The task of deducing three-dimensional molecular configurations from their two-dimensional graph representations holds paramount importance in the fields of computational chemistry and pharmaceutical development. The rapid advancement of machine learning, particularly within the domain of deep generative networks, has revolutionized the precision of predictive modeling in this context. Traditional approaches often adopt a two-step strategy: initially estimating interatomic distances and subsequently refining the spatial molecular structure by solving a distance geometry problem. However, this sequential approach occasionally falls short in accurately capturing the intricacies of local atomic arrangements, thereby compromising the fidelity of the resulting structural models. Addressing these limitations, this research introduces a cutting-edge generative framework named DDGF. This framework is grounded in the principles of diffusion observed in classical non-equilibrium thermodynamics. DDGF views atoms as discrete entities and excels in guiding the reversal of diffusion, transforming a distribution of stochastic noise back into coherent molecular structures through a process akin to a Markov chain. This transformation commences with the initial representation of a molecular graph in an abstract latent space, culminating in the realization of three-dimensional structures via a sophisticated bilevel optimization scheme meticulously tailored to meet the specific requirements of the task. One of the formidable challenges in this modeling endeavor involves preserving roto-translational invariance to ensure that the generated molecular conformations adhere to the laws of physics. Extensive experimental evaluations confirm the efficacy of the proposed DDGF in comparison to state-of-the-art methods.
WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学首都人工智能学院）；Beijing Key Laboratory of Research on Large Models（北京大型模型研究关键实验室）；Engineering Research Center of Next-Generation Intelligent Search（下一代智能搜索工程研究中心）
MuCO: Generative Peptide Cyclization Empowered by Multi-stage Conformation Optimization
Department of XXX, University of YYY, Location, Country（XXX部门，YYY大学，地点，国家）；School of ZZZ, Institute of WWW, Location, Country（ZZZ学院，WWW研究所，地点，国家）；Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China（中关村人工智能学院，中国人民大学，北京，中国）；Beijing Key Laboratory of Research on Large Models（北京大模型研究关键实验室）；Engineering Research Center of Next-Generation Intelligent Search（下一代智能搜索工程研究中心）
Polyformer: a generative framework for thermodynamic modeling of polymeric molecules
PsiDagger；Department of Physics and Astronomy, University of Pittsburgh（物理与天文学系，匹兹堡大学）；Department of Chemistry and The PULSE Institute, Stanford University（化学系和PULSE研究所，斯坦福大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Massachusetts Institute of Technology（麻省理工学院）；Peking University（北京大学）；Canadian Institute for Advanced Research (CIFAR)（加拿大高级研究院）；HEC Montréal（蒙特利尔高等商学院）
Molecular dynamics without molecules: searching the conformational space of proteins with generative neural networks
Wayne State University（韦恩州立大学）；Univ. of Bari Aldo Moro（巴里大学阿尔多·莫罗分校）；National Research Council (CONICET)（国家研究委员会（CONICET））；Daruma Institute for Applied Intelligence（达摩应用智能研究所）
Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings
Signal Processing Laboratory (LTS2), EPFL, Lausanne, Switzerland（信号处理实验室（LTS2），瑞士洛桑联邦理工学院）；Institute of Bioengineering, EPFL, Lausanne, Switzerland（生物工程研究所，瑞士洛桑联邦理工学院）；Bioinformatics Group, University & Research Wageningen, The Netherlands（生物信息学组，荷兰瓦赫宁根大学及研究学院）；Ludwig Institute for Cancer Research, Lausanne, Switzerland（癌症研究路易斯研究所，瑞士洛桑）
