Moremi Bio Agent: Leveraging Agentic Large Language Model for the Discovery of Broad-Spectrum Antibiotics for Enterobacteriaceae

Antimicrobial resistance (AMR) is a pressing global health crisis, exacerbated by a stag- nating antibiotic discovery pipeline and the emergence of multidrug-resistant pathogens such as Klebsiella pneumoniae. We hypothesize that dual-target strategies may offer a more robust means to overcome AMR by reducing the likelihood of resistance development compared to single-target approaches. In response, we leveraged Moremi Bio Agent, an agentic large lan- guage model (LLM) for the autonomous design, in silico validation, and prioritization of broad- spectrum antibiotics targeting Enterobacteriaceae. Using our proposed dual-target strategy, we generated and evaluated 1,002 candidate molecules predicted to simultaneously inhibit the FabI enzyme and the AcrAB-TolC efflux pump, two key resistance mechanisms in Gram- negative bacteria. Our fully autonomous pipeline integrated compound generation, molecular docking, pharmacodynamics/pharmacokinetic predictions & toxicity profiling, and molecule- ranking based on ADMET and drug-likeness properties. Out of 1,002 molecules generated, 774 passed preliminary ADMET benchmarks, with majority of the 60 top-performing candidates (score ≥0.8) showing favorable drug-likeness, minimal toxicity. 391 of the compounds exhib- ited moderate binding interaction to both targets. This study demonstrates the feasibility of AI-driven antibiotic discovery and lays the foundation for future experimental validation to address AMR.