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Updated: Jun 21, 2026

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
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Published on: September 26, 2025

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

Hongmei Wang1, Xinjia Zeng2, Lirong Zhu1

  • 1School of Basic Medical Sciences, Tianjin Medical University, 22 Qixiangtai Road, Tianjin 300070, China.

Computational Biology and Chemistry
|June 19, 2026
PubMed
Summary

We developed PRISM-Gen, a novel AI method for discovering broad-spectrum coronavirus Mpro inhibitors. This physics-guided approach ensures electronic plausibility and identifies unique drug candidates missed by traditional filters.

Keywords:
Broad-spectrum antiviralsCoronavirus main proteaseDistributionally robust optimizationGaussian electronic moderationGenerative molecular designMulti-fidelity electronic screening

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08:35

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Published on: May 29, 2021

Area of Science:

  • Drug Discovery
  • Computational Chemistry
  • Artificial Intelligence in Medicine

Background:

  • Coronavirus main protease (Mpro) is a key antiviral target.
  • Existing AI methods often lack electronic plausibility and cross-target consistency in inhibitor design.

Purpose of the Study:

  • To present PRISM-Gen, a multi-fidelity generative framework for identifying broad-spectrum Mpro inhibitors.
  • To enforce electronic plausibility and cross-target consistency in AI-driven drug discovery.

Main Methods:

  • Coupling fragment-tree molecular generation with a three-tier electronic screening cascade (GFN2-xTB, GEM, DFT).
  • Utilizing conservative worst-case docking across SARS-CoV-2, SARS-CoV-1, and MERS-CoV Mpro.
  • Implementing continuous, physics-informed electronic moderation for candidate selection.

Main Results:

  • Identified 36 broad-spectrum Mpro inhibitor candidates from 4136 generated molecules.
  • Top candidates showed binding energies comparable to ensitrelvir under identical docking conditions.
  • Continuous electronic moderation preserved scaffold diversity, unlike hard electronic cutoffs.

Conclusions:

  • PRISM-Gen effectively identifies novel, broad-spectrum Mpro inhibitors with high structural diversity.
  • Physics-informed electronic moderation is crucial for discovering unique chemotypes in AI drug discovery.
  • The PRISM-Gen framework offers a versatile design principle for generator-agnostic molecular discovery.