Prospective discovery of LasB inhibitors by integrating generative design, free energy ranking, and experimental validation
- 1Saarland University
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.
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BibTeX
@misc{leon2026prospective,
title = {{Prospective discovery of LasB inhibitors by integrating generative design, free energy ranking, and experimental validation}},
author = {A. Martinez Leon and A. Vgenopoulou and G. Jezequel and A. M. Kany and A. K. Hirsch and J. S. Hub},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.12.758448},
url = {https://www.biorxiv.org/content/10.64898/2026.10.12.758448v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Prospective discovery of LasB inhibitors by integrating generative design, free energy ranking, and experimental validation
AU - A. Martinez Leon
AU - A. Vgenopoulou
AU - G. Jezequel
AU - A. M. Kany
AU - A. K. Hirsch
AU - J. S. Hub
PY - 2026
DA - 2026/10/12
PB - bioRxiv
DO - 10.64898/2026.10.12.758448
UR - https://www.biorxiv.org/content/10.64898/2026.10.12.758448v1
AB - 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.
ER -