Modeling the Sensitivity of Large-Scale Virtual Screening to Scoring Function Accuracy, Artifacts, and Library Composition
Large library docking has emerged as a productive approach for ligand discovery, yet a quantitative framework for understanding how docking performance responds to methodological improvements has been lacking. Here, we develop such a framework by modeling large-scale experiments from three previously published docking campaigns, in which 2,682 ligands had been synthesized and tested across the scoring landscape (poor scores, mediocre scores, high scores). The observed experimental hit-rate curves can be reproduced by a simple bivariate normal distribution model, where docking score is interpreted as a noisy predictor of binding free energy. To account for the plateauing and subsequent drop in hit rates often seen at highly favorable docking scores, we add a term for high-ranking docking artifacts, a phenomenon we observe across targets. From this model, three predictions about the sensitivity of docking performance emerge. First, even slight improvements in scoring accuracy would substantially improve both hit rates and hit affinities: quantitatively, a 0.1 increase in the correlation between docking score and binding affinity would justify accepting a ∼10-fold increase in computational cost per molecule, arguing for reinvestment in scoring function accuracy in library docking. Second, docking artifacts, while hard to anticipate, can come to dominate top-scoring lists as libraries grow. Physically testing molecules across a range of log-normalized ranks (pProp) is therefore essential to identify the peak hit rate for a given campaign. Third, prefiltering a library to enrich for molecules with appropriate physicochemical features increases the intrinsic hit rate and substantially boosts docking performance, particularly at tera-scale, with effects comparable to a meaningful improvement in scoring accuracy. Beyond docking, the model’s parameters (affinity distribution, score-affinity correlation, artifact frequency) can be fit to any screening method with sufficient experimental data, providing an objective basis for benchmarking and comparing virtual screening approaches. These findings offer a practical framework for optimizing large-scale virtual screening as chemical libraries continue to grow.