A 3D generation framework using diffusion model and reinforcement learning to generate multi-target compounds with desired properties.

Deep generative models provide a powerful solution for the de novo design of molecules. However, the majority of existing methods only generate molecules for a single target. Generating molecules with biological activities against multiple specific targets and desired properties remains an extremely difficult challenge. In this study, we propose a novel 3D molecule generation framework based on reinforcement learning and diffusion model to generate molecules with predefined properties for given multiple targets. The proposed framework, MDRL, uses a diffusion model to understand the 3D chemical structure of molecules and employs Kolmogorov-Arnold Networks instead of Multilayer Perceptron to enhance model performance. Through reinforcement learning, the framework is able to generate molecules that simultaneously target two targets and further optimizes multiple molecular properties. Experimental results show that our model exhibits comparable performance to various state-of-the-art molecular generation models, and MDRL can effectively navigate chemical space to design polypharmacological compounds and control multiple molecular properties. In multiple case studies, we verify that the generated molecules can simultaneously target two targets through molecular docking and assess the model's ability to control multiple molecular properties. The results in this study highlight the advantages and practicalities of our model in generating polypharmacological compounds with desired properties.