In silico method and bioactivity evaluation to discover novel antimicrobial agents targeting FtsZ protein: Machine learning, virtual screening and antibacterial mechanism study.

This research paper utilizes a fused-in-silico approach alongside bioactivity evaluation to identify active FtsZ inhibitors for drug discovery. Initially, ROC-guided machine learning was employed to obtain almost 13182 compounds from three libraries. After conducting virtual screening to assess the affinity of 2621 acquired compounds, cluster analysis and bonding model analysis led to the discovery of five hit compounds. Additionally, antibacterial activity assays and time-killing kinetics revealed that T3995 could eliminate Staphylococcus aureus ATCC6538 and Bacillus subtilis ATCC9732, with MIC values of 32 and 2 μg/mL. Further morphology and FtsZ polymerization assays indicated that T3995 could be an antimicrobial inhibitor by targeting FtsZ protein. Moreover, hemolytic toxicity evaluation demonstrated that T3995 is safe at or below 16 ug/mL concentration. Additionally, bonding model analysis explained how the compound T3995 can display antimicrobial activity by targeting the FtsZ protein. In conclusion, this study presents a promising FtsZ inhibitor that was discovered through a fused computer method and bioactivity evaluation.