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Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods

The identification and optimization of promising lead molecules is essential for drug discovery. Recently, artificial intelligence (AI) based generative methods provided complementary approaches for generating molecules under specific design constraints of relevance in drug design. The goal of our s...

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Autores principales: Sauer, Susanne, Matter, Hans, Hessler, Gerhard, Grebner, Christoph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629386/
https://www.ncbi.nlm.nih.gov/pubmed/36339033
http://dx.doi.org/10.3389/fchem.2022.1012507
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author Sauer, Susanne
Matter, Hans
Hessler, Gerhard
Grebner, Christoph
author_facet Sauer, Susanne
Matter, Hans
Hessler, Gerhard
Grebner, Christoph
author_sort Sauer, Susanne
collection PubMed
description The identification and optimization of promising lead molecules is essential for drug discovery. Recently, artificial intelligence (AI) based generative methods provided complementary approaches for generating molecules under specific design constraints of relevance in drug design. The goal of our study is to incorporate protein 3D information directly into generative design by flexible docking plus an adapted protein-ligand scoring function, thereby moving towards automated structure-based design. First, the protein-ligand scoring function RFXscore integrating individual scoring terms, ligand descriptors, and combined terms was derived using the PDBbind database and internal data. Next, design results for different workflows are compared to solely ligand-based reward schemes. Our newly proposed, optimal workflow for structure-based generative design is shown to produce promising results, especially for those exploration scenarios, where diverse structures fitting to a protein binding site are requested. Best results are obtained using docking followed by RFXscore, while, depending on the exact application scenario, it was also found useful to combine this approach with other metrics that bias structure generation into “drug-like” chemical space, such as target-activity machine learning models, respectively.
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spelling pubmed-96293862022-11-03 Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods Sauer, Susanne Matter, Hans Hessler, Gerhard Grebner, Christoph Front Chem Chemistry The identification and optimization of promising lead molecules is essential for drug discovery. Recently, artificial intelligence (AI) based generative methods provided complementary approaches for generating molecules under specific design constraints of relevance in drug design. The goal of our study is to incorporate protein 3D information directly into generative design by flexible docking plus an adapted protein-ligand scoring function, thereby moving towards automated structure-based design. First, the protein-ligand scoring function RFXscore integrating individual scoring terms, ligand descriptors, and combined terms was derived using the PDBbind database and internal data. Next, design results for different workflows are compared to solely ligand-based reward schemes. Our newly proposed, optimal workflow for structure-based generative design is shown to produce promising results, especially for those exploration scenarios, where diverse structures fitting to a protein binding site are requested. Best results are obtained using docking followed by RFXscore, while, depending on the exact application scenario, it was also found useful to combine this approach with other metrics that bias structure generation into “drug-like” chemical space, such as target-activity machine learning models, respectively. Frontiers Media S.A. 2022-10-19 /pmc/articles/PMC9629386/ /pubmed/36339033 http://dx.doi.org/10.3389/fchem.2022.1012507 Text en Copyright © 2022 Sauer, Matter, Hessler and Grebner. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Chemistry
Sauer, Susanne
Matter, Hans
Hessler, Gerhard
Grebner, Christoph
Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods
title Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods
title_full Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods
title_fullStr Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods
title_full_unstemmed Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods
title_short Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods
title_sort optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative ai methods
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629386/
https://www.ncbi.nlm.nih.gov/pubmed/36339033
http://dx.doi.org/10.3389/fchem.2022.1012507
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