Prospective discovery of LasB inhibitors by integrating generative design, free energy ranking, and experimental validation

Authors

Martinez Leon, A., Vgenopoulou, A., Jezequel, G., Kany, A. M., Hirsch, A. K., Hub, J. S.

Abstract

Identifying synthetically accessible, potent ligands from vast chemical space remains a major challenge in drug discovery, particularly because experimental synthesis and testing are costly and low throughput. Here, we present a fully open-source computational-experimental workflow that integrates fragment-based de novo molecular design with rigorous alchemical absolute binding free-energy calculations to enable physics-based prioritization before chemical synthesis. We apply this strategy by targeting the virulence-associated zinc metalloprotease LasB from Pseudomonas aeruginosa. From ~40,000 diverse compounds generated through multi-objective optimization, a small subset was prioritized and experimentally evaluated, leading to the discovery of three previously unreported nanomolar inhibitors (IC50 22-53 nM) with excellent metabolic and plasma stability. This work demonstrates a general and scalable framework for coupling generative molecular design with quantitative binding affinity prediction, improving decision-making and reducing the cost, time, and risk associated with early-stage drug discovery.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 14 Oct 2026.

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