CIRCE: Web-Based Platform for the Prediction of Cannabinoid Receptor Ligands Using Explainable Machine Learning
Nicola Gambacorta, Fulvio Ciriaco, Nicola Amoroso, Cosimo Altomare, Jürgen Bajorath, Orazio Nicolotti
Abstract
The endocannabinoid system, which includes cannabinoid receptor 1 and 2 subtypes (CB 1 R and CB 2 R, respectively), is responsible for the onset of various pathologies including neurodegeneration, cancer, neuropathic and inflammatory pain, obesity, and inflammatory bowel disease. Given the high similarity of CB 1 R and CB 2 R, generating subtype-selective ligands is still an open challenge. In this work, the Cannabinoid Iterative Revaluation for Classification and Explanation (CIRCE) compound prediction platform has been generated based on explainable machine learning to support the design of selective CB 1 R and CB 2 R ligands. Multilayer classifiers were combined with Shapley value analysis to facilitate explainable predictions. In test calculations, CIRCE predictions reached ∼80% accuracy and structural features determining ligand predictions were rationalized. CIRCE was designed as a web-based prediction platform that is made freely available as a part of our study.