Chemoinformatics in Drug Design. Artificial Neural Networks for Simultaneous Prediction of Anti-enterococci Activities and Toxicological Profiles

Enterococci are dangerous opportunistic pathogens which are responsible of a huge number of nosocomial infections, displaying intrinsic resistance to many antibiotics. The battle against enterococci by using antimicrobial chemotherapies will depend on the design of new antibacterial agents with high activity and low toxicity. Multi-target methodologies focused on quantitative-structure activity relationships (mtQSAR), have contributed to rationalize the process of drug discovery, improving the knowledge about the molecular patterns related with antimicrobial activity. Until know, almost all mt-QSAR models have considered the study of biological activity or toxicity separately. Here, we developed a unified mtk-QSBER (multitasking quantitative-structure biological effect relationships) model for simultaneous prediction of anti-enterococci activity and toxicity on laboratory animal and human immune cells. The mtk-QSBER model was created by using artificial neural network (ANN) analysis combined with topological indices, with the aim of classifying compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under multiple experimental conditions. The mtk-QSBER model correctly classified more than 90% of the whole dataset (more than 10900 cases). We used the model to predict multiple biological effects of the investigational drug BC-3781. Results demonstrate that our mtk-QSBER may represent a new horizon for the discovery of desirable anti-enterococci drugs.