ["arxiv", "semantic_scholar"], "title": "PS8-Net: A Deep Convolutional Neural Network to Predict the Eight-State Protein Secondary Structure", "abstract": "Protein secondary structure is crucial to creating an information bridge between the primary and tertiary (3D) structures. Precise prediction of eight-state protein secondary structure (PSS) has significantly utilized in the structural and functional analysis of proteins in bioinformatics. Deep learning techniques have been recently applied in this research area and raised the eight-state (Q8) protein secondary structure prediction accuracy remarkably. Nevertheless, from a theoretical standpoint, there are still lots of rooms for improvement, specifically in the eight-state PSS prediction. In this study, we have presented a new deep convolutional neural network (DCNN), namely PS8-Net, to enhance the accuracy of eight-class PSS prediction. The input of this architecture is a carefully constructed feature matrix from the proteins sequence features and profile features. We introduce a new PS8 module in the network, which is applied with skip connection to extracting the long-term inter-dependencies from higher layers, obtaining local contexts in earlier layers, and achieving global information during secondary structure prediction. Our proposed PS8-Net achieves 76.89%, 71.94%, 76.86%, and 75.26% Q8 accuracy respectively on benchmark CullPdb6133, CB513, CASP10, and CASP11 datasets. This architecture enables the efficient processing of local and global interdependencies between amino acids to make an accurate prediction of each class. To the best of our knowledge, PS8-Net experiment results demonstrate that it outperforms all the state-of-the-art methods on the aforementioned benchmark datasets.", "authors": ["Md Aminur Rab Ratul", "Maryam Tavakol Elahi", "M. Hamed Mozaffari", "WonSook Lee"], "categories": ["cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Mathematics"], "published_date": "2020-09-22", "url": "https://arxiv.org/abs/2009.10380", "pdf_url": "https://arxiv.org/pdf/2009.10380v1", "arxiv_id": "2009.10380", "doi": "10.1109/DICTA51227.2020.9363393", "citation_count": 6, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "International Conference on Digital Image Computing: Techniques and Applications", "quality_score": 0.2113}
{"id": "d1fbbc4fc04cea11c50503e68b3173c1d45d63908664588cb1d635accb6430aa", "sources": ["arxiv", "semantic_scholar"], "title": "Unsupervised and Supervised Structure Learning for Protein Contact Prediction", "abstract": "Protein contacts provide key information for the understanding of protein structure and function, and therefore contact prediction from sequences is an important problem. Recent research shows that some correctly predicted long-range contacts could help topology-level structure modeling. Thus, contact prediction and contact-assisted protein folding also proves the importance of this problem. In this thesis, I will briefly introduce the extant related work, then show how to establish the contact prediction through unsupervised graphical models with topology constraints. Further, I will explain how to use the supervised deep learning methods to further boost the accuracy of contact prediction. Finally, I will propose a scoring system called diversity score to measure the novelty of contact predictions, as well as an algorithm that predicts contacts with respect to the new scoring system.", "authors": ["Siqi Sun"], "categories": ["q-bio.QM", "cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Biology", "Mathematics"], "published_date": "2020-08-31", "url": "https://arxiv.org/abs/2009.00133", "pdf_url": "https://arxiv.org/pdf/2009.00133v1", "arxiv_id": "2009.00133", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.0}
{"id": "7bf8d231344229cac70d58052cefdadc2ad88cc515e039199a2ac16ee725b5e2", "sources": ["arxiv", "semantic_scholar"], "title": "Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems", "abstract": "Task-oriented dialogue systems use four connected modules, namely, Natural Language Understanding (NLU), a Dialogue State Tracking (DST), Dialogue Policy (DP) and Natural Language Generation (NLG). A research challenge is to learn each module with the least amount of samples (i.e., few-shots) given the high cost related to the data collection. The most common and effective technique to solve this problem is transfer learning, where large language models, either pre-trained on text or task-specific data, are fine-tuned on the few samples. These methods require fine-tuning steps and a set of parameters for each task. Differently, language models, such as GPT-2 (Radford et al., 2019) and GPT-3 (Brown et al., 2020), allow few-shot learning by priming the model with few examples. In this paper, we evaluate the priming few-shot ability of language models in the NLU, DST, DP and NLG tasks. Importantly, we highlight the current limitations of this approach, and we discuss the possible implication for future work.", "authors": ["Andrea Madotto", "Zihan Liu", "Zhaojiang Lin", "Pascale Fung"], "categories": ["cs.CL", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2020-08-14", "url": "https://arxiv.org/abs/2008.06239", "pdf_url": "https://arxiv.org/pdf/2008.06239v2", "arxiv_id": "2008.06239", "doi": null, "citation_count": 63, "influential_citation_count": 2, "has_code": true, "code_url": "https://github.com/andreamad8/TASK-ORIENTED-LM-FEWSHOT", "venue": "arXiv.org", "quality_score": 0.4515}
