DockOpt: A Tool for Automatic Optimization of Docking Models

Molecular docking is a widely used technique for leveraging protein structure for ligand discovery, but it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress toward automation, docking still requires expert guidance, hindering its adoption...

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Bibliographic Details
Published inJournal of chemical information and modeling Vol. 64; no. 3; pp. 1004 - 1016
Main Authors Knight, Ian S., Mailhot, Olivier, Tang, Khanh G., Irwin, John J.
Format Journal Article
LanguageEnglish
Published United States American Chemical Society 12.02.2024
Subjects
Automation
Benchmarking
Ligands
Molecular docking
Molecular Docking Simulation
Optimization
Pharmaceutical Modeling
Prospective Studies
Protein Binding
Proteins - chemistry
Screens
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Summary:Molecular docking is a widely used technique for leveraging protein structure for ligand discovery, but it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress toward automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we developed a new utility called DockOpt, which automates the creation, evaluation, and optimization of docking models prior to their deployment in large-scale prospective screens. DockOpt outperforms our previous automated pipeline across all 43 targets in the DUDE-Z benchmark data set, and the generated models for 84% of targets demonstrate sufficient enrichment to warrant their use in prospective screens, with normalized LogAUC values of at least 15%. DockOpt is available as part of the Python package Pydock3 included in the UCSF DOCK 3.8 distribution, which is available for free to academic researchers at https://dock.compbio.ucsf.edu and free for everyone upon registration at https://tldr.docking.org.
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ISSN:1549-9596
1549-960X
1549-960X
DOI:10.1021/acs.jcim.3c01406