  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.