{"id": "cca103fdc170fc9f1add89cf2d01f0afb2875dadceeab507e946201cbf42728c", "sources": ["arxiv", "semantic_scholar"], "title": "Deep Learning in Protein Structural Modeling and Design", "abstract": "Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior of a protein, is critical to understand and engineer biological systems at the molecular level. In this review, we summarize the recent advances in applying deep learning techniques to tackle problems in protein structural modeling and design. We dissect the emerging approaches using deep learning techniques for protein structural modeling, and discuss advances and challenges that must be addressed. We argue for the central importance of structure, following the \"sequence -> structure -> function\" paradigm. This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically meaningful problems that may benefit from deep learning techniques.", "authors": ["Wenhao Gao", "Sai Pooja Mahajan", "Jeremias Sulam", "Jeffrey J. Gray"], "categories": ["q-bio.BM", "cs.LG"], "fields_of_study": ["Computer Science", "Biology", "Medicine"], "published_date": "2020-07-16", "url": "https://arxiv.org/abs/2007.08383", "pdf_url": "https://arxiv.org/pdf/2007.08383v1", "arxiv_id": "2007.08383", "doi": "10.1016/j.patter.2020.100142", "citation_count": 189, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Patterns", "quality_score": 0.5697}
{"id": "ed887cdf04fa19b0c90dd26e26a9da1cc9e56b235b3d32a07ed93d5eef303433", "sources": ["arxiv", "semantic_scholar"], "title": "Near-complete protein structural modelling of the minimal genome", "abstract": "Protein tertiary structure prediction has improved dramatically in recent years. A considerable fraction of various proteomes can be modelled in the absence of structural templates. We ask whether our DMPfold method can model all the proteins without templates in the JCVI-syn3.0 minimal genome, which contains 438 proteins. We find that a useful tertiary structure annotation can be provided for all but 10 proteins. The models may help annotate function in cases where it is unknown, and provide coverage for 29 predicted protein-protein interactions which lacked monomer models. We also show that DMPfold performs well on proteins with structures released since initial publication. It is likely that the minimal genome will have complete structural coverage within a few years.", "authors": ["Joe G Greener", "Nikita Desai", "Shaun M Kandathil", "David T Jones"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology"], "published_date": "2020-07-13", "url": "https://arxiv.org/abs/2007.06623", "pdf_url": "https://arxiv.org/pdf/2007.06623v1", "arxiv_id": "2007.06623", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.1193}
{"id": "6f1837c9548360f384f8a1f2cc7de6e79fc3d4f32704c0576b4c04d3050d46b5", "sources": ["arxiv", "semantic_scholar"], "title": "Towards the Study of Morphological Processing of the Tangkhul Language", "abstract": "There is no or little work on natural language processing of Tangkhul language. The current work is a humble beginning of morphological processing of this language using an unsupervised approach. We use a small corpus collected from different sources of text books, short stories and articles of other topics. Based on the experiments carried out, the morpheme identification task using morphessor gives reasonable and interesting output despite using a small corpus.", "authors": ["Mirinso Shadang", "Navanath Saharia", "Thoudam Doren Singh"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2020-06-29", "url": "https://arxiv.org/abs/2006.16212", "pdf_url": "https://arxiv.org/pdf/2006.16212v1", "arxiv_id": "2006.16212", "doi": null, "citation_count": 3, "influential_citation_count": 1, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1505}
{"id": "368f0a0337d8c85d03657efb131a744874d75349e114b2170b05109174313b14", "sources": ["arxiv", "semantic_scholar"], "title": "Experience Grounds Language", "abstract": "Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world. It is this shared experience that makes utterances meaningful. Natural language processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.", "authors": ["Yonatan Bisk", "Ari Holtzman", "Jesse Thomason", "Jacob Andreas", "Yoshua Bengio", "Joyce Chai", "Mirella Lapata", "Angeliki Lazaridou", "Jonathan May", "Aleksandr Nisnevich", "Nicolas Pinto", "Joseph Turian"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2020-04-21", "url": "https://arxiv.org/abs/2004.10151", "pdf_url": "https://arxiv.org/pdf/2004.10151v3", "arxiv_id": "2004.10151", "doi": "10.18653/v1/2020.emnlp-main.703", "citation_count": 433, "influential_citation_count": 15, "has_code": false, "code_url": null, "venue": "Conference on Empirical Methods in Natural Language Processing", "quality_score": 0.6594}
