Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening.

ABSTRACT: In structure-based virtual screening, compound ranking through a consensus of scores from a variety of docking programs or scoring functions, rather than ranking by scores from a single program, provides better predictive performance and reduces target performance variability. Here we compare traditional consensus scoring methods with a novel, unsupervised gradient boosting approach. We also observed increased score variation among active ligands and developed a statistical mixture model consensus score based on combining score means and variances. To evaluate performance, we used the common performance metrics ROCAUC and EF1 on 21 benchmark targets from DUD-E. Traditional consensus methods, such as taking the mean of quantile normalized docking scores, outperformed individual docking meth

SUBMITTER: Ericksen SS 

PROVIDER: S-EPMC5872818 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature