DrugCLIP: Contrastive Protein-Molecule Representation Learning for
  Virtual Screening
Bowen Gao Bo Qiang Haichuan Tan Minsi Ren Yinjun Jia Minsi Lu Jingjing Liu Weiying Ma Yanyan Lan
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.