{"id": "38fd8ff90780606899a93d07050a8c5e7a4ef9bbae8ce8f637459e323aed783f", "sources": ["arxiv", "semantic_scholar"], "title": "Towards Relevance and Sequence Modeling in Language Recognition", "abstract": "The task of automatic language identification (LID) involving multiple dialects of the same language family in the presence of noise is a challenging problem. In these scenarios, the identity of the language/dialect may be reliably present only in parts of the temporal sequence of the speech signal. The conventional approaches to LID (and for speaker recognition) ignore the sequence information by extracting long-term statistical summary of the recording assuming an independence of the feature frames. In this paper, we propose a neural network framework utilizing short-sequence information in language recognition. In particular, a new model is proposed for incorporating relevance in language recognition, where parts of speech data are weighted more based on their relevance for the language recognition task. This relevance weighting is achieved using the bidirectional long short-term memory (BLSTM) network with attention modeling. We explore two approaches, the first approach uses segment level i-vector/x-vector representations that are aggregated in the neural model and the second approach where the acoustic features are directly modeled in an end-to-end neural model. Experiments are performed using the language recognition task in NIST LRE 2017 Challenge using clean, noisy and multi-speaker speech data as well as in the RATS language recognition corpus. In these experiments on noisy LRE tasks as well as the RATS dataset, the proposed approach yields significant improvements over the conventional i-vector/x-vector based language recognition approaches as well as with other previous models incorporating sequence information.", "authors": ["Bharat Padi", "Anand Mohan", "Sriram Ganapathy"], "categories": ["eess.AS", "cs.CL", "cs.LG", "cs.SD", "stat.ML"], "fields_of_study": ["Computer Science", "Engineering", "Mathematics"], "published_date": "2020-04-02", "url": "https://arxiv.org/abs/2004.01221", "pdf_url": "https://arxiv.org/pdf/2004.01221v1", "arxiv_id": "2004.01221", "doi": "10.1109/TASLP.2020.2983580", "citation_count": 16, "influential_citation_count": 3, "has_code": true, "code_url": "https://github.com/iiscleap/lre-relevance-weighting", "venue": "IEEE/ACM Transactions on Audio Speech and Language Processing", "quality_score": 0.3076}
{"id": "bb21a6a62e8ce128eb8275b44ecea336763c4361c87b7e602c871f97f0c61565", "sources": ["arxiv", "semantic_scholar"], "title": "ProGen: Language Modeling for Protein Generation", "abstract": "Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. We pose protein engineering as an unsupervised sequence generation problem in order to leverage the exponentially growing set of proteins that lack costly, structural annotations. We train a 1.2B-parameter language model, ProGen, on ~280M protein sequences conditioned on taxonomic and keyword tags such as molecular function and cellular component. This provides ProGen with an unprecedented range of evolutionary sequence diversity and allows it to generate with fine-grained control as demonstrated by metrics based on primary sequence similarity, secondary structure accuracy, and conformational energy.", "authors": ["Ali Madani", "Bryan McCann", "Nikhil Naik", "Nitish Shirish Keskar", "Namrata Anand", "Raphael R. Eguchi", "Po-Ssu Huang", "Richard Socher"], "categories": ["q-bio.BM", "cs.LG", "stat.ML"], "fields_of_study": ["Computer Science", "Biology", "Mathematics"], "published_date": "2020-03-08", "url": "https://arxiv.org/abs/2004.03497", "pdf_url": "https://arxiv.org/pdf/2004.03497v1", "arxiv_id": "2004.03497", "doi": "10.1101/2020.03.07.982272", "citation_count": 349, "influential_citation_count": 15, "has_code": false, "code_url": null, "venue": "bioRxiv", "quality_score": 0.636}
{"id": "bd4353b428d6e789df8a33bf51f4b75a875cd7d37575787574fa8182d065d764", "sources": ["arxiv", "semantic_scholar"], "title": "Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction", "abstract": "Language-independent tokenisation (LIT) methods that do not require labelled language resources or lexicons have recently gained popularity because of their applicability in resource-poor languages. Moreover, they compactly represent a language using a fixed size vocabulary and can efficiently handle unseen or rare words. On the other hand, language-specific tokenisation (LST) methods have a long and established history, and are developed using carefully created lexicons and training resources. Unlike subtokens produced by LIT methods, LST methods produce valid morphological subwords. Despite the contrasting trade-offs between LIT vs. LST methods, their performance on downstream NLP tasks remain unclear. In this paper, we empirically compare the two approaches using semantic similarity measurement as an evaluation task across a diverse set of languages. Our experimental results covering eight languages show that LST consistently outperforms LIT when the vocabulary size is large, but LIT can produce comparable or better results than LST in many languages with comparatively smaller (i.e. less than 100K words) vocabulary sizes, encouraging the use of LIT when language-specific resources are unavailable, incomplete or a smaller model is required. Moreover, we find that smoothed inverse frequency (SIF) to be an accurate method to create word embeddings from subword embeddings for multilingual semantic similarity prediction tasks. Further analysis of the nearest neighbours of tokens show that semantically and syntactically related tokens are closely embedded in subword embedding spaces", "authors": ["Danushka Bollegala", "Ryuichi Kiryo", "Kosuke Tsujino", "Haruki Yukawa"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "fields_of_study": ["Computer Science"], "published_date": "2020-02-25", "url": "https://arxiv.org/abs/2002.11004", "pdf_url": "https://arxiv.org/pdf/2002.11004v1", "arxiv_id": "2002.11004", "doi": null, "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Language Resources and Evaluation", "quality_score": 0.2258}
