Abstract: The new generation of artificial intelligence technology, represented by deep learning, has emerged as a crucial driving force in the advancement of new drug research and development. This article creatively proposes a workflow named “Molecular Factory” for the design and optimization of drug molecules based on artificial intelligence technology. This workflow integrates intelligent molecular generation models, high-performance molecular docking algorithms, and accurate protein-ligand binding affinity prediction methods. It has been integrated as a core module into DrugFlow, a one-stop drug design software platform, providing a comprehensive set of mature solutions for the discovery and optimization of lead compounds. Utilizing the “Molecular Factory” module, we conducted the research of second-generation inhibitors against Menin that can combat drug resistance. Through the integration of computational and experimental approaches, we rapidly identified multiple promising compounds. Among them, compound RG-10 exhibited the IC₅₀ values of 9.681 nmol/L, 233.2 nmol/L, and 40.09 nmol/L against the wild-type Menin, M327I mutant, and T349M mutant, respectively. Compared to the positive reference molecule SNDX-5613, which has entered Phase II clinical trials, RG-10 demonstrated significantly enhanced inhibitory activity against the M327I and T349M mutants. These findings fully demonstrate the unique advantages of the "Molecular Factory" technology in practical drug design and development scenarios. It can rapidly and efficiently generate high-quality active molecules targeting specific protein structures, holding significant value and profound implications for advancing new drug discovery.