SyntheMol-RL: a flexible reinforcement learning framework for designing easily synthesizable antibiotics

ABSTRACT: The rise of antibiotic resistance has created an urgent need for novel antibacterial drugs. Generative artificial intelligence (AI) has shown promise in drug discovery, but existing models often fail to propose compounds that are synthetically tractable. We introduce SyntheMol-RL, a reinforcement learning-based generative model that can design synthetically accessible small molecule candidates from a chemical space of 46 billion compounds. SyntheMol-RL improves upon our prior Monte Carlo tree search (MCTS)-based SyntheMol model by generalizing across chemically similar building blocks and enabling multi-parameter optimization. We applied SyntheMol-RL to generate antibiotics against Staphylococcus aureus and found that SyntheMol-RL generated molecules with improved predicted properties comp

SUBMITTER: Kyle Swanson 

PROVIDER: S-SCDT-10_1038-S44320-026-00206-9 | biostudies-other |

REPOSITORIES: biostudies-other