Pocket-Based Generative Diffusion Model Accelerates Potent Influenza A Hemagglutinin Inhibitor Discovery

The deep generative model has recently advanced 3D chemical
space
exploration but overlooked the balance between target affinity and
structural rationality, limiting their effectiveness in drug discovery.
Herein, we established a novel dual conditional diffusion model (DCDM)
that leveraged ligand-protein interaction features to refine 3D target-based
molecular generation. DCDM exhibited superiority in enhancing predicted
binding affinity while maintaining high structural rationality and
diversity. Subsequently, we applied DCDM to optimize penindolone (PND),
a marine-derived lead compound from our laboratory, targeting influenza
A hemagglutinin (HA). Efficiently, a promising candidate (compound C2e) was successfully obtained from eight synthesized derivatives
inspired by the DCDM-generated molecules, with a 26-fold higher affinity
for HA. Notably, C2e exhibited a 10-fold decrease in
IC50 compared with the parent compound PND. Further in
vivo assessments demonstrated its potent antiviral activity and safety.
All results indicate that DCDM is a valuable generative model, capable
of accelerating drug development in real-world applications.