ALPACA: A machine Learning Platform for Affinity and selectivity profiling of CAnnabinoids receptors modulators.
Comput Biol Med
; 164: 107314, 2023 09.
Article
em En
| MEDLINE
| ID: mdl-37572442
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Canabinoides
/
Camelídeos Americanos
/
Neoplasias
Tipo de estudo:
Prognostic_studies
Limite:
Animals
Idioma:
En
Revista:
Comput Biol Med
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Itália