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• W3174446023 abstract "Abstract 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. TOC Graphic" @default.
• W3174446023 created "2021-07-05" @default.
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• W3174446023 date "2021-06-21" @default.
• W3174446023 modified "2023-09-23" @default.
• W3174446023 title "A Machine Learning-Enabled Pipeline for Large-Scale Virtual Drug Screening" @default.
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• W3174446023 doi "https://doi.org/10.1101/2021.06.20.449177" @default.
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