One size does not fit all: revising traditional paradigms for QSAR-based virtual screenings.

Traditional best practices for Quantitative Structure Activity Relationship (QSAR) modeling recommend dataset balancing and balanced accuracy (BA) as the key desired objective of model development. This study challenges the conventional norms by recommending the use of models with the highest positive predictive value (PPV) built for imbalanced training sets as preferred tools in virtual screening campaigns for drug discovery. As proof of concept, we developed QSAR models for five expansive datasets with different ratios of active and inactive molecules and assessed model performance using BA, PPV, and other metrics. We demonstrated that PPV-oriented models used in virtual screening have at least 30% higher first-batch hit rate as compared to all other models. These findings suggest that QSAR models trained on imbalanced datasets for the highest PPV are preferred tools for virtual screening campaigns in drug discovery.