Protein structure prediction is pivotal for understanding the structure-function relationship of proteins, advancing biological research, and facilitating pharmaceutical development and experimental design. While deep learning methods and the expanded availability of experimental 3D protein structures have accelerated structure prediction, the dynamic nature of protein structures has received limited attention. This study introduces an innovative 4D diffusion model incorporating molecular dynamics (MD) simulation data to learn dynamic protein structures. Our approach is distinguished by the following components: (1) a unified diffusion model capable of generating dynamic protein structures, including both the backbone and side chains, utilizing atomic grouping and side-chain dihedral angle predictions; (2) a reference network that enhances structural consistency by integrating the latent embeddings of the initial 3D protein structures; and (3) a motion alignment module aimed at improving temporal structural coherence across multiple time steps. To our knowledge, this is the first diffusion-based model aimed at predicting protein trajectories across multiple time steps simultaneously. Validation on benchmark datasets demonstrates that our model exhibits high accuracy in predicting dynamic 3D structures of proteins containing up to 256 amino acids over 32 time steps, effectively capturing both local flexibility in stable states and significant conformational changes. URL: https://fudan-generative-vision.github.io/AlphaFolding/#/
ProteinGuide: On-the-fly property guidance for protein sequence generative models
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley（电气工程与计算机科学系，加州大学伯克利分校）；Center for Computational Biology, University of California, Berkeley（计算生物学中心，加州大学伯克利分校）；Department of Molecular and Cell Biology, University of California, Berkeley（分子与细胞生物学系，加州大学伯克利分校）；Innovative Genomics Institute, University of California, Berkeley（创新基因组研究所，加州大学伯克利分校）；Howard Hughes Medical Institute, University of California, Berkeley（霍华德·休斯医学研究所，加州大学伯克利分校）
Protein Counterfactuals via Diffusion-Guided Latent Optimization
Machine Learning Group, Technische Universität Berlin（技术大学柏林机器学习组）；Berlin Institute for the Foundations of Learning and Data (BIFOLD)（柏林学习与数据基础研究所（BIFOLD））；BASF Digital Solutions GmbH（巴斯夫数字解决方案有限公司）
Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation
Duke University（杜克大学）；University of Pennsylvania（宾夕法尼亚大学）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Mila, Quebec AI Institute（米拉魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）
Monte Carlo Tree Diffusion with Multiple Experts for Protein Design
University of Chicago（芝加哥大学）；Data Science Institute（数据科学研究所）；Department of Biochemistry and Molecular Biology（生物化学与分子生物学系）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）；Argonne National Laboratory（阿贡国家实验室）；Princeton University（普林斯顿大学）
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）
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）
Chalmers University of Technology and University of Gothenburg, Dept. Computer Science and Engineering（查尔姆斯理工大学和哥德堡大学，计算机科学与工程系）；Massachusetts Institute of Technology, Research Laboratory of Electronics（麻省理工学院，电子研究实验室）；Technical University of Munich（慕尼黑技术大学）；Helmholtz AI, Munich（海德堡人工智能研究所，慕尼黑）；Max Planck Institute for Intelligent Systems, Tübingen（马克斯·普朗克智能系统研究所，图宾根）
Improved motif-scaffolding with SE(3) flow matching
Computer Science and Artificial Intelligence Laboratory（计算机科学与人工智能实验室）；Massachusetts Institute of Technology（麻省理工学院）；University of Oxford（牛津大学）；University of Cambridge（剑桥大学）；Microsoft Research AI4Science（微软研究院AI4Science部门）
DriftLite: Lightweight Drift Control for Inference-Time Scaling of Diffusion Models
Institute for Computational and Mathematical Engineering, Stanford University（计算与数学工程研究所，斯坦福大学）；Department of Chemistry, Stanford University（化学系，斯坦福大学）；Department of Mathematics, Stanford University（数学系，斯坦福大学）；Center for Computational Mathematics, Flatiron Institute（计算数学中心，Flatiron研究所）
READ: A Retrieval-Alignment Diffusion Framework for Structure-based Drug Design
School of Artificial Intelligence（人工智能学院）；Shenzhen University（深圳大学）；National Engineering Laboratory for Big Data System Computing Technology（大数据系统计算技术国家工程实验室）；School of Computer Science（计算机科学学院）；University of Nottingham Ningbo（诺丁汉大学宁波校区）；Department of Computing（计算学院）
Structure-aware Reinforcement Learning for Protein Directed Evolution
Institute of AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Pharmolix Inc.（Pharmolix公司）
TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation
Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程系）；Department of Bioengineering, University of Pennsylvania（宾夕法尼亚大学生物工程系）；Department of Computer and Information Science, University of Pennsylvania（宾夕法尼亚大学计算机与信息科学系）；Centre for Computational Biology, Duke-NUS Medical School（杜克-新加坡国立大学医学学校计算生物学中心）
MTPNet: Multi-Grained Target Perception for Unified Activity Cliff Prediction
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Pengcheng Laboratory（鹏城实验室）；AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School（北京大学深圳研究生院人工智能科学（AI4S）优选计划）
UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules
Department of Computer Science and Technology, Tsinghua University, Beijing, China（清华大学计算机科学与技术系）；Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China（清华大学人工智能产业研究院）；Gaoling School of Artificial intelligence, Renmin University of China, Beijing, China（中国人民大学agog学校人工智能学院）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（下一代智能搜索与推荐工程研究中心，教育部）
Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning
The Chinese University of Hong Kong（香港中文大学）；BioMap (Beijing) Intelligence Technology Limited（百图生科（北京）智能技术有限公司）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；The CUHK Shenzhen Research Institute（香港中文大学深圳研究院）