{"id": "3b9b419d0adb772577a5aba087f2f60960a182b7d20e09036bacd2c6251b4f81", "sources": ["arxiv", "semantic_scholar"], "title": "A glance into the evolution of template-free protein structure prediction methodologies", "abstract": "Prediction of protein structures using computational approaches has been explored for over two decades, paving a way for more focused research and development of algorithms in comparative modelling, ab intio modelling and structure refinement protocols. A tremendous success has been witnessed in template-based modelling protocols, whereas strategies that involve template-free modelling still lag behind, specifically for larger proteins (> 150 a.a.). Various improvements have been observed in ab initio protein structure prediction methodologies overtime, with recent ones attributed to the usage of deep learning approaches to construct protein backbone structure from its amino acid sequence. This review highlights the major strategies undertaken for template-free modelling of protein structures while discussing few tools developed under each strategy. It will also briefly comment on the progress observed in the field of ab initio modelling of proteins over the course of time as seen through the evolution of CASP platform.", "authors": ["Surbhi Dhingra", "Ramanathan Sowdhamini", "Frédéric Cadet", "Bernard Offmann"], "categories": ["q-bio.QM", "q-bio.BM"], "fields_of_study": ["Biology", "Computer Science", "Medicine", "Mathematics"], "published_date": "2020-02-16", "url": "https://arxiv.org/abs/2002.06616", "pdf_url": "https://arxiv.org/pdf/2002.06616v2", "arxiv_id": "2002.06616", "doi": "10.1016/j.biochi.2020.04.026", "citation_count": 29, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "Biochimie", "quality_score": 0.3693}
{"id": "0f2d674638f24c41379064005cf33915d5fbac6d62e29a6131240478b41e8ba5", "sources": ["arxiv", "semantic_scholar"], "title": "Using physical features of protein core packing to distinguish real proteins from decoys", "abstract": "The ability to consistently distinguish real protein structures from computationally generated model decoys is not yet a solved problem. One route to distinguish real protein structures from decoys is to delineate the important physical features that specify a real protein. For example, it has long been appreciated that the hydrophobic cores of proteins contribute significantly to their stability. As a dataset of decoys to compare with real protein structures, we studied submissions to the bi-annual CASP competition (specifically CASP11, 12, and 13), in which researchers attempt to predict the structure of a protein only knowing its amino acid sequence. Our analysis reveals that many of the submissions possess cores that do not recapitulate the features that define real proteins. In particular, the model structures appear more densely packed (because of energetically unfavorable atomic overlaps), contain too few residues in the core, and have improper distributions of hydrophobic residues throughout the structure. Based on these observations, we developed a deep learning method, which incorporates key physical features of protein cores, to predict how well a computational model recapitulates the real protein structure without knowledge of the structure of the target sequence. By identifying the important features of protein structure, our method is able to rank decoys from the CASP competitions equally well, if not better than, state-of-the-art methods that incorporate many additional features.", "authors": ["Alex T. Grigas", "Zhe Mei", "John D. Treado", "Zachary A. Levine", "Lynne Regan", "Corey S. O'Hern"], "categories": ["q-bio.BM", "cond-mat.soft"], "fields_of_study": ["Computer Science", "Medicine", "Biology", "Physics"], "published_date": "2020-01-05", "url": "https://arxiv.org/abs/2001.01161", "pdf_url": "https://arxiv.org/pdf/2001.01161v1", "arxiv_id": "2001.01161", "doi": "10.1002/pro.3914", "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Protein Science", "quality_score": 0.2258}
{"id": "916714882cf142176bf06d8f8ff14970f4efe6a518600bab299694364abfd81f", "sources": ["arxiv", "semantic_scholar"], "title": "From Quantum Chemistry to Networks in Biology: A Graph Spectral Approach to Protein Structure Analyses", "abstract": "In this perspective article, we present a multidisciplinary approach for characterizing protein structure networks. We first place our approach in its historical context and describe the manner in which it synthesizes concepts from quantum chemistry, biology of polymer conformations, matrix mathematics, and percolation theory. We then explicitly provide the method for constructing the protein structure network in terms of non-covalently interacting amino acid side chains and show how a mine of information can be obtained from the graph spectra of these networks. Employing suitable mathematical approaches, such as the use of a weighted, Laplacian