Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening

Molecular docking is a key in silico method used routinely in modern drug discovery projects. Although docking provides high-quality ligand binding predictions, it regularly fails to separate the active compounds from the inactive ones. In negative image-based rescoring (R-NiB), the shape/electrosta...

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Bibliographic Details
Published inJournal of chemical information and modeling Vol. 62; no. 4; pp. 1100 - 1112
Main Authors Kurkinen, Sami T, Lehtonen, Jukka V, Pentikäinen, Olli T, Postila, Pekka A
Format Journal Article
LanguageEnglish
Published United States American Chemical Society 28.02.2022
Subjects
Binding
Binding Sites
Editing
Iterative methods
Ligands
Molecular docking
Molecular Docking Simulation
Optimization
Pharmaceutical Modeling
Protein Binding
Screening
Software
Static Electricity
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Summary:Molecular docking is a key in silico method used routinely in modern drug discovery projects. Although docking provides high-quality ligand binding predictions, it regularly fails to separate the active compounds from the inactive ones. In negative image-based rescoring (R-NiB), the shape/electrostatic potential (ESP) of docking poses is compared to the negative image of the protein’s ligand binding cavity. While R-NiB often improves the docking yield considerably, the cavity-based models do not reach their full potential without expert editing. Accordingly, a greedy search-driven methodology, brute force negative image-based optimization (BR-NiB), is presented for optimizing the models via iterative editing and benchmarking. Thorough and unbiased training, testing and stringent validation with a multitude of drug targets, and alternative docking software show that BR-NiB ensures excellent docking efficacy. BR-NiB can be considered as a new type of shape-focused pharmacophore modeling, where the optimized models contain only the most vital cavity information needed for effectively filtering docked actives from the inactive or decoy compounds. Finally, the BR-NiB code for performing the automated optimization is provided free-of-charge under MIT license via GitHub (https://github.com/jvlehtonen/brutenib) for boosting the success rates of docking-based virtual screening campaigns.
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ISSN:1549-9596
1549-960X
DOI:10.1021/acs.jcim.1c01145