Deep contrastive learning enables genome-wide virtual screening

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…

• NN
  National Natural Science Foundation of China

  Awards: 32341016, 32171204, 22137005, T2488301
• NK
  National Key Research and Development Program of China

  Awards: 2021ZD0200306, 2021YFF1201600, 2020YFA0509301, 2021ZD0203303