Full-Atom Peptide Design based on Multi-modal Flow Matching
Helixon Research（Helixon研究院）；Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）；Department of Computer Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机科学系）
Peptides, short chains of amino acid residues, play a vital role in numerous biological processes by interacting with other target molecules, offering substantial potential in drug discovery. In this work, we present PepFlow, the first multi-modal deep generative model grounded in the flow-matching framework for the design of full-atom peptides that target specific protein receptors. Drawing inspiration from the crucial roles of residue backbone orientations and side-chain dynamics in protein-peptide interactions, we characterize the peptide structure using rigid backbone frames within the $\mathrm{SE}(3)$ manifold and side-chain angles on high-dimensional tori. Furthermore, we represent discrete residue types in the peptide sequence as categorical distributions on the probability simplex. By learning the joint distributions of each modality using derived flows and vector fields on corresponding manifolds, our method excels in the fine-grained design of full-atom peptides. Harnessing the multi-modal paradigm, our approach adeptly tackles various tasks such as fix-backbone sequence design and side-chain packing through partial sampling. Through meticulously crafted experiments, we demonstrate that PepFlow exhibits superior performance in comprehensive benchmarks, highlighting its significant potential in computational peptide design and analysis.
Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A
Centre for Cognitive Machines and Computational Health (CMaCH)（认知机器与计算健康中心）；School of Computer Science（计算机科学学院）；Shanghai Jiao Tong University（上海交通大学）；QuietD Biotech（QuietD生物科技）
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）
DC-Flow: Target-Conditioned Cyclic Peptide Design via Delayed-Coupling Flow Matching
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；DP Technology（DP科技公司）；Cyclon Therapeutics（Cyclon治疗公司）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大模型与智能治理重点实验室）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（教育部新一代智能搜索与推荐工程研究中心）
PepTriX: A Framework for Explainable Peptide Analysis through Protein Language Models
Center for Artificial Intelligence in Public Health Research (ZKI-PH), Robert Koch Institute（人工智能与公共健康研究所以及罗伯特·科赫研究所）；Department of Mathematics and Computer Science, Free University of Berlin（数学与计算机科学系，柏林自由大学）
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）
Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design
Department of Biomedical Engineering, Duke University（杜克大学生物医学工程系）；Department of Computer Science, Fudan University（复旦大学计算机科学系）；Center of 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）；Management and Technology Program, University of Pennsylvania（宾夕法尼亚大学管理与技术项目）；Department of Computer Science, Duke University（杜克大学计算机科学系）；Department of Biostatistics and Bioinformatics, Duke University（杜克大学生物统计与生物信息学系）
提出 MOG-DFM 框架，通过混合秩方向分数和自适应超锥滤波引导预训练离散流匹配模型，实现多目标帕累托高效的肽与增强子 DNA 序列设计。
A decoupled alignment kernel for peptide membrane permeability predictions
Center for Applied Intelligent Systems Research in Health（应用智能系统研究健康中心）；Halmstad University（哈姆斯塔德大学）；Chalmers University of Technology（查尔姆斯理工大学）；AstraZeneca（阿斯利康）；Region Halland（哈兰德地区）；Lund University（吕勒欧大学）
AsyncCouple-Flow: Asynchronous Cross-Modal Coupling and Flow Matching for Spatio-Temporal Forecasting
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Emory University（埃默里大学）；University of California, Berkeley（加州大学伯克利分校）；Yale University（耶鲁大学）；Stevens Institute of Technology（史蒂文斯理工学院）；City University of New York（纽约城市大学）
Force-Guided Bridge Matching for Full-Atom Time-Coarsened Dynamics of Peptides
Tsinghua University（清华大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）
