Deep contrastive learning enables genome-wide virtual screening
- Yinjun Jia
- Bowen Gao
- Jiaxin Tan
- Jiqing Zheng
- Xin Hong
- Wenyu Zhu
- Haichuan Tan
- Yuan Xiao
- Liping Tan
- Hongyi Cai
- Yanwen Huang
- Zhiheng Deng
- Xiangwei Wu
- Yue Jin
- Yafei Yuan
- Jiekang Tian
- Wei He
- Weiying Ma
- Yaqin Zhang
- Lei Liu
- Chuangye Yan
- Wei Zhang
- Yanyan Lan
2026-01-08
Recent breakthroughs in protein structure prediction have opened new avenues for genome-wide drug discovery, yet existing virtual screening methods remain computationally prohibitive. We present DrugCLIP, a contrastive learning framework that achieves ultrafast and accurate virtual screening, up to 10 million times faster than docking, while consistently outperforming various baselines on in silico benchmarks. In wet-lab validations, DrugCLIP achieved a 15% hit rate for norepinephrine transporter, and structures of two identified inhibitors were determined in complex with the target protein. For thyroid hormone receptor interactor 12, a target that lacks holo structures and small-molecule binders, DrugCLIP achieved a 17.5% hit rate using only AlphaFold2-predicted structures. Finally, we released GenomeScreenDB, an open-access database providing precomputed results for ~10,000 human proteins screened against 500 million compounds, pioneering a drug discovery paradigm in the post-AlphaFold era.