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

Sami T. Kurkinen, Jukka V. Lehtonen, Olli T. Pentikäinen, Pekka A. Postila*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
53 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1100-1112
JournalJournal of Chemical Information and Modeling
Issue number4
Publication statusPublished - 28 Feb 2022
MoE publication typeA1 Journal article-refereed


  • Cavities,Ligands,Optimization,Software,Testing and assessment


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