| Texto completo | |
| Autor(es): |
de Souza, Anacleto S.
;
Ferreira, Leonardo L. G.
;
de Oliveira, Aldo S.
;
Andricopulo, Adriano D.
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES; v. 20, n. 11, p. 21-pg., 2019-06-01. |
| Resumo | |
Small-molecule compounds that have promising activity against macromolecular targets from Trypanosoma cruzi 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-T. cruzi activity data for broadly diverse chemotypes. The models exhibited a good predictive ability for the test set compounds, yielding q(2) 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-T. cruzi activity, which provided important guidelines for the design of novel trypanocidal agents having drug-like properties. (AU) | |
| Processo FAPESP: | 13/07600-3 - CIBFar - Centro de Inovação em Biodiversidade e Fármacos |
| Beneficiário: | Glaucius Oliva |
| Modalidade de apoio: | Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs |