Integrated multi-class classification and prediction of GPCR allosteric modulators by machine learning intelligence
Open accessAbstract
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.
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Recent artificial intelligence developments for multiclass classification using ML are well suited to combine different connectivity modalities and methods to test the power of multiclass classification.
“Recent artificial intelligence developments for multiclass classification (Churcher et al., 2021; Gao et al., 2021; Hou et al., 2021) using ML are well suited to combine different connectivity modalities and methods to test the power of multiclass classification.”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Hou et al., 2021
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- Authors
- Hou, T., Bian, Y., McGuire, T., & Xie, X.-Q.
- Affiliations
- University of Pittsburgh (US)
- Corresponding author
- Xie, Xiang-Qun
- Venue
- Biomolecules
- Publisher
- MDPI AG
- Published
- 2021-06-11
- Volume
- 11
- Issue
- 6
- Pages
- 870
- ISSN
- 2218-273X (ISSN-L)
- PMC
- PMC8230833
- OpenAlex
- W3172978511
- Cited by
- 30
- References
- 51
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 3 · 2023: 7 · 2022: 7 · 2012: 1
- FWCI
- 2.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Biomolecules (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Drug Abuse (P30 DA035778A1)
- DOD (W81XWH-16-1-0490)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- G protein-coupled receptors, allosteric modulators, multi-class classification, molecular descriptors, MACCS fingerprints, drug-likeness prediction, machine learning models
- MeSH terms
- Machine Learning, Algorithms, Allosteric Regulation, Artificial Intelligence, Bayes Theorem, Forecasting, Humans, Ligands, Models, Molecular, Databases, Factual, Receptors, G-Protein-Coupled, Support Vector Machine
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