Prediction of Orthosteric and Allosteric Regulations on Cannabinoid Receptors Using Supervised Machine Learning Classifiers

• Published in: Molecular pharmaceutics Vol. 16; no. 6; pp. 2605 - 2615
• Main Authors: Bian, Yuemin, Jing, Yankang, Wang, Lirong, Ma, Shifan, Jun, Jaden Jungho, Xie, Xiang-Qun
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 03.06.2019
• Subjects: Algorithms; Allosteric Regulation; Animals; Humans; Machine Learning; Receptors, Cannabinoid - metabolism; Support Vector Machine; allosteric regulation; cannabinoid receptor; machine learning; drug design; deep neural network
• ISSN: 1543-8384; 1543-8392
• DOI: 10.1021/acs.molpharmaceut.9b00182

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, naïve 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.