Datasets:
Formats:
parquet
Languages:
English
Size:
10M - 100M
Tags:
biology
chemistry
drug-discovery
clinical-trials
protein-protein-interaction
gene-essentiality
License:
| # NegBioDB -- Roadmap | |
| > Last updated: 2026-03-30 | |
| ## Completed (Phase 1) | |
| ### DTI Domain (Drug-Target Interaction) | |
| - 30.5M negative results from 4 sources (ChEMBL, PubChem, BindingDB, DAVIS) | |
| - ML baselines: DeepDTA, GraphDTA, DrugBAN across 5 splits + 2 negative controls | |
| - LLM benchmark: L1-L4 tasks, 5 models, zero-shot and 3-shot configs | |
| - Key finding: degree-matched negatives inflate LogAUC by +0.112 | |
| ### CT Domain (Clinical Trial Failure) | |
| - 132,925 failure results from 216,987 trials (AACT, CTO, Open Targets, Shi & Du) | |
| - ML baselines: XGBoost, MLP, GNN across M1 (binary) and M2 (7-way) tasks | |
| - LLM benchmark: L1-L4, 5 models | |
| - Key finding: NegBioDB negatives trivially separable (AUROC=1.0); M2 macro-F1=0.51 | |
| ### PPI Domain (Protein-Protein Interaction) | |
| - 2.2M negative results from 4 sources (IntAct, HuRI, hu.MAP, STRING) | |
| - ML baselines: Siamese CNN, PIPR, MLPFeatures across 4 splits | |
| - LLM benchmark: L1-L4, 5 models | |
| - Key finding: PIPR AUROC drops to 0.41 on cold_both; temporal contamination detected | |
| ### GE Domain (Gene Essentiality / DepMap) | |
| - 28.8M negative results from DepMap CRISPR and RNAi screens | |
| - ML baselines: XGBoost, MLPFeatures (seed 42 complete) | |
| - LLM benchmark: 4/5 models complete | |
| - Key finding: cold-gene splits reveal severe generalization gaps | |
| ## In Progress | |
| - GE domain: remaining ML seeds (43/44) and Llama LLM runs | |
| - Paper preparation for NeurIPS 2026 Evaluations & Datasets Track | |
| ## Planned | |
| ### Phase 2: Community & Platform | |
| - Web interface for search, browse, and download | |
| - Python library: `pip install negbiodb` | |
| - REST API with tiered access | |
| - Community submission portal | |
| - Public leaderboard | |
| ### Phase 3: Scale | |
| - Expand curated entries via literature mining | |
| - Specialized bio-LLM evaluations | |
| - Cross-track ensemble evaluation (ML + LLM) | |