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Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling

Deep machine learning is expanding the conceptual framework and capacity of computational compound design, enabling new applications through generative modeling. We have explored the systematic design of covalent protein kinase inhibitors by learning from kinome-relevant chemical space, followed by...

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Detalles Bibliográficos
Autores principales: Yoshimori, Atsushi, Miljković, Filip, Bajorath, Jürgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8778003/
https://www.ncbi.nlm.nih.gov/pubmed/35056884
http://dx.doi.org/10.3390/molecules27020570
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author Yoshimori, Atsushi
Miljković, Filip
Bajorath, Jürgen
author_facet Yoshimori, Atsushi
Miljković, Filip
Bajorath, Jürgen
author_sort Yoshimori, Atsushi
collection PubMed
description Deep machine learning is expanding the conceptual framework and capacity of computational compound design, enabling new applications through generative modeling. We have explored the systematic design of covalent protein kinase inhibitors by learning from kinome-relevant chemical space, followed by focusing on an exemplary kinase of interest. Covalent inhibitors experience a renaissance in drug discovery, especially for targeting protein kinases. However, computational design of this class of inhibitors has thus far only been little investigated. To this end, we have devised a computational approach combining fragment-based design and deep generative modeling augmented by three-dimensional pharmacophore screening. This approach is thought to be particularly relevant for medicinal chemistry applications because it combines knowledge-based elements with deep learning and is chemically intuitive. As an exemplary application, we report for Bruton’s tyrosine kinase (BTK), a major drug target for the treatment of inflammatory diseases and leukemia, the generation of novel candidate inhibitors with a specific chemically reactive group for covalent modification, requiring only little target-specific compound information to guide the design efforts. Newly generated compounds include known inhibitors and characteristic substructures and many novel candidates, thus lending credence to the computational approach, which is readily applicable to other targets.
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spelling pubmed-87780032022-01-22 Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling Yoshimori, Atsushi Miljković, Filip Bajorath, Jürgen Molecules Article Deep machine learning is expanding the conceptual framework and capacity of computational compound design, enabling new applications through generative modeling. We have explored the systematic design of covalent protein kinase inhibitors by learning from kinome-relevant chemical space, followed by focusing on an exemplary kinase of interest. Covalent inhibitors experience a renaissance in drug discovery, especially for targeting protein kinases. However, computational design of this class of inhibitors has thus far only been little investigated. To this end, we have devised a computational approach combining fragment-based design and deep generative modeling augmented by three-dimensional pharmacophore screening. This approach is thought to be particularly relevant for medicinal chemistry applications because it combines knowledge-based elements with deep learning and is chemically intuitive. As an exemplary application, we report for Bruton’s tyrosine kinase (BTK), a major drug target for the treatment of inflammatory diseases and leukemia, the generation of novel candidate inhibitors with a specific chemically reactive group for covalent modification, requiring only little target-specific compound information to guide the design efforts. Newly generated compounds include known inhibitors and characteristic substructures and many novel candidates, thus lending credence to the computational approach, which is readily applicable to other targets. MDPI 2022-01-17 /pmc/articles/PMC8778003/ /pubmed/35056884 http://dx.doi.org/10.3390/molecules27020570 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yoshimori, Atsushi
Miljković, Filip
Bajorath, Jürgen
Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling
title Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling
title_full Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling
title_fullStr Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling
title_full_unstemmed Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling
title_short Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling
title_sort approach for the design of covalent protein kinase inhibitors via focused deep generative modeling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8778003/
https://www.ncbi.nlm.nih.gov/pubmed/35056884
http://dx.doi.org/10.3390/molecules27020570
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