Articolo in rivista, 2023, CPE, 10.1016/j.compbiomed.2023.107314
Delre, Pietro; Contino, Marialessandra; Alberga, Domenico; Saviano, Michele; Corriero, Nicola; Mangiatordi, Giuseppe Felice
Università degli studi di Bari Aldo Moro; Consiglio Nazionale delle Ricerche; CNR-Institute of Crystallography
The development of small molecules that selectively target the cannabinoid receptor subtype 2 (CB2R) is emerging as an intriguing therapeutic strategy to treat neurodegeneration, as well as to contrast the onset and progression of cancer. In this context, in-silico tools able to predict CB2R affinity and selectivity with respect to the subtype 1 (CB1R), whose modulation is responsible for undesired psychotropic effects, are highly desirable. In this work, we developed a series of machine learning classifiers trained on high-quality bioactivity data of small molecules acting on CB2R and/or CB1R extracted from ChEMBL v30. Our classifiers showed strong predictive power in accurately determining CB2R affinity, CB1R affinity, and CB2R/CB1R selectivity. Among the built models, those obtained using random forest as algorithm proved to be the top-performing ones (AUC in validation >=0.96) and were made freely accessible through a user-friendly web platform developed ad hoc and called ALPACA (https://www.ba.ic.cnr.it/softwareic/alpaca/). Due to its user-friendly interface and robust predictive power, ALPACA can be a valuable tool in saving both time and resources involved in the design of selective CB2R modulators.
Computers in biology and medicine 164
Cannabinoid receptors, Classifiers, Ligand-based models, Machine learning, Web-platform
Delre Pietro, Mangiatordi Giuseppe Felice, Corriero Nicola, Saviano Michele, Alberga Domenico
ID: 485987
Year: 2023
Type: Articolo in rivista
Creation: 2023-09-04 16:09:18.000
Last update: 2023-10-09 16:54:27.000
CNR institutes
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1016/j.compbiomed.2023.107314
URL: http://www.scopus.com/record/display.url?eid=2-s2.0-85167624743&origin=inward
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:485987
DOI: 10.1016/j.compbiomed.2023.107314
Scopus: 2-s2.0-85167624743