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Systemic evolutionary chemical space exploration for drug discovery
Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8973791/ https://www.ncbi.nlm.nih.gov/pubmed/35365231 http://dx.doi.org/10.1186/s13321-022-00598-4 |
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author | Lu, Chong Liu, Shien Shi, Weihua Yu, Jun Zhou, Zhou Zhang, Xiaoxiao Lu, Xiaoli Cai, Faji Xia, Ning Wang, Yikai |
author_facet | Lu, Chong Liu, Shien Shi, Weihua Yu, Jun Zhou, Zhou Zhang, Xiaoxiao Lu, Xiaoli Cai, Faji Xia, Ning Wang, Yikai |
author_sort | Lu, Chong |
collection | PubMed |
description | Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a “lego-building” process within the pocket of a certain target. The key to virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against phosphoglycerate dehydrogenase (PHGDH), proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http://github.com/KeenThera/SECSE. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00598-4. |
format | Online Article Text |
id | pubmed-8973791 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-89737912022-04-02 Systemic evolutionary chemical space exploration for drug discovery Lu, Chong Liu, Shien Shi, Weihua Yu, Jun Zhou, Zhou Zhang, Xiaoxiao Lu, Xiaoli Cai, Faji Xia, Ning Wang, Yikai J Cheminform Software Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a “lego-building” process within the pocket of a certain target. The key to virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against phosphoglycerate dehydrogenase (PHGDH), proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http://github.com/KeenThera/SECSE. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00598-4. Springer International Publishing 2022-04-01 /pmc/articles/PMC8973791/ /pubmed/35365231 http://dx.doi.org/10.1186/s13321-022-00598-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Lu, Chong Liu, Shien Shi, Weihua Yu, Jun Zhou, Zhou Zhang, Xiaoxiao Lu, Xiaoli Cai, Faji Xia, Ning Wang, Yikai Systemic evolutionary chemical space exploration for drug discovery |
title | Systemic evolutionary chemical space exploration for drug discovery |
title_full | Systemic evolutionary chemical space exploration for drug discovery |
title_fullStr | Systemic evolutionary chemical space exploration for drug discovery |
title_full_unstemmed | Systemic evolutionary chemical space exploration for drug discovery |
title_short | Systemic evolutionary chemical space exploration for drug discovery |
title_sort | systemic evolutionary chemical space exploration for drug discovery |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8973791/ https://www.ncbi.nlm.nih.gov/pubmed/35365231 http://dx.doi.org/10.1186/s13321-022-00598-4 |
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