Virtual screening is receiving renewed attention in drug discovery, but progress is hampered by challenges on two fronts: handling the ever increasing sizes of libraries of drug-like compounds, and separating true positives from false positives. Here we developed a machine learning-enabled pipeline for large-scale virtual screening that promises breakthroughs on both fronts. By clustering compounds according to molecular properties and limited docking against a drug target, the full library was trimmed by 10-fold; the remaining compounds were then screened individually by docking; and finally a dense neural network was trained to classify the hits into true and false positives. As illustration, we screened for inhibitors against RPN11, the deubiquitinase subunit of the proteasome and a drug target for breast cancer.

中文翻译：

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用于大规模虚拟药物筛选的机器学习管道

虚拟筛选在药物发现中重新受到关注，但进展受到两个方面的挑战的阻碍：处理不断增加的类药物化合物库，以及区分真阳性和假阳性。在这里，我们为大规模虚拟筛选开发了一个支持机器学习的管道，有望在两个方面取得突破。通过根据分子特性对化合物进行聚类并限制与药物靶点的对接，整个库被修剪了 10 倍；然后通过对接单独筛选剩余的化合物；最后训练了一个密集的神经网络来将命中分类为真阳性和假阳性。作为说明，我们筛选了针对 RPN11 的抑制剂，RPN11 是蛋白酶体的去泛素化酶亚基，也是乳腺癌的药物靶点。