Machine learning classification can reduce false positives in structure-based virtual screening.

ABSTRACT: With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discovery's search for active chemical matter. In typical virtual screens, however, only about 12% of the top-scoring compounds actually show activity when tested in biochemical assays. We argue that most scoring functions used for this task have been developed with insufficient thoughtfulness into the datasets on which they are trained and tested, leading to overly simplistic models and/or overtraining. These problems are compounded in the literature because studies reporting new scoring methods have not validated their models prospectively within the same study. Here, we report a strategy for building a training dataset (D-COID) that ai

SUBMITTER: Adeshina YO 

PROVIDER: S-EPMC7414157 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature