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.