DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening

• URL: http://arxiv.org/abs/2310.06367v1
• Date: Tue, 10 Oct 2023 07:08:35 GMT
• Title: DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening
• Authors: Bowen Gao, Bo Qiang, Haichuan Tan, Minsi Ren, Yinjun Jia, Minsi Lu, Jingjing Liu, Weiying Ma, Yanyan Lan
• Abstract summary: DrugCLIP is a novel contrastive learning framework for virtual screening. It can align representations of binding protein pockets and molecules from a large quantity of pairwise data without explicit binding-affinity scores. It significantly outperforms traditional docking and supervised learning methods on diverse virtual screening benchmarks.
• Score: 16.31607535765497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Virtual screening, which identifies potential drugs from vast compound databases to bind with a particular protein pocket, is a critical step in AI-assisted drug discovery. Traditional docking methods are highly time-consuming, and can only work with a restricted search library in real-life applications. Recent supervised learning approaches using scoring functions for binding-affinity prediction, although promising, have not yet surpassed docking methods due to their strong dependency on limited data with reliable binding-affinity labels. In this paper, we propose a novel contrastive learning framework, DrugCLIP, by reformulating virtual screening as a dense retrieval task and employing contrastive learning to align representations of binding protein pockets and molecules from a large quantity of pairwise data without explicit binding-affinity scores. We also introduce a biological-knowledge inspired data augmentation strategy to learn better protein-molecule representations. Extensive experiments show that DrugCLIP significantly outperforms traditional docking and supervised learning methods on diverse virtual screening benchmarks with highly reduced computation time, especially in zero-shot setting.

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