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Integrated Multi-Class Classification and Prediction of GPCR Allosteric Modulators by Machine Learning Intelligence
G-protein-coupled receptors (GPCRs) are the largest and most diverse group of cell surface receptors that respond to various extracellular signals. The allosteric modulation of GPCRs has emerged in recent years as a promising approach for developing target-selective therapies. Moreover, the discover...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8230833/ https://www.ncbi.nlm.nih.gov/pubmed/34208096 http://dx.doi.org/10.3390/biom11060870 |
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author | Hou, Tianling Bian, Yuemin McGuire, Terence Xie, Xiang-Qun |
author_facet | Hou, Tianling Bian, Yuemin McGuire, Terence Xie, Xiang-Qun |
author_sort | Hou, Tianling |
collection | PubMed |
description | G-protein-coupled receptors (GPCRs) are the largest and most diverse group of cell surface receptors that respond to various extracellular signals. The allosteric modulation of GPCRs has emerged in recent years as a promising approach for developing target-selective therapies. Moreover, the discovery of new GPCR allosteric modulators can greatly benefit the further understanding of GPCR cell signaling mechanisms. It is critical but also challenging to make an accurate distinction of modulators for different GPCR groups in an efficient and effective manner. In this study, we focus on an 11-class classification task with 10 GPCR subtype classes and a random compounds class. We used a dataset containing 34,434 compounds with allosteric modulators collected from classical GPCR families A, B, and C, as well as random drug-like compounds. Six types of machine learning models, including support vector machine, naïve Bayes, decision tree, random forest, logistic regression, and multilayer perceptron, were trained using different combinations of features including molecular descriptors, Atom-pair fingerprints, MACCS fingerprints, and ECFP6 fingerprints. The performances of trained machine learning models with different feature combinations were closely investigated and discussed. To the best of our knowledge, this is the first work on the multi-class classification of GPCR allosteric modulators. We believe that the classification models developed in this study can be used as simple and accurate tools for the discovery and development of GPCR allosteric modulators. |
format | Online Article Text |
id | pubmed-8230833 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82308332021-06-26 Integrated Multi-Class Classification and Prediction of GPCR Allosteric Modulators by Machine Learning Intelligence Hou, Tianling Bian, Yuemin McGuire, Terence Xie, Xiang-Qun Biomolecules Article G-protein-coupled receptors (GPCRs) are the largest and most diverse group of cell surface receptors that respond to various extracellular signals. The allosteric modulation of GPCRs has emerged in recent years as a promising approach for developing target-selective therapies. Moreover, the discovery of new GPCR allosteric modulators can greatly benefit the further understanding of GPCR cell signaling mechanisms. It is critical but also challenging to make an accurate distinction of modulators for different GPCR groups in an efficient and effective manner. In this study, we focus on an 11-class classification task with 10 GPCR subtype classes and a random compounds class. We used a dataset containing 34,434 compounds with allosteric modulators collected from classical GPCR families A, B, and C, as well as random drug-like compounds. Six types of machine learning models, including support vector machine, naïve Bayes, decision tree, random forest, logistic regression, and multilayer perceptron, were trained using different combinations of features including molecular descriptors, Atom-pair fingerprints, MACCS fingerprints, and ECFP6 fingerprints. The performances of trained machine learning models with different feature combinations were closely investigated and discussed. To the best of our knowledge, this is the first work on the multi-class classification of GPCR allosteric modulators. We believe that the classification models developed in this study can be used as simple and accurate tools for the discovery and development of GPCR allosteric modulators. MDPI 2021-06-11 /pmc/articles/PMC8230833/ /pubmed/34208096 http://dx.doi.org/10.3390/biom11060870 Text en © 2021 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 Hou, Tianling Bian, Yuemin McGuire, Terence Xie, Xiang-Qun Integrated Multi-Class Classification and Prediction of GPCR Allosteric Modulators by Machine Learning Intelligence |
title | Integrated Multi-Class Classification and Prediction of GPCR Allosteric Modulators by Machine Learning Intelligence |
title_full | Integrated Multi-Class Classification and Prediction of GPCR Allosteric Modulators by Machine Learning Intelligence |
title_fullStr | Integrated Multi-Class Classification and Prediction of GPCR Allosteric Modulators by Machine Learning Intelligence |
title_full_unstemmed | Integrated Multi-Class Classification and Prediction of GPCR Allosteric Modulators by Machine Learning Intelligence |
title_short | Integrated Multi-Class Classification and Prediction of GPCR Allosteric Modulators by Machine Learning Intelligence |
title_sort | integrated multi-class classification and prediction of gpcr allosteric modulators by machine learning intelligence |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8230833/ https://www.ncbi.nlm.nih.gov/pubmed/34208096 http://dx.doi.org/10.3390/biom11060870 |
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