A curated collection of papers, tools, and resources accompanying Flow Matching Meets Biology and Life Science: A Survey (arXiv 2025).
Flow matching is an emerging framework for generative modeling. This repository tracks its applications across biology and life sciences.
✨ This repository is continuously updated — star ⭐ it to stay up to date!
📖 If you find these resources helpful, please consider citing our survey (see Citations).
Flow Matching (FM) has recently emerged as a powerful paradigm for generative modeling, offering a flexible and scalable framework applicable across a wide range of biological domains. By constructing continuous probability trajectories between simple and complex distributions, FM provides an efficient and principled method to model high-dimensional, structured biological data while preserving structural and geometric constraints.
Contributions are welcome! If you spot errors or know of relevant papers/tools, please open an issue or submit a pull request.
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- Awesome-Flow-Matching-Biology
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Building normalizing flows with stochastic interpolants
ICLR 2023Michael S. Albergo, Eric Vanden-Eijnden
, 2023
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Variational flow matching for graph generation
NeurIPS 2024Floor Eijkelboom, Grigory Bartosh, Christian Andersson Naesseth, Max Welling, Jan-Willem van de Meent
, 2024
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Flow matching for generative modeling
ICLR 2023Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matt Le
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Improving and generalizing flow-based generative models with minibatch optimal transport
TMLRAlexander Tong, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Kilian Fatras, Guy Wolf, Yoshua Bengio
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Optimal flow matching: Learning straight trajectories in just one step
NeurIPS 2024Nikita Kornilov, Petr Mokrov, Alexander Gasnikov, Alexander Korotin
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Improving and generalizing flow-based generative models with minibatch optimal transport
TMLRAlexander Tong, Kilian Fatras, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Guy Wolf, Yoshua Bengio
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Improving the training of rectified flows
NeurIPS 2024Sangyun Lee, Zinan Lin, Giulia Fanti
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Flow straight and fast: Learning to generate and transfer data with rectified flow
ICLR 2023Xingchao Liu, Chengyue Gong, Qiang Liu
, 2023
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Flow matching on general geometries
ICLR 2024Ricky T. Q. Chen, Yaron Lipman
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α-flow: A unified framework for continuous-state discrete flow matching models
preprintCheng Cheng, Jiahao Li, Jianfei Fan, Ge Liu
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Fisher flow matching for generative modeling over discrete data
NeurIPS 2024Oscar Davis, Samuel Kessler, Mircea Petrache, Ismail Ceylan, Michael Bronstein, Joey Bose
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Neural manifold ordinary differential equations
NeurIPS 2020Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser Nam Lim, Christopher M. De Sa
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Riemannian continuous normalizing flows
NeurIPS 2020Emile Mathieu, Maximilian Nickel
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Discrete flow matching
NeurIPS 2024Itai Gat, Tal Remez, Neta Shaul, Felix Kreuk, Ricky T. Q. Chen, Gabriel Synnaeve, Yossi Adi, Yaron Lipman
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α-flow: A unified framework for continuous-state discrete flow matching models
arXivCheng Cheng, Jiahao Li, Jianfei Fan, Ge Liu
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Fisher flow matching for generative modeling over discrete data
NeurIPS 2024Oscar Davis, Samuel Kessler, Mircea Petrache, Ismail Ceylan, Michael Bronstein, Joey Bose
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Generative flows on discrete state-spaces: Enabling multimodal flows with applications to protein co-design
arXivAndrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi Jaakkola
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Defog: Discrete flow matching for graph generation
arXivYiming Qin, Manuel Madeira, Dorina Thanou, Pascal Frossard
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Flow matching with general discrete paths: A kinetic-optimal perspective
ICLR 2025 OralNeta Shaul, Itai Gat, Marton Havasi, Daniel Severo, Anuroop Sriram, Peter Holderrieth, Brian Karrer, Yaron Lipman, Ricky T. Q. Chen
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Dirichlet flow matching with applications to DNA sequence design
ICML 2024Hannes Stark, Bowen Jing, Chenyu Wang, Gabriele Corso, Bonnie Berger, Regina Barzilay, Tommi Jaakkola
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Mixed continuous and categorical flow matching for 3D de novo molecule generation
arXivIan Dunn, David Ryan Koes
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Gumbel-softmax flow matching with straight-through guidance for controllable biological sequence generation
arXivSophia Tang, Yinuo Zhang, Alexander Tong, Pranam Chatterjee
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Fisher flow matching for generative modeling over discrete data
NeurIPS 2024Oscar Davis, Samuel Kessler, Mircea Petrache, Ismail Ceylan, Michael Bronstein, Joey Bose
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Dirichlet flow matching with applications to DNA sequence design
arXivHannes Stark, Bowen Jing, Chenyu Wang, Gabriele Corso, Bonnie Berger, Regina Barzilay, Tommi Jaakkola
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Gumbel-softmax flow matching with straight-through guidance for controllable biological sequence generation
arXivSophia Tang, Yinuo Zhang, Alexander Tong, Pranam Chatterjee
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Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design
arXivTong Chen, Yinuo Zhang, Sophia Tang, Pranam Chatterjee
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RNACG: A universal RNA sequence conditional generation model based on flow-matching
arXivLei Gao, Zhi John Lu
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RNAFlow: RNA structure & sequence design via inverse folding-based flow matching
ICML 2024Divya Nori, Wengong Jin
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RiboGen: RNA sequence and structure co-generation with equivariant multiflow
arXivDaniel Rubin, Alex de Souza Costa, Manvitha Ponnapati, Joseph Jacobson
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RiboFlow: Conditional De Novo RNA Co-Design via Synergistic Flow Matching
NeurIPS 2025Runze Ma, Zhongyue Zhang, Zichen Wang, Chenqing Hua, Jiahua Rao, Zhuomin Zhou, Shuangjia Zheng
, 2025
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GeNOT: Entropic (Gromov) Wasserstein flow matching with applications to single-cell genomics
NeurIPS 2024Dominik Klein, Théo Uscidda, Fabian Theis, Marco Cuturi
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CellFlow: A generative flow-based model for single-cell count data
ICLR 2024 WorkshopAntonio Palma, Till Richter, Hao Zhang, Andrea Dittadi, Fabian J. Theis
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Multi-modal and multi-attribute generation of single cells with CFGen
ICLR 2025Antonio Palma, Till Richter, Hao Zhang, Martin Lubetzki, Alexander Tong, Andrea Dittadi, Fabian J. Theis
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IgFlow: Flow Matching for de novo Antibody Design
NeurIPS 2024S. Nagaraj, A. Shanehsazzadeh, H. Park, J. King, S. Levine
, 2024
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DyAB: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen
AAAI 2025C. Tan, Y. Zhang, Z. Gao, Y. Huang, H. Lin, L. Wu, F. Wu, M. Blanchette, S. Z. Li
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Improving Molecular Graph Generation with Flow Matching and Optimal Transport
arXiv 2024X. Hou, T. Zhu, M. Ren, D. Bu, X. Gao, C. Zhang, S. Sun
, 2024
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Variational Flow Matching for Graph Generation
NeurIPS 2024F. Eijkelboom, G. Bartosh, C. A. Naesseth, M. Welling, J. van de Meent
, 2024
