Chemoinformatics in antibacterial drug discovery: Simultaneous modeling of anti-enterococci activities and ADMET profiles through the use of probabilistic quadratic indices

ABSTRACT

Enterococci are Gram-positive bacteria responsible for causing multiple nosocomial infections in humans. Chemoinformatics could be a great ally of medicinal chemistry in the search for efficacious anti-enterococci drugs. Current methods cannot model the anti-enterococci activity and ADMET (absorption, distribution, metabolism, elimination, toxicity) properties at the same time. We create the first multitasking model for quantitative-structure biological effect relationships (mtk-QSBER), focused on the simultaneous prediction of anti-enterococci activities and ADMET profiles of compounds.  The mtk-QSBER model was constructed by using a large and heterogeneous dataset of chemicals, and exhibited accuracy higher than 95% in both training and prediction sets. We provided the physicochemical interpretations of the molecular descriptors (probabilistic quadratic indices) that entered in the model. In order to demonstrate the practical utility of our model, we predicted multiple biological profiles of the investigational antibacterial drug oritavancin, and the results of the virtual predictions strongly converged with the experimental evidences. To date, this is the first attempt to use a unified in silico model to guide drug discovery in antimicrobial research by predicting the antibacterial potency against enterococci, as well as the safety in laboratory animals and humans.