Chemical Space Exploration and Reinforcement Learning for Discovery of Novel Benzimidazole Hybrid Antibiotics

Benzimidazole hybrids are promising antibacterial agents, but the growing problem of antibiotic resistance has led to the necessity of developing novel compounds with enhanced antimicrobial activity. This study utilizes AI methods to generate new antibacterial compounds based on benzimidazole derivatives. We compiled a data set of these hybrids to explore their chemical space and identify effective scaffolds. An interpretable machine learning model was trained, achieving an R² of 0.81 and RMSE of 0.212 for bioactivity prediction. Additionally, we employed a reinforcement learning model to create novel hybrid antibiotics through fragment-based, linker-based, and de novo approaches, selecting candidates based on bioactivity and off-target effects. This process yielded 56 novel synthetically feasible compounds with lower minimum inhibitory concentrations and improved drug-like properties. Molecular docking studies and absolute binding free energy (ABFE) calculations revealed that these generated molecules exhibit higher binding affinities to target proteins compared to approved antibiotics like ciprofloxacin and novobiocin.