Geometric Structure-Aware Diffusion Model with Self-Optimization Strategy for Molecular Generation

With the advancement of artificial intelligence, molecular design based on generative models offers novel approaches to accelerate drug discovery. However, existing molecular generation methods suffer from inadequate representational capability in geometric structure and discrepancies between topological and geometric structure representations. These challenges result in generating chemically implausible and structurally unstable molecules. Furthermore, existing methods neglect the crucial properties of both quantum and drug-likeness in drug design. To address these challenges, we propose a novel Geometric Structure-Aware Diffusion Model for molecular generation and optimization tasks, named MolGD. First, we designed a Geometric Structure-Aware Network (GSAN) to directly predict structurally stable molecules from noisy inputs. Within GSAN, a Molecular Graph Attention Network (MGAT) is designed to incorporate geometric information during the topological message-passing process. Then, atomic spatial positions are updated by a Geometric Reconstruction Network (GRN) for enabling integrated modeling of molecular structures. Second, MolGD integrates quantum attributes as conditional constraints for precise quantum property regulation. These conditional constraints can guide MolGD to generate molecules with specific quantum properties. Finally, for drug-likeness property optimization, MolGD integrates self-optimization strategies (MolGD-RL) to guide the model toward generating high drug-likeness and easily synthesisable molecules. Experimental results on the quantum chemistry data set QM9 and the molecular conformation data set GEOM-Drugs demonstrate that the MolGD model outperforms existing molecular generation methods in terms of the effectiveness and stability of generated molecules, the generation of specific quantum properties, and high drug-likeness optimization. This validates its efficacy in molecular generation and optimization tasks, offering novel insights for intelligent molecular design.