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DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction
Cytochrome P450 (CYP) is a superfamily of heme-containing oxidizing enzymes involved in the metabolism of a wide range of medicines, xenobiotics, and endogenous compounds. Five of the CYPs (1A2, 2C9, 2C19, 2D6, and 3A4) are responsible for metabolizing the vast majority of approved drugs. Adverse dr...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10123292/ https://www.ncbi.nlm.nih.gov/pubmed/37101544 http://dx.doi.org/10.3389/fphar.2023.1099093 |
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author | Ai, Daiqiao Cai, Hanxuan Wei, Jiajia Zhao, Duancheng Chen, Yihao Wang, Ling |
author_facet | Ai, Daiqiao Cai, Hanxuan Wei, Jiajia Zhao, Duancheng Chen, Yihao Wang, Ling |
author_sort | Ai, Daiqiao |
collection | PubMed |
description | Cytochrome P450 (CYP) is a superfamily of heme-containing oxidizing enzymes involved in the metabolism of a wide range of medicines, xenobiotics, and endogenous compounds. Five of the CYPs (1A2, 2C9, 2C19, 2D6, and 3A4) are responsible for metabolizing the vast majority of approved drugs. Adverse drug-drug interactions, many of which are mediated by CYPs, are one of the important causes for the premature termination of drug development and drug withdrawal from the market. In this work, we reported in silicon classification models to predict the inhibitory activity of molecules against these five CYP isoforms using our recently developed FP-GNN deep learning method. The evaluation results showed that, to the best of our knowledge, the multi-task FP-GNN model achieved the best predictive performance with the highest average AUC (0.905), F1 (0.779), BA (0.819), and MCC (0.647) values for the test sets, even compared to advanced machine learning, deep learning, and existing models. Y-scrambling testing confirmed that the results of the multi-task FP-GNN model were not attributed to chance correlation. Furthermore, the interpretability of the multi-task FP-GNN model enables the discovery of critical structural fragments associated with CYPs inhibition. Finally, an online webserver called DEEPCYPs and its local version software were created based on the optimal multi-task FP-GNN model to detect whether compounds bear potential inhibitory activity against CYPs, thereby promoting the prediction of drug-drug interactions in clinical practice and could be used to rule out inappropriate compounds in the early stages of drug discovery and/or identify new CYPs inhibitors. |
format | Online Article Text |
id | pubmed-10123292 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101232922023-04-25 DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction Ai, Daiqiao Cai, Hanxuan Wei, Jiajia Zhao, Duancheng Chen, Yihao Wang, Ling Front Pharmacol Pharmacology Cytochrome P450 (CYP) is a superfamily of heme-containing oxidizing enzymes involved in the metabolism of a wide range of medicines, xenobiotics, and endogenous compounds. Five of the CYPs (1A2, 2C9, 2C19, 2D6, and 3A4) are responsible for metabolizing the vast majority of approved drugs. Adverse drug-drug interactions, many of which are mediated by CYPs, are one of the important causes for the premature termination of drug development and drug withdrawal from the market. In this work, we reported in silicon classification models to predict the inhibitory activity of molecules against these five CYP isoforms using our recently developed FP-GNN deep learning method. The evaluation results showed that, to the best of our knowledge, the multi-task FP-GNN model achieved the best predictive performance with the highest average AUC (0.905), F1 (0.779), BA (0.819), and MCC (0.647) values for the test sets, even compared to advanced machine learning, deep learning, and existing models. Y-scrambling testing confirmed that the results of the multi-task FP-GNN model were not attributed to chance correlation. Furthermore, the interpretability of the multi-task FP-GNN model enables the discovery of critical structural fragments associated with CYPs inhibition. Finally, an online webserver called DEEPCYPs and its local version software were created based on the optimal multi-task FP-GNN model to detect whether compounds bear potential inhibitory activity against CYPs, thereby promoting the prediction of drug-drug interactions in clinical practice and could be used to rule out inappropriate compounds in the early stages of drug discovery and/or identify new CYPs inhibitors. Frontiers Media S.A. 2023-04-10 /pmc/articles/PMC10123292/ /pubmed/37101544 http://dx.doi.org/10.3389/fphar.2023.1099093 Text en Copyright © 2023 Ai, Cai, Wei, Zhao, Chen and Wang. 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 | Pharmacology Ai, Daiqiao Cai, Hanxuan Wei, Jiajia Zhao, Duancheng Chen, Yihao Wang, Ling DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction |
title | DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction |
title_full | DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction |
title_fullStr | DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction |
title_full_unstemmed | DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction |
title_short | DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction |
title_sort | deepcyps: a deep learning platform for enhanced cytochrome p450 activity prediction |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10123292/ https://www.ncbi.nlm.nih.gov/pubmed/37101544 http://dx.doi.org/10.3389/fphar.2023.1099093 |
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