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.

Keywords

Virtual screeningDocking (animal)DOCKComputer scienceProtein–ligand dockingArtificial intelligenceComputational biologyMachine learningChemistryDrug discoveryBiologyMedicineBiochemistry

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Publication Info

Year
2009
Type
article
Volume
49
Issue
6
Pages
1455-1474
Citations
444
Access
Closed

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Cite This

Jason B. Cross, David C. Thompson, K. Brajesh et al. (2009). Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy. Journal of Chemical Information and Modeling , 49 (6) , 1455-1474. https://doi.org/10.1021/ci900056c

Identifiers

DOI
10.1021/ci900056c