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

[Image: see text] 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), th...

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Autores principales: Kurkinen, Sami T., Lehtonen, Jukka V., Pentikäinen, Olli T., Postila, Pekka A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889583/
https://www.ncbi.nlm.nih.gov/pubmed/35133138
http://dx.doi.org/10.1021/acs.jcim.1c01145
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author Kurkinen, Sami T.
Lehtonen, Jukka V.
Pentikäinen, Olli T.
Postila, Pekka A.
author_facet Kurkinen, Sami T.
Lehtonen, Jukka V.
Pentikäinen, Olli T.
Postila, Pekka A.
author_sort Kurkinen, Sami T.
collection PubMed
description [Image: see text] 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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spelling pubmed-88895832022-03-02 Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening Kurkinen, Sami T. Lehtonen, Jukka V. Pentikäinen, Olli T. Postila, Pekka A. J Chem Inf Model [Image: see text] 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. American Chemical Society 2022-02-08 2022-02-28 /pmc/articles/PMC8889583/ /pubmed/35133138 http://dx.doi.org/10.1021/acs.jcim.1c01145 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Kurkinen, Sami T.
Lehtonen, Jukka V.
Pentikäinen, Olli T.
Postila, Pekka A.
Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening
title Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening
title_full Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening
title_fullStr Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening
title_full_unstemmed Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening
title_short Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening
title_sort optimization of cavity-based negative images to boost docking enrichment in virtual screening
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889583/
https://www.ncbi.nlm.nih.gov/pubmed/35133138
http://dx.doi.org/10.1021/acs.jcim.1c01145
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