2022/09/13 by Lei Huang, Huang, Lei, Hengtong Zhang +6 · 11 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Biomolecules (q-bio.BM) #Cell Image Analysis Techniques #FOS: Biological sciences #FOS: Computer and information sciences #Innovative Microfluidic and Catalytic Techniques Innovation #Machine Learning (cs.LG) #Microfluidic and Capillary Electrophoresis Applications #cs.LG #q-bio.BM
paper · pdf · doi:10.48550/arxiv.2209.05710
Submitted to AAAI'23
arxiv created 2022/09/13 · openalex publication_date 2022/09/13 · arxiv updated 2022/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Molecule generation, especially generating 3D molecular geometries from scratch (i.e., 3D de novo generation), has become a fundamental task in drug designs. Existing diffusion-based 3D molecule generation methods could suffer from unsatisfactory performances, especially when generating large molecules. At the same time, the generated molecules lack enough diversity. This paper proposes a novel diffusion model to address those two challenges. First, interatomic relations are not in molecules' 3D point cloud representations. Thus, it is difficult for existing generative models to capture the potential interatomic forces and abundant local constraints. To tackle this challenge, we propose to augment the potential interatomic forces and further involve dual equivariant encoders to encode interatomic forces of different strengths. Second, existing diffusion-based models essentially shift elements in geometry along the gradient of data density. Such a process lacks enough exploration in the intermediate steps of the Langevin dynamics. To address this issue, we introduce a distributional controlling variable in each diffusion/reverse step to enforce thorough explorations and further improve generation diversity. Extensive experiments on multiple benchmarks demonstrate that the proposed model significantly outperforms existing methods for both unconditional and conditional generation tasks. We also conduct case studies to help understand the physicochemical properties of the generated molecules.