Chemoinformatics for Medicinal Chemistry: In Silico Model to Enable the Discovery of Potent and Safer Anti-Cocci Agents
Alejandro Speck‐Planche, M. Natália D. S. Cordeiro · Future Medicinal Chemistry · 2014
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.