Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy

TLDR

Molecular docking programs are widely used modeling tools for predicting ligand binding modes and structure‑based virtual screening. The study evaluates six molecular docking programs to assess their docking pose and virtual screening accuracy. The authors compared the programs using metrics designed to evaluate pose prediction and virtual screening performance. ICM, GLIDE, and Surflex produced more accurate ligand poses and superior virtual screening performance than the other programs, with performance varying by protein family and sensitive to parameter settings.

Abstract

Molecular docking programs are widely used modeling tools for predicting ligand binding modes and structure based virtual screening. In this study, six molecular docking programs (DOCK, FlexX, GLIDE, ICM, PhDOCK, and Surflex) were evaluated using metrics intended to assess docking pose and virtual screening accuracy. Cognate ligand docking to 68 diverse, high-resolution X-ray complexes revealed that ICM, GLIDE, and Surflex generated ligand poses close to the X-ray conformation more often than the other docking programs. GLIDE and Surflex also outperformed the other docking programs when used for virtual screening, based on mean ROC AUC and ROC enrichment values obtained for the 40 protein targets in the Directory of Useful Decoys (DUD). Further analysis uncovered general trends in accuracy that are specific for particular protein families. Modifying basic parameters in the software was shown to have a significant effect on docking and virtual screening results, suggesting that expert knowledge is critical for optimizing the accuracy of these methods.