matrix to generate the spectra, enables us to develop rigorous methods for network comparison and to identify crucial nodes responsible for the network integrity through a perturbation approach. Our scoring methods have several applications in structural biology that are elusive to conventional methods of analyses. Here, we discuss the instances of: (a) Protein structure comparison that include the details of side chain connectivity, (b) The contribution to node clustering as a function of bound ligand, explaining the global effect of local changes in phenomena such as allostery and (c) The identification of crucial amino acids for structural integrity, derived purely from the spectra of the graph. We demonstrate how our method enables us to obtain valuable information on key proteins involved in cellular functions and diseases such as GPCR and HIV protease, and discuss the biological implications. We then briefly describe how concepts from percolation theory further augment our analyses. In our concluding perspective for future developments, we suggest a further unifying approach to protein structure analyses and a judicious choice of questions to employ our methods for larger, more complex networks, such as metabolic and disease networks.", "authors": ["Vasundhara Gadiyaram", "Smitha Vishveshwara", "Saraswathi Vishveshwara"], "categories": ["q-bio.MN", "cond-mat.stat-mech"], "fields_of_study": ["Biology", "Physics", "Medicine", "Computer Science"], "published_date": "2019-12-25", "url": "https://arxiv.org/abs/1912.11609", "pdf_url": "https://arxiv.org/pdf/1912.11609v1", "arxiv_id": "1912.11609", "doi": "10.1021/acs.jcim.9b00002", "citation_count": 31, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Journal of Chemical Information and Modeling", "quality_score": 0.3763}
{"id": "643398fd07b02c15250407516b2c6c18c5b3a5b308dc9fba6697ef9c5bfe3115", "sources": ["arxiv", "semantic_scholar"], "title": "Hierarchical Character Embeddings: Learning Phonological and Semantic Representations in Languages of Logographic Origin using Recursive Neural Networks", "abstract": "Logographs (Chinese characters) have recursive structures (i.e. hierarchies of sub-units in logographs) that contain phonological and semantic information, as developmental psychology literature suggests that native speakers leverage on the structures to learn how to read. Exploiting these structures could potentially lead to better embeddings that can benefit many downstream tasks. We propose building hierarchical logograph (character) embeddings from logograph recursive structures using treeLSTM, a recursive neural network. Using recursive neural network imposes a prior on the mapping from logographs to embeddings since the network must read in the sub-units in logographs according to the order specified by the recursive structures. Based on human behavior in language learning and reading, we hypothesize that modeling logographs' structures using recursive neural network should be beneficial. To verify this claim, we consider two tasks (1) predicting logographs' Cantonese pronunciation from logographic structures and (2) language modeling. Empirical results show that the proposed hierarchical embeddings outperform baseline approaches. Diagnostic analysis suggests that hierarchical embeddings constructed using treeLSTM is less sensitive to distractors, thus is more robust, especially on complex logographs.", "authors": ["Minh Nguyen", "Gia H. Ngo", "Nancy F. Chen"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2019-12-20", "url": "https://arxiv.org/abs/1912.09913", "pdf_url": "https://arxiv.org/pdf/1912.09913v2", "arxiv_id": "1912.09913", "doi": "10.1109/TASLP.2019.2955246", "citation_count": 21, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "IEEE/ACM Transactions on Audio Speech and Language Processing", "quality_score": 0.3356}
{"id": "3f82f523408556b365fff2d31260832c069d2c2afc3171d43c6458c1f991627f", "sources": ["arxiv", "semantic_scholar"], "title": "Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations", "abstract": "Proteins are the major building blocks of life, and actuators of almost all chemical and biophysical events in living organisms. Their native structures in turn enable their biological functions which have a fundamental role in drug design. This motivates predicting the structure of a protein from its sequence of amino acids, a fundamental problem in computational biology. In this work, we demonstrate state-of-the-art protein structure prediction (PSP) results using embeddings and deep learning models for prediction of backbone atom distance matrices and torsion angles. We recover 3D coordinates of backbone atoms and reconstruct full atom protein by optimization. We create a new gold standard dataset of proteins which is comprehensive and easy to use. Our dataset consists of amino acid sequences, Q8 secondary structures, position specific scoring matrices, multiple sequence alignment co-evolutionary features, backbone atom distance matrices, torsion angles, and 3D coordinates. We evaluate the quality of our structure prediction by RMSD on the latest Critical Assessment of Techniques for Protein Structure Prediction (CASP) test data and demonstrate competitive results with the winning teams and AlphaFold in CASP13 and supersede the results of the winning teams in CASP12. We make our data, models, and code publicly available.", "authors": ["Iddo Drori", "Darshan Thaker", "Arjun Srivatsa", "Daniel Jeong", "Yueqi Wang", "Linyong Nan", "Fan Wu", "Dimitri Leggas", "Jinhao Lei", "Weiyi Lu", "Weilong Fu", "Yuan Gao", "Sashank Karri", "Anand Kannan", "Antonio Moretti", "Mohammed AlQuraishi", "Chen Keasar", "Itsik Pe'er"], "categories": ["q-bio.BM", "cs.LG", "stat.ML"], "fields_of_study": ["Biology", "Computer Science", "Mathematics"], "published_date": "2019-11-09", "url": "https://arxiv.org/abs/1911.05531", "pdf_url": "https://arxiv.org/pdf/1911.05531v1", "arxiv_id": "1911.05531", "doi": null, "citation_count": 12, "influential_citation_count": 2, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.2785}