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DeFoG: Discrete Flow Matching for Graph Generation
ICML 2025Y. Qin, M. Madeira, D. Thanou, P. Frossard
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EquiFlow: Equivariant Conditional Flow Matching with Optimal Transport for 3D Molecular Conformation Prediction
arXiv 2024Q. Tian, Y. Xu, Y. Yang, Z. Wang, Z. Liu, P. Yan, X. Li
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Applications of Modular Co-Design for de novo 3D Molecule Generation
NeurIPS 2024 WorkshopD. Reidenbach, F. Nikitin, O. Isayev, S. G. Paliwal
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Controlled Generation with Equivariant Variational Flow Matching
ICML 2025F. Eijkelboom, H. Zimmermann, S. Vadgama, E. Bekkers, M. Welling, C. A. Naesseth, J.-W. van de Meent
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ET-Flow: Equivariant Transformer Flow for Molecular Conformer Generation
arXiv 2024M. Hassan, N. Shenoy, J. Lee, H. Stark, S. Thaler, D. Beaini
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Accelerating 3D Molecule Generation via Jointly Geometric Optimal Transport (GOAT)
ICLR 2025H. Hong, W. Lin, K. C. Tan
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SemlaFlow—Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matching
AISTATS 2025R. Irwin, A. Tibo, J. P. Janet, S. Olsson
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Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow
ICML 2025Z. Cao, M. Geiger, A. D. S. Costa, D. Reidenbach, K. Kreis, T. Geffner, F. Pellegrini, G. Zhou, E. Kucukbenli
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ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation
NeurIPS 2024M. Hassan, N. Shenoy, J. Lee, H. Stärk, S. Thaler, D. Beaini
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3D Structure Prediction of Atomic Systems with Flow-based Direct Preference Optimization
NeurIPS 2024R. Jiao, X. Kong, W. Huang, Y. Liu
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Extended Flow Matching: A Method of Conditional Generation with Generalized Continuity Equation
arXiv 2024N. Isobe, M. Koyama, K. Hayashi, K. Fukumizu
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Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation
arXiv 2024I. Dunn, D. R. Koes
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Energy-Based Flow Matching for Generating 3D Molecular Structure
ICML 2025W. Zhou, C. I. Sprague, V. Viliuga, M. Tadiello, A. Elofsson, H. Azizpour
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Training-Free Guided Flow Matching with Optimal Control
arXiv 2024L. Wang, C. Cheng, Y. Liao, Y. Qu, G. Liu
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PropMolFlow: property-guided molecule generation with geometry-complete flow matching
Nature Computational Science 2026Cheng Zeng, Jirui Jin, Connor Ambrose, George Karypis, Mark Transtrum, Ellad B. Tadmor, Richard G. Hennig, Adrian Roitberg, Stefano Martiniani, Mingjie Liu
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FlexSBDD: Structure-Based Drug Design with Flexible Protein Modeling
NeurIPS 2024Z. Zhang, M. Wang, Q. Liu
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Geometric Representation Condition Improves Equivariant Molecule Generation
arXiv 2024Z. Li, C. Zhou, X. Wang, X. Peng, M. Zhang
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Improving Structural Plausibility in 3D Molecule Generation via Property-Conditioned Training with Distorted Molecules
bioRxiv 2024L. Vost, V. Chenthamarakshan, P. Das, C. M. Deane
, 2024
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Stiefel Flow Matching for Moment-Constrained Structure Elucidation
arXiv 2024A. H. Cheng, A. Lo, K. L. K. Lee, S. Miret, A. Aspuru-Guzik
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Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization
NeurIPS 2025Noémie Bergues, Arthur Carré, Paul Join-Lambert, Brice Hoffmann, Arnaud Blondel, Hamza Tajmouati
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Prior-Guided Flow Matching for Target-Aware Molecule Design with Learnable Atom Number
NeurIPS 2025Jingyuan Zhou, Hao Qian, Shikui Tu, Lei Xu
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Fast Protein Backbone Generation with SE(3) Flow Matching
