posted on 2024-01-11, 17:06authored byIan S. Knight, Olivier Mailhot, Khanh G. Tang, John J. Irwin
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.