{"id": "dba139ff91c1a01d9235851f456cc6ad5eeda58331bf72a33e469e46ccd4d8c1", "sources": ["arxiv", "semantic_scholar"], "title": "Minimum Data Requirements and Supplemental Angle Constraints for Protein Structure Prediction with REDCRAFT", "abstract": "One algorithm to predict protein structure is the residual dipolar coupling based residue assembly and filter tool (REDCRAFT). This algorithm exploits an exponential reduction of the search space of all possible structures to find a structure that best fits a set of experimental residual dipolar couplings. However, the minimum amount of data required to successfully determine a protein's structure using REDCRAFT has not been previously investigated. Here we explore the effect of reducing the amount of data used to fold proteins. Our goal is to reduce experimental data collection times while retaining the accuracy levels previously achieved with larger amounts of data. We also investigate incorporating a priori secondary structure information into REDCRAFT to improve its structure prediction ability.", "authors": ["E. Timko", "P. Shealy", "M. Bryson", "H. Valafar"], "categories": ["q-bio.BM"], "fields_of_study": ["Computer Science", "Physics", "Biology"], "published_date": "2019-10-31", "url": "https://arxiv.org/abs/1910.14469", "pdf_url": "https://arxiv.org/pdf/1910.14469v2", "arxiv_id": "1910.14469", "doi": null, "citation_count": 7, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "International Conference on Bioinformatics & Computational Biology", "quality_score": 0.2258}
{"id": "5ea2ce8a2d4d9c928835649505fb7a4439a6b30fb9d976366316391049a0bd6a", "sources": ["arxiv", "semantic_scholar"], "title": "A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction", "abstract": "The ABCD Neurocognitive Prediction Challenge is a community driven competition asking competitors to develop algorithms to predict fluid intelligence score from T1-w MRIs. In this work, we propose a deep learning combined with gradient boosting machine framework to solve this task. We train a convolutional neural network to compress the high dimensional MRI data and learn meaningful image features by predicting the 123 continuous-valued derived data provided with each MRI. These extracted features are then used to train a gradient boosting machine that predicts the residualized fluid intelligence score. Our approach achieved mean square error (MSE) scores of 18.4374, 68.7868, and 96.1806 for the training, validation, and test set respectively.", "authors": ["Yeeleng S. Vang", "Yingxin Cao", "Xiaohui Xie"], "categories": ["cs.CV", "q-bio.NC"], "fields_of_study": ["Computer Science", "Biology"], "published_date": "2019-10-16", "url": "https://arxiv.org/abs/1910.07640", "pdf_url": "https://arxiv.org/pdf/1910.07640v1", "arxiv_id": "1910.07640", "doi": "10.1007/978-3-030-31901-4_1", "citation_count": 9, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "In: Pohl K., Thompson W., Adeli E., Linguraru M. (eds) Adolescent Brain Cognitive Development Neurocognitive Prediction. ABCD-NP 2019. Lecture Notes in Computer Science, vol 11791. Springer, Cham (2019)", "quality_score": 0.25}
{"id": "8bbef3f9f2df58d7bdf81f6b989d7701bd1c65bbc58b69ade15de25623b9d35a", "sources": ["arxiv", "semantic_scholar"], "title": "A Continuous Effective Model of the Protein Dynamics", "abstract": "The theory of elastic rods can be used to describe certain geometric and topological properties of the DNA molecules. A similar effective field theory approach was previously suggested to describe the conformations and dynamics of proteins. In this letter we report a detailed study of the basic features of a version of the proposed model, which assumes proteins to be very long continuous curves. In the most appealing case, the model is based on a potential with a pair of minima corresponding to helical and strand-like configurations of the curves. It allows to derive several predictions about the geometric features of the molecules, and we show that the predictions are compatible with the phenomenology. While the helices represent the ground state configurations, the abundance of beta strands is controlled by a parameter, which can either completely suppress their presence in a molecule, or make them abundant. The few-parameter model investigated in the letter rather represents a universality class of protein molecules. Generalizations accounting for the discrete nature and inhomogeneity of the molecules presumably allow to model realistic cases.", "authors": ["Dmitry Melnikov", "Alyson B. F. Neves"], "categories": ["q-bio.BM", "cond-mat.soft", "hep-ph", "hep-th", "physics.bio-ph"], "fields_of_study": ["Biology", "Physics", "Chemistry"], "published_date": "2019-08-30", "url": "https://arxiv.org/abs/1908.11739", "pdf_url": "https://arxiv.org/pdf/1908.11739v1", "arxiv_id": "1908.11739", "doi": null, "citation_count": 1, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0753}
