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- W2022135260 abstract "High-throughput screening approaches, where hundreds of thousands of compounds are evaluated in microamounts for their activity against certain targets, can regularly result in hit rates that are only a fraction of a percent. Here, we take a previously developed machine-learning classification model (with the Signature molecular descriptor) used to identify active compounds against Factor XIa and experimentally verify the virtual screening model predictions. Of 21 predicted compounds tested, seven show activity against Factor XIa, a 33% hit rate. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2741–2746, 2014 Additional Supporting Information may be found in the online version of this article. Supplementary Information Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article." @default.
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- W2022135260 date "2014-06-02" @default.
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- W2022135260 title "Confirmation of predicted activity for factor XIa inhibitors from a virtual screening approach" @default.
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