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.