{"id": "6c5153341454f6a399116f37ae62b3bb30b6b70a0105135395cdd3fe6e4cb31b", "sources": ["arxiv", "semantic_scholar"], "title": "In silico prediction of protein flexibility with local structure approach", "abstract": "Flexibility is an intrinsic essential feature of protein structures, directly linked to their functions. To this day, most of the prediction methods use the crystallographic data (namely B-factors) as the only indicator of protein's inner flexibility and predicts them as rigid or flexible.PredyFlexy stands differently from other approaches as it relies on the definition of protein flexibility (i) not only taken from crystallographic data, but also (ii) from Root Mean Square Fluctuation (RMSFs) observed in Molecular Dynamics simulations. It also uses a specific representation of protein structures, named Long Structural Prototypes (LSPs). From Position-Specific Scoring Matrix, the 120 LSPs are predicted with a good accuracy and directly used to predict (i) the protein flexibility in three categories (flexible, intermediate and rigid), (ii) the normalized B-factors, (iii) the normalized RMSFs, and (iv) a confidence index. Prediction accuracy among these three classes is equivalent to the best two class prediction methods, while the normalized B-factors and normalized RMSFs have a good correlation with experimental and in silico values. Thus, PredyFlexy is a unique approach, which is of major utility for the scientific community. It support parallelization features and can be run on a local cluster using multiple cores.The entire project is available under an open-source license at http://www.dsimb.inserm.fr/~debrevern/TOOLS/predyflexy_1.3/index.php.", "authors": ["Tarun Narwani", "Catherine Etchebest", "Pierrick Craveur", "Sylvain Léonard", "Joseph Rebehmed", "Narayanaswamy Srinivasan", "Aurélie Bornot", "Jean-Christophe Gelly", "Alexandre de Brevern"], "categories": ["q-bio.QM", "q-bio.BM"], "fields_of_study": ["Medicine", "Mathematics", "Biology"], "published_date": "2019-08-14", "url": "https://arxiv.org/abs/1908.05120", "pdf_url": "https://arxiv.org/pdf/1908.05120v1", "arxiv_id": "1908.05120", "doi": "10.1016/j.biochi.2019.07.025", "citation_count": 34, "influential_citation_count": 0, "has_code": true, "code_url": null, "venue": "Biochimie", "quality_score": 0.386}
{"id": "1cbe67b7a09b0c3bc437279e6d171b10184dd10435901c908e91933987004311", "sources": ["arxiv", "semantic_scholar"], "title": "Analyses of protein cores reveal fundamental differences between solution and crystal structures", "abstract": "There have been several studies suggesting that protein structures solved by NMR spectroscopy and x-ray crystallography show significant differences. To understand the origin of these differences, we assembled a database of high-quality protein structures solved by both methods. We also find significant differences between NMR and crystal structures---in the root-mean-square deviations of the C$_α$ atomic positions, identities of core amino acids, backbone and sidechain dihedral angles, and packing fraction of core residues. In contrast to prior studies, we identify the physical basis for these differences by modelling protein cores as jammed packings of amino-acid-shaped particles. We find that we can tune the jammed packing fraction by varying the degree of thermalization used to generate the packings. For an athermal protocol, we find that the average jammed packing fraction is identical to that observed in the cores of protein structures solved by x-ray crystallography. In contrast, highly thermalized packing-generation protocols yield jammed packing fractions that are even higher than those observed in NMR structures. These results indicate that thermalized systems can pack more densely than athermal systems, which suggests a physical basis for the structural differences between protein structures solved by NMR and x-ray crystallography.", "authors": ["Zhe Mei", "John D. Treado", "Alex T. Grigas", "Zachary A. Levine", "Lynne Regan", "Corey S. O'Hern"], "categories": ["q-bio.BM", "physics.bio-ph"], "fields_of_study": ["Medicine", "Biology", "Physics", "Materials Science"], "published_date": "2019-07-18", "url": "https://arxiv.org/abs/1907.08233", "pdf_url": "https://arxiv.org/pdf/1907.08233v1", "arxiv_id": "1907.08233", "doi": "10.1002/prot.25884", "citation_count": 22, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "Proteins: Structure, Function, and Bioinformatics", "quality_score": 0.3404}
