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- W4387561029 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." @default.
- W4387561029 created "2023-10-12" @default.
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- W4387561029 date "2023-10-10" @default.
- W4387561029 modified "2023-10-13" @default.
- W4387561029 title "DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening" @default.
- W4387561029 doi "https://doi.org/10.48550/arxiv.2310.06367" @default.
- W4387561029 hasPublicationYear "2023" @default.
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