arXiv 2023J. Yim, A. Campbell, A. Y. Foong, M. Gastegger, J. Jiménez-Luna, S. Lewis, V. G. Satorras, B. S. Veeling, R. Barzilay, T. Jaakkola, F. Noé
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SE(3)-Stochastic Flow Matching for Protein Backbone Generation
ICLR 2024A. J. Bose, T. Akhound-Sadegh, G. Huguet, K. Fatras, J. Rector-Brooks, C.-H. Liu, A. C. Nica, M. Korablyov, M. Bronstein, A. Tong
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Robust and Reliable de novo Protein Design: A Flow-Matching-Based Protein Generative Model Achieves Remarkably High Success Rates
bioRxiv 2025J. Yan, Z. Cui, W. Yan, Y. Chen, M. Pu, S. Li, S. Ye
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Generating Highly Designable Proteins with Geometric Algebra Flow Matching
NeurIPS 2024S. Wagner, L. Seute, V. Viliuga, N. Wolf, F. Gräter, J. Stühmer
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Sequence-Augmented SE(3)-Flow Matching for Conditional Protein Backbone Generation
NeurIPS 2024G. Huguet, J. Vuckovic, K. Fatras, E. Thibodeau-Laufer, P. Lemos, R. Islam, C.-H. Liu, J. Rector-Brooks, T. Akhound-Sadegh, M. Bronstein, A. Tong, A. J. Bose
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Proteina: Scaling Flow-Based Protein Structure Generative Models
ICLR 2025 OralT. Geffner, K. Didi, Z. Zhang, D. Reidenbach, Z. Cao, J. Yim, M. Geiger, C. Dallago, E. Kucukbenli, A. Vahdat, et al.
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ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids
ICLR 2025H. Stark, B. Jing, T. Geffner, J. Yim, T. Jaakkola, A. Vahdat, K. Kreis
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Atom-level enzyme active site scaffolding using RFdiffusion2
Nature Methods 2025Woody Ahern, Jason Yim, Doug Tischer, Saman Salike, Seth M. Woodbury, Donghyo Kim, Indrek Kalvet, Yakov Kipnis, Brian Coventry, Han Raut Altae-Tran, Magnus S. Bauer, Regina Barzilay, Tommi S. Jaakkola, Rohith Krishna, David Baker
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Co-Design Protein Sequence and Structure in Discrete Space via Generative Flow (CoFlow)
Bioinformatics 2025S. Yang, L. Ju, P. Cheng, J. Zhou, Y. Cai, D. Feng
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An All-Atom Generative Model for Designing Protein Complexes (APM)
ICML 2025R. Chen, D. Xue, X. Zhou, Z. Zheng, X. Zeng, Q. Gu
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Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design (Multiflow)
ICML 2024A. Campbell, J. Yim, R. Barzilay, T. Rainforth, T. Jaakkola
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Improved Motif-Scaffolding with SE(3) Flow Matching
arXiv 2024Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola
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EVA: Geometric Inverse Design for Fast Protein Motif-Scaffolding with Coupled Flow
ICLR 2025Yufei Huang, Yunshu Liu, Lirong Wu, Haitao Lin, Cheng Tan, Odin Zhang, Zhangyang Gao, Siyuan Li, Zicheng Liu, Yunfan Liu, Tailin Wu, Stan Z. Li
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Design of Ligand-Binding Proteins with Atomic Flow Matching
arXiv 2024Junqi Liu, Shaoning Li, Chence Shi, Zhi Yang, Jian Tang
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Harmonic Self-Conditioned Flow Matching for Joint Multi-Ligand Docking and Binding Site Design
ICML 2024Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi Jaakkola
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Generalized Protein Pocket Generation with Prior-Informed Flow Matching (PocketFlow)
NeurIPS 2024Zaixi Zhang, Marinka Zitnik, Qi Liu
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FlowBack: A Generalized Flow-Matching Approach for Biomolecular Backmapping
JCIM 2025Michael S. Jones, Smayan Khanna, Andrew L. Ferguson
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AlphaFold Meets Flow Matching for Generating Protein Ensembles
ICML 2024B. Jing, B. Berger, T. Jaakkola
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P2DFlow: A Protein Ensemble Generative Model with SE(3) Flow Matching
JCTC 2025Y. Jin, Q. Huang, Z. Song, M. Zheng, D. Teng, Q. Shi
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FlowPacker: Protein Side-Chain Packing with Torsional Flow Matching
