Machine learning vs. field 3D-QSAR models for serotonin 2A receptor psychoactive substances identification.

ABSTRACT: Serotonergic psychedelics, substances exerting their effects primarily through the serotonin 2A receptor (5HT2AR), continue to comprise a substantial portion of reported new psychoactive substances (NPS). In this paper five quantitative structure-activity relationship (QSAR) models for predicting the affinity of 5-HT2AR ligands have been developed. The resulting models, exploiting the accessibility of the QSAR equations, generate a useful tool for the investigation and identification of unclassified molecules. The models have been built using a set of 375 molecules using Forge software, and the quality was confirmed by statistical analysis, resulting in effective tools with respect to their predictive and descriptive capabilities. The best performing algorithm among the machine learning ap

SUBMITTER: Floresta G 

PROVIDER: S-EPMC8697832 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature