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Modeling the Sensitivity of Large-Scale Virtual Screening to Scoring Function Accuracy, Artifacts, and Library

Laust Moesgaard¹, Brian K Shoichet², Olivier Mailhot³

¹Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense M 5230 Denmark.

Journal of Chemical Information and Modeling

|July 8, 2026

Summary

A new quantitative framework models large-scale docking experiments, revealing that improved scoring accuracy and physicochemical prefiltering significantly enhance ligand discovery success. This approach optimizes virtual screening for growing chemical libraries.

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Area of Science:

Computational chemistry
Drug discovery
Bioinformatics

Background:

Large library docking is vital for ligand discovery but lacks a quantitative framework to assess methodological improvements.
Previous docking campaigns synthesized and tested thousands of ligands, providing data across various scoring ranges.

Purpose of the Study:

Develop a quantitative framework to understand how docking performance changes with methodological improvements.
Provide a basis for benchmarking and comparing virtual screening approaches.

Main Methods:

Modeled large-scale docking experiments from three published campaigns using a bivariate normal distribution model.
Incorporated a term for high-ranking docking artifacts to account for observed hit-rate curves.
Analyzed the impact of scoring accuracy, docking artifacts, and physicochemical prefiltering on hit rates and affinities.

Main Results:

A simple bivariate normal distribution model accurately reproduced experimental hit-rate curves, interpreting docking score as a noisy predictor of binding free energy.
Even minor improvements in scoring accuracy (0.1 increase in correlation) could justify a tenfold increase in computational cost, highlighting the value of scoring function accuracy.
Docking artifacts can dominate top-scoring lists in large libraries, necessitating physical testing across various ranks (pProp) to find the peak hit rate.
Prefiltering libraries by physicochemical features significantly boosts docking performance, comparable to improved scoring accuracy, especially for massive (tera-scale) libraries.

Conclusions:

The developed framework offers a practical method for optimizing large-scale virtual screening.
Findings support reinvestment in scoring function accuracy and emphasize the benefits of physicochemical prefiltering.
The model's parameters can be adapted to benchmark and compare diverse virtual screening methods beyond docking.