DockOpt: A Tool for Automatic Optimization of Docking Models

12 September 2023, Version 3
This content is a preprint and has not undergone peer review at the time of posting.


Molecular docking is a widely used technique for leveraging protein structure in ligand discovery, but as a method, it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress towards automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we have 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 dataset, 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 and free for everyone upon registration at


molecular docking
virtual screening
ligand discovery

Supplementary materials

Supporting Information for DockOpt — A Tool for Automatic Optimization of Docking Models
S1. Documentation about Pydock3, how to install it, and how to use its tools, including DockOpt and Blastermaster. S2. Documentation about the DUDE-Z dataset toolkit, used to benchmark DockOpt against DUDE-Z. S3. Empirical distributions approximating the behavior of a truly random classifier in terms of normalized LogAUC.


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