Quantitative Structure-Activity Relationships for Structurally Diverse Chemotypes Having Anti- Activity.
Journal:
International journal of molecular sciences
PMID:
31181717
Abstract
Small-molecule compounds that have promising activity against macromolecular targets from occasionally fail when tested in whole-cell phenotypic assays. This outcome can be attributed to many factors, including inadequate physicochemical and pharmacokinetic properties. Unsuitable physicochemical profiles usually result in molecules with a poor ability to cross cell membranes. Quantitative structure-activity relationship (QSAR) analysis is a valuable approach to the investigation of how physicochemical characteristics affect biological activity. In this study, artificial neural networks (ANNs) and kernel-based partial least squares regression (KPLS) were developed using anti- activity data for broadly diverse chemotypes. The models exhibited a good predictive ability for the test set compounds, yielding values of 0.81 and 0.84 for the ANN and KPLS models, respectively. The results of this investigation highlighted privileged molecular scaffolds and the optimum physicochemical space associated with high anti- activity, which provided important guidelines for the design of novel trypanocidal agents having drug-like properties.