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 # Benchmark for SeqScreen
+
 The paper is under review.
 
+\[[Github Repo](https://github.com/pcdslab/SeqScreen)\] | \[[Model Collection](https://huggingface.co/collections/SaeedLab/seqscreen)\] | \[[Cite](#citation)\]
+
+## Abstract
+
+Virtual screening aims to identify candidate molecules that bind to a target protein, playing a central role in computational drug discovery. Sequence-based deep learning methods offer an applicable alternative to structure-based approaches, but typically process one protein-molecule pair at a time, limiting their scalability to large molecular libraries. Contrastive learning methods inspired by CLIP have shown promise for learning joint protein-molecule representations, but standard CLIP training was designed for symmetric tasks and does not account for the asymmetric and one-to-many nature of protein-molecule binding. In this paper, we introduce *SeqScreen*, a sequence-based virtual screening method built on a dual-encoder contrastive architecture. SeqScreen introduces a protein-centric batch construction strategy and an asymmetric multi-positive InfoNCE loss to cope with the protein-centric nature of virtual screening. We conduct a systematic evaluation across 8 protein language models and 3 molecular language model variants. The protein-centric batch construction consistently outperforms standard CLIP training across all evaluated encoders, while requiring approximately 32 times fewer training epochs and 7 times fewer forward passes during inference compared to pair-based methods. On the LIT-PCBA dataset, SeqScreen outperforms all sequence-based baselines, achieving a relative improvement of up to 39% in EF at 0.5 over the best competing method, while remaining competitive with traditional docking approaches without requiring 3D structural information.
+
+## Dataset Details
+
+### ChEMBL
+The dataset consists of 1,492 proteins, 394,190 molecules, and 583,960 positive pairs for training; 334 proteins, 101,586 molecules, and 147,676 positive pairs for validation; 326 proteins, 105,048 molecules, and 130,281 positive pairs for testing. The dataset was generated using ChEMBL 36, split by protein sequence similarity.
+
+### LIT-PCBA
+The dataset consists of 1,452 proteins, 375,120 molecules, and 548,294 positive pairs for training; 326 proteins, 97,213 molecules, and 143,088 positive pairs for validation; 15 proteins, 404,586 molecules, and 2,776,973 pairs (positives and negatives) for testing. The training and validation datasets were generated using a filtered version of the ChEMBL dataset. The test set is the [LIT-PCBA dataset](https://pubs.acs.org/doi/10.1021/acs.jcim.0c00155).
+
+## Dataset Usage
+
+### ChEMBL
+
+Use the code below to load the ChEMBL dataset. 
+```py
+from datasets import load_dataset 
+
+dataset_dict = load_dataset("SaeedLab/SeqScreen", data_dir="chembl")
+```
+
+### LIT-PCBA
+
+Use the code below to load the LIT-PCBA dataset. 
+```py
+from datasets import load_dataset 
+
+dataset_dict = load_dataset("SaeedLab/SeqScreen", data_dir="lit_pcba")
+```
+
 ## Citation
 
 The paper is under review. As soon as it is accepted, we will update this section.