Prediction of Orthosteric and Allosteric Regulations on Cannabinoid Receptors Using Supervised Machine Learning Classifiers
| Autor: | Xiang-Qun Xie, Jaden Jungho Jun, Lirong Wang, Yuemin Bian, Shifan Ma, Yankang Jing |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Support Vector Machine
Computer science Pharmaceutical Science 02 engineering and technology Machine learning computer.software_genre 030226 pharmacology & pharmacy Article Machine Learning 03 medical and health sciences Naive Bayes classifier 0302 clinical medicine Allosteric Regulation Molecular descriptor Drug Discovery Animals Humans Receptors Cannabinoid Virtual screening Receiver operating characteristic Drug discovery Mechanism (biology) business.industry 021001 nanoscience & nanotechnology Ensemble learning Support vector machine ComputingMethodologies_PATTERNRECOGNITION Molecular Medicine Artificial intelligence 0210 nano-technology business computer Algorithms |
| Popis: | Designing highly selective compounds to protein subtypes and developing allosteric modulators targeting them are critical considerations to both drug discovery and mechanism studies for cannabinoid receptors. It is challenging but in demand to have classifiers to identify active ligands from inactive or random compounds and distinguish allosteric modulators from orthosteric ligands. In this study, supervised machine learning classifiers were built for two subtypes of cannabinoid receptors, CB1 and CB2. Three types of features, including molecular descriptors, MACCS fingerprints, and ECFP6 fingerprints, were calculated to evaluate the compound sets from diverse aspects. Deep neural networks, as well as conventional machine learning algorithms including support vector machine, naive Bayes, logistic regression, and ensemble learning, were applied. Their performances on the classification with different types of features were compared and discussed. According to the receiver operating characteristic curves and the calculated metrics, the advantages and drawbacks of each algorithm were investigated. The feature ranking was followed to help extract useful knowledge about critical molecular properties, substructural keys, and circular fingerprints. The extracted features will then facilitate the research on cannabinoid receptors by providing guidance on preferred properties for compound modification and novel scaffold design. Besides using conventional molecular docking studies for compound virtual screening, machine-learning-based decision-making models provide alternative options. This study can be of value to the application of machine learning in the area of drug discovery and compound development. |
| Databáze: | OpenAIRE |
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