Beyond the Score: Fixed-Budget Benchmarking of Virtual Screening Integration Strategies for Decision-Centric Drug Discovery
TL;DR
Target-level results showed substantial variability in MCS-containing workflows and limited benefits from adding docking without target-specific optimization, and a validated ligand-based predictor or a simple two-method rank-fusion scheme provided the highest observed mean hit recovery without requiring elaborate integration.
Abstract
Virtual screening (VS) workflows often combine structure- and ligand-based methods; however, their value depends on the number of compounds that can be tested. We benchmarked 20 fixed-budget strategies derived from molecular docking (GNINA CNN score), maximum common substructure (MCS) similarity, and a calibrated machine-learning (ML)-QSAR classifier across five pharmacologically diverse targets. Individual methods, best-rank and worst-rank fusion, mean-rank consensus, and sequential funnels were evaluated at 1%, 5%, and 10% library fractions, with every strategy selecting the same number of compounds. ML-QSAR was the strongest standalone method, recovering 47.6%, 81.6%, and 84.4% of actives at the three cutoffs. At the 1% budget, ML-QSAR achieved the highest mean hit recovery (47.6% recall; 99.2% precision). At 5% and 10%, best-rank fusion of QSAR and MCS produced the highest mean recall (83.2% and 86.4%). Among the sequential workflows, QSAR → MCS achieved the highest 1% hit recovery (45.2 ± 3.3% recall), whereas docking-first funnels consistently underperformed under the default, non-optimized conditions evaluated in this study. Target-level results showed substantial variability in MCS-containing workflows and limited benefits from adding docking without target-specific optimization. Under matched assay budgets, a validated ligand-based predictor or a simple two-method rank-fusion scheme provided the highest observed mean hit recovery without requiring elaborate integration.