Chemoinformatics for medicinal chemistry: in silico model to enable the discovery of potent and safer anti-cocci agents. (December 2014) - Speck-Planche, Alejandro; Cordeiro, Maria Natália Dias Soeiro
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.