DiffMC-Gen: A Dual Denoising Diffusion Model for Multi-Conditional Molecular Generation

The precise and efficient design of potential drug molecules with diverse physicochemical properties has long been a critical challenge. In recent years, the emergence of various deep learning-based de novo molecular generation algorithms offered new directions to this issue, among which denoising diffusion models have demonstrated significant potential. However, previous methods often fail to simultaneously optimize multiple properties of candidate compounds, which may stem from directly employing nongeometric graph neural networks (GNNs), rendering them incapable of accurately capturing molecular topologic and geometric information. In this study, a dual denoising diffusion model is developed for multi-conditional molecular generation (DiffMC-Gen), which integrates both discrete and continuous features to enhance its ability to perceive 3D molecular structures. Additionally, it involves a multi-objective optimization strategy to simultaneously optimize multiple properties of the target molecule, including binding affinity, drug-likeness, synthesizability, and toxicity. From the perspectives of both 2D and 3D molecular generation, the molecules generated by DiffMC-Gen exhibit state-of-the-art (SOTA) performance in terms of novelty and uniqueness, meanwhile achieving comparable results to previous methods in drug-likeness and synthesizability. Furthermore, the generated molecules have well-predicted biological activity and druglike properties for three target proteins—LRRK2, HPK1, and GLP-1 receptor, while also maintaining high standards of validity, uniqueness, and novelty. These results underscore its potential for practical applications in drug design.