{"id": "f5c136bea9c0d82ad7a4c01bdfef1d11812d1350c14ee99d6150ce403dc96f72", "sources": ["arxiv", "semantic_scholar"], "title": "AmoebaContact and GDFold: a new pipeline for rapid prediction of protein structures", "abstract": "Native contacts between residues could be predicted from the amino acid sequence of proteins, and the predicted contact information could assist the de novo protein structure prediction. Here, we present a novel pipeline of a residue contact predictor AmoebaContact and a contact-assisted folder GDFold for rapid protein structure prediction. Unlike mainstream contact predictors that utilize human-designed neural networks, AmoebaContact adopts a set of network architectures that are found as optimal for contact prediction through automatic searching and predicts the residue contacts at a series of cutoffs. Different from conventional contact-assisted folders that only use top-scored contact pairs, GDFold considers all residue pairs from the prediction results of AmoebaContact in a differentiable loss function and optimizes the atom coordinates using the gradient descent algorithm. Combination of AmoebaContact and GDFold allows quick reconstruction of the protein structure, with comparable model quality to the state-of-the-art protein structure prediction methods.", "authors": ["Wenzhi Mao", "Wenze Ding", "Haipeng Gong"], "categories": ["q-bio.BM"], "fields_of_study": ["Biology", "Computer Science"], "published_date": "2019-05-28", "url": "https://arxiv.org/abs/1905.11640", "pdf_url": "https://arxiv.org/pdf/1905.11640v1", "arxiv_id": "1905.11640", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}
{"id": "e457d395bd990dc398b3aa8b2700554e0388a313ec49855a11f65a19bd6565fd", "sources": ["arxiv", "semantic_scholar"], "title": "Formal models of Structure Building in Music, Language and Animal Songs", "abstract": "Human language, music and a variety of animal vocalisations constitute ways of sonic communication that exhibit remarkable structural complexity. While the complexities of language and possible parallels in animal communication have been discussed intensively, reflections on the complexity of music and animal song, and their comparisons are underrepresented. In some ways, music and animal songs are more comparable to each other than to language, as propositional semantics cannot be used as as indicator of communicative success or well-formedness, and notions of grammaticality are less easily defined. This review brings together accounts of the principles of structure building in language, music and animal song, relating them to the corresponding models in formal language theory, with a special focus on evaluating the benefits of using the Chomsky hierarchy (CH). We further discuss common misunderstandings and shortcomings concerning the CH, as well as extensions or augmentations of it that address some of these issues, and suggest ways to move beyond.", "authors": ["Willem Zuidema", "Dieuwke Hupkes", "Geraint Wiggins", "Constance Scharff", "Martin Rohrmeier"], "categories": ["cs.CL"], "fields_of_study": ["Computer Science"], "published_date": "2019-01-16", "url": "https://arxiv.org/abs/1901.05180", "pdf_url": "https://arxiv.org/pdf/1901.05180v1", "arxiv_id": "1901.05180", "doi": null, "citation_count": 2, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": "arXiv.org", "quality_score": 0.1193}
{"id": "075d96b46a9190ebc6c151580403d68c97b390a987aeab760dc34638b7bfece5", "sources": ["arxiv", "semantic_scholar"], "title": "Estimation of Distribution Algorithm for Protein Structure Prediction", "abstract": "Proteins are essential for maintaining life. For example, knowing the structure of a protein, cell regulatory mechanisms of organisms can be modeled, supporting the development of disease treatments or the understanding of relationships between protein structures and food attributes. However, discovering the structure of a protein can be a difficult and expensive task, since it is hard to explore the large search to predict even a small protein. Template-based methods (coarse-grained, homology, threading etc) depend on Prior Knowledge (PK) of proteins determined using other methods as X-Ray Crystallography or Nuclear Magnetic Resonance. On the other hand, template-free methods (full-atom and ab initio) rely on atoms physical-chemical properties to predict protein structures. In comparison with other approaches, the Estimation of Distribution Algorithms (EDAs) can require significant less PK, suggesting that it could be adequate for proteins of low-level of PK. Finding an EDA able to handle both prediction quality and computational time is a difficult task, since they are strong inversely correlated. We developed an EDA specific for the ab initio Protein Structure Prediction (PSP) problem using full-atom representation. We developed one univariate and two bivariate probabilistic models in order to design a proper EDA for PSP. The bivariate models make relationships between dihedral angles $φ$ and $ψ$ within an amino acid. Furthermore, we compared the proposed EDA with other approaches from the literature. We noticed that even a relatively simple algorithm such as Random Walk can find the correct solution, but it would require a large amount of prior knowledge (biased prediction). On the other hand, our EDA was able to correctly predict with no prior knowledge at all, characterizing such a prediction as pure ab initio.", "authors": ["Daniel Bonetti", "Alexandre Delbem", "Dorival Leão", "Jochen Einbeck"], "categories": ["q-bio.BM"], "fields_of_study": ["Chemistry", "Biology"], "published_date": "2019-01-04", "url": "https://arxiv.org/abs/1901.01059", "pdf_url": "https://arxiv.org/pdf/1901.01059v1", "arxiv_id": "1901.01059", "doi": null, "citation_count": 0, "influential_citation_count": 0, "has_code": false, "code_url": null, "venue": null, "quality_score": 0.0}