Chemoinformatics for medicinal chemistry: In silico model to enable the discovery of potent and safer anti-cocci agents

Alejandro Speck-Planche, Maria Natália Dias Soeiro Cordeiro

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

41 Citas (Scopus)

Resumen

Background: Gram-positive cocci are increasingly antibiotic-resistant bacteria responsible for causing serious diseases. Chemoinformatics can help to rationalize the discovery of more potent and safer antibacterial drugs. We have developed a chemoinformatic model for simultaneous prediction of anti-cocci activities, and profiles involving absorption, distribution, metabolism, elimination and toxicity (ADMET). Results: A dataset containing 48,874 cases from many different chemicals assayed under dissimilar experimental conditions was created. The best model displayed accuracies around 93% in both training and prediction (test) sets. Quantitative contributions of several fragments to the biological effects were calculated and analyzed. Multiple biological effects of the investigational drug JNJ-Q2 were correctly predicted. Conclusion: Our chemoinformatic model can be used as powerful tool for virtual screening of promising anti-cocci agents.

Idioma originalInglés
Páginas (desde-hasta)2013-2028
Número de páginas16
PublicaciónFuture Medicinal Chemistry
Volumen6
N.º18
DOI
EstadoPublicada - 1 dic. 2014
Publicado de forma externa

Huella

Profundice en los temas de investigación de 'Chemoinformatics for medicinal chemistry: In silico model to enable the discovery of potent and safer anti-cocci agents'. En conjunto forman una huella única.

Citar esto