Denoising diffusion probabilistic models (DDPMs) have pioneered new state-of-the-art results in disciplines such as computer vision and computational biology for diverse tasks ranging from text-guided image generation to structure-guided protein design. Along this latter line of research, methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a DDPM framework. However, such methods are unable to learn important geometric properties of 3D molecules, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which notably hinders their ability to generate valid large 3D molecules. In this work, we address these gaps by introducing the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation, which outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings for the QM9 dataset and the larger GEOM-Drugs dataset, respectively, and generates more novel and unique unconditional 3D molecules for the QM9 dataset compared to previous methods. Importantly, we demonstrate that the geometry-complete denoising process of GCDM learned for 3D molecule generation enables the model to generate a significant proportion of valid and energetically-stable large molecules at the scale of GEOM-Drugs, whereas previous methods fail to do so with the features they learn. Additionally, we show that extensions of GCDM can not only effectively design 3D molecules for specific protein pockets but also that GCDM's geometric features can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules for molecular stability and property specificity, demonstrating new versatility of molecular diffusion models. Our source code and data are freely available at https://github.com/BioinfoMachineLearning/Bio-Diffusion.
NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation
National University of Singapore（新加坡国立大学）；University of Science and Technology of China（中国科学技术大学）；Chinese University of Hong Kong（香港中文大学）；Hokkaido University（北海道大学）
Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion
Huawei Technologies Co., Ltd.（华为技术有限公司）；Chinese Academy of Sciences（中国科学院）；Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China（电子科技大学深圳高等研究院）；Université de Montréal（蒙特利尔大学）
Diff4VS: HIV-inhibiting Molecules Generation with Classifier Guidance Diffusion for Virtual Screening
Information Science and Technology College, Dalian Maritime University（大连海事大学信息科学技术学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；School of Innovation and Entrepreneurship, Dalian University of Technology（大连理工大学创新创业学院）
D3MES: Diffusion Transformer with multihead equivariant self-attention for 3D molecule generation
School of Physics and Electronic Engineering, Jiangsu University（江苏大学物理与电子工程学院）；Jiangsu Engineering Research Center on Quantum Perception and Intelligent Detection of Agricultural Information（江苏省农业信息量子感知与智能检测工程研究中心）
A 3D pocket-aware and evolutionary conserved interaction guided diffusion model for molecular optimization
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Pharmaceutical Science, Sun Yat-sen University（中山大学药学院）；West China School of Pharmacy, Sichuan University（四川大学华西药学院）
Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules versus Therapeutic Peptides
Xinjiang Key Laboratory of Biological Resources and Genetic Engineering（新疆生物资源与基因工程重点实验室）；College of Life Science and Technology, Xinjiang University（新疆大学生命科学与技术学院）；School of Mathematics and System Sciences, Xinjiang University（新疆大学数学与系统科学学院）
Unraveling the Potential of Diffusion Models in Small Molecule Generation
Department of Computer Science and Engineering, University of Connecticut（计算机科学与工程系，康涅狄格大学）；Department of Biomedical Engineering, University of Connecticut（生物医学工程系，康涅狄格大学）
Diffusion Models for Molecules: A Survey of Methods and Tasks
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Northwestern Polytechnical University（西北工业大学）；National University of Singapore（新加坡国立大学）；Alibaba DAMO Academy（阿里巴巴达摩院）
Chemistry-Enhanced Diffusion-Based Framework for Small-to-Large Molecular Conformation Generation
SCNU Environmental Research Institute（华南师范大学环境研究院）；Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety（广东省化学污染与环境安全重点实验室）；School of Environment, South China Normal University（华南师范大学环境学院）；MOE Key Laboratory of Environmental Theoretical Chemistry（教育部环境理论化学重点实验室）
Peptide2Mol: A Diffusion Model for Generating Small Molecules as Peptide Mimics for Targeted Protein Binding
Lingang Laboratory（梁岗实验室）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究所）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究所）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Department of Pharmaceutical and Artificial-Intelligence Sciences, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院药学与人工智能科学系）；Institute of Medical Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学医学人工智能研究所）
Generating 3D Binding Molecules Using Shape-Conditioned Diffusion Models with Guidance
The Ohio State University（俄亥俄州立大学）；University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；Translational Data Analytics Institute, The Ohio State University（俄亥俄州立大学转化数据分析研究所）
Guiding Diffusion Models with Reinforcement Learning for Stable Molecule Generation
Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；INFTECH；Zhongshan Hospital（中山医院）