Bioinformatics 2025J. S. Lee, P. M. Kim
, 2025
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EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
ICLR 2024Y.-L. Liao, B. Wood, A. Das, T. Smidt
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Full-Atom Peptide Design based on Multi-modal Flow Matching
ICML 2024J. Li, C. Cheng, Z. Wu, R. Guo, S. Luo, Z. Ren, J. Peng, J. Ma
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FlowPacker: Protein Side-Chain Packing with Torsional Flow Matching
Bioinformatics 2025J. S. Lee, P. M. Kim
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EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
arXiv 2023Y.-L. Liao, B. Wood, A. Das, T. Smidt
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Full-Atom Peptide Design based on Multi-modal Flow Matching
ICML 2024J. Li, C. Cheng, Z. Wu, R. Guo, S. Luo, Z. Ren, J. Peng, J. Ma
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Harmonic Self-Conditioned Flow Matching for Joint Multi-Ligand Docking and Binding Site Design
ICML 2024Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi Jaakkola
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FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction
Bioinformatics 2025Alex Morehead, Jianlin Cheng
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ForceFM: Enhancing Protein-Ligand Predictions through Force-Guided Flow Matching
NeurIPS 2025Huanlei Guo, Song Liu, Bingyi Jing
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P2DFlow: A Protein Ensemble Generative Model with SE(3) Flow Matching
JCTC 2025Y. Jin, Q. Huang, Z. Song, M. Zheng, D. Teng, Q. Shi
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Full-Atom Peptide Design based on Multi-modal Flow Matching
ICML 2024J. Li, C. Cheng, Z. Wu, R. Guo, S. Luo, Z. Ren, J. Peng, J. Ma
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PPFlow: Target-Aware Peptide Design with Torsional Flow Matching
bioRxiv 2024H. Lin, O. Zhang, H. Zhao, D. Jiang, L. Wu, Z. Liu, Y. Huang, S. Z. Li
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Non-Linear Flow Matching for Full-Atom Peptide Design
arXiv 2025D. Huang, S. Tu
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ProtFlow: Fast Protein Sequence Design via Flow Matching on Compressed Protein Language Model Embeddings
arXiv 2025Z. Kong, Y. Zhu, Y. Xu, H. Zhou, M. Yin, J. Wu, H. Xu, C.-Y. Hsieh, T. Hou, J. Wu
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GeNOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics
NeurIPS 2024Dominik Klein, Théo Uscidda, Fabian Theis, Marco Cuturi
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CellFlux: Simulating Cellular Morphology Changes via Flow Matching
ICML 2025Yuhui Zhang, Yuchang Su, Chenyu Wang, Tianhong Li, Zoe Wefers, Jeffrey Nirschl, James Burgess, Daisy Ding, Alejandro Lozano, Emma Lundberg, Serena Yeung-Levy
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Metric Flow Matching for Smooth Interpolations on the Data Manifold
NeurIPS 2024Kacper Kapuśniak, Peter Potaptchik, Teodora Reu, Leo Zhang, Alexander Tong, Michael Bronstein, Avishek Joey Bose, Francesco Di Giovanni
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Diversified Flow Matching with Translation Identifiability
ICML 2025Sagar Shrestha, Xiao Fu
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FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms
arXiv 2024Lea Bogensperger, Dominik Narnhofer, Alexander Falk, Konrad Schindler, Thomas Pock
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Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and Quality
arXiv 2025Milad Yazdani, Yasamin Medghalchi, Pooria Ashrafian, Ilker Hacihaliloglu, Dena Shahriari
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Multimodal Straight Flow Matching for Accelerated MR Imaging
Computers in Biology and Medicine 2024Daikun Zhang, Qiuyi Han, Yuzhu Xiong, Hongwei Du
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Wasserstein Flow Matching: Generative Modeling over Families of Distributions
ICML 2025Doron Haviv, Aram-Alexandre Pooladian, Dana Pe’er, Brandon Amos
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Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching
