PRISM-Gen: A physics-informed multi-fidelity framework for broad-spectrum coronavirus mpro inhibitor discovery.

Authors

Hongmei Wang, Xinjia Zeng, Lirong Zhu, Aiping Yang, Meiling Zhang

Published in

Computational biology and chemistry. Volume 124. Issue Pt 2. Pages 109199. Jun 16, 2026. Epub Jun 16, 2026.

Abstract

Coronavirus main protease (Mpro) is a conserved antiviral target, yet most generative AI pipelines optimize surrogate-predicted affinity without enforcing electronic plausibility or cross-target consistency. We present PRISM-Gen (Physics-guided Robust Inhibitor Selection Method - Generative Module), a multi-fidelity framework coupling fragment-tree molecular generation with a three-tier electronic screening cascade - GFN2-xTB semi-empirical descriptors, Gaussian Electronic Moderation (GEM) scoring, and B3LYP/6-31 G* DFT validation - followed by conservative worst-case docking across SARS-CoV-2, SARS-CoV-1, and MERS-CoV Mpro. Applied to 4136 generated candidates, the pipeline identifies 36 broad-spectrum-consistent inhibitor candidates whose top-ranked members exhibit predicted worst-case binding energies comparable to those of the non-covalent reference inhibitor ensitrelvir under identical docking conditions, while sharing no Bemis-Murcko scaffolds with nirmatrelvir, ensitrelvir, or GC376. Stage-wise statistical validation confirms that each tier exerts non-redundant, orthogonal selection pressure. Retrospective analysis demonstrates that replacing GEM's continuous moderation with a conventional hard electronic cutoff would eliminate 48.0% of candidates, including 55.6% of the final 36 molecules, disproportionately depleting scaffold diversity. These results establish that continuous, physics-informed electronic moderation integrated within a multi-fidelity generative pipeline can recover structurally novel chemotypes that binary exclusion filters would irreversibly discard - a design principle applicable to generator-agnostic molecular discovery workflows.

PMID:
42320197
Bibliographic data and abstract were imported from PubMed on 20 Jun 2026.

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