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Purpose <p>Lung adenocarcinoma (LUAD) undergoes extensive metabolic rewiring that promotes uncontrolled proliferation and survival. Uridine phosphorylase 1 (UPP1), a key enzyme in the pyrimidine salvage pathway, drives glycolytic activation and histone acetylation-mediated epigenetic regulation in LUAD. However, no clinically approved UPP1 inhibitors currently exist. This study aimed to identify potent natural and analog inhibitors of UPP1 using an integrative structure-based and artificial intelligence (AI) guided computational drug discovery approach.</p> Methods <p>A library of 695,129 natural compounds was curated from the COCONUT database and filtered using custom Python scripts based on Lipinski’s Rule of Five. Structure-based virtual screening was performed using molecular docking, followed by ADMET profiling to evaluate pharmacokinetic and toxicity profiles. To improve potency and drug-likeness, structural analogs of the lead compound were generated using an RDKit-integrated workflow. AI-based modeling further validated optimal protein–ligand conformations. Finally, the stability and energetics of UPP1–ligand complexes were assessed through 500 ns molecular dynamics simulations, MM/GBSA free energy calculations, and free energy landscape analyses.</p> Results <p>CNP0285821.1, a flavonoid derived from Zephyranthes flava (Yellow Rain Lily), and its optimized structural analog (Analog_56) emerged as the most promising UPP1 inhibitors. These compounds exhibited strong docking affinities (− 10.5 and − 10.7&#xa0;kcal/mol, respectively), strong predicted IC<sub>50</sub> values (19.8nM and 14.14nM, respectively), and favorable binding free energies (ΔG_TOTAL of − 25.74 and − 30.51&#xa0;kcal/mol, respectively), indicating stable interactions within the UPP1 catalytic pocket. ADMET analysis of both parent and analog compounds revealed improved pharmacokinetic properties and reduced toxicity risk for Analog_56. Long-timescale simulations demonstrated that Analog_56 maintained a compact, deeply buried binding pose with reduced conformational fluctuation and more persistent protein-ligand interactions, supported by MM/GBSA and free energy landscape analyses.</p> Conclusion <p>This integrative computational approach identifies natural-product scaffolds capable of targeting UPP1 and provides a strong foundation for developing LUAD therapeutics aimed at disrupting uridine metabolism and histone acetylation–mediated oncogenic regulation. Future experimental studies are required to validate these in silico findings and advance these candidates toward anti-cancer therapeutic development.</p> Graphical Abstract <p></p>

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Integrative Structure-Based and AI-Guided Discovery of Natural and Analog UPP1 Inhibitors Targeting the Epigenetic-Metabolic Axis in Lung Adenocarcinoma

  • 期刊论文

  • 发表日期：

    2026年5月14日

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  • Arkajit De

摘要

Purpose

Lung adenocarcinoma (LUAD) undergoes extensive metabolic rewiring that promotes uncontrolled proliferation and survival. Uridine phosphorylase 1 (UPP1), a key enzyme in the pyrimidine salvage pathway, drives glycolytic activation and histone acetylation-mediated epigenetic regulation in LUAD. However, no clinically approved UPP1 inhibitors currently exist. This study aimed to identify potent natural and analog inhibitors of UPP1 using an integrative structure-based and artificial intelligence (AI) guided computational drug discovery approach.

Methods

A library of 695,129 natural compounds was curated from the COCONUT database and filtered using custom Python scripts based on Lipinski’s Rule of Five. Structure-based virtual screening was performed using molecular docking, followed by ADMET profiling to evaluate pharmacokinetic and toxicity profiles. To improve potency and drug-likeness, structural analogs of the lead compound were generated using an RDKit-integrated workflow. AI-based modeling further validated optimal protein–ligand conformations. Finally, the stability and energetics of UPP1–ligand complexes were assessed through 500 ns molecular dynamics simulations, MM/GBSA free energy calculations, and free energy landscape analyses.

Results

CNP0285821.1, a flavonoid derived from Zephyranthes flava (Yellow Rain Lily), and its optimized structural analog (Analog_56) emerged as the most promising UPP1 inhibitors. These compounds exhibited strong docking affinities (− 10.5 and − 10.7 kcal/mol, respectively), strong predicted IC50 values (19.8nM and 14.14nM, respectively), and favorable binding free energies (ΔG_TOTAL of − 25.74 and − 30.51 kcal/mol, respectively), indicating stable interactions within the UPP1 catalytic pocket. ADMET analysis of both parent and analog compounds revealed improved pharmacokinetic properties and reduced toxicity risk for Analog_56. Long-timescale simulations demonstrated that Analog_56 maintained a compact, deeply buried binding pose with reduced conformational fluctuation and more persistent protein-ligand interactions, supported by MM/GBSA and free energy landscape analyses.

Conclusion

This integrative computational approach identifies natural-product scaffolds capable of targeting UPP1 and provides a strong foundation for developing LUAD therapeutics aimed at disrupting uridine metabolism and histone acetylation–mediated oncogenic regulation. Future experimental studies are required to validate these in silico findings and advance these candidates toward anti-cancer therapeutic development.

Graphical Abstract