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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-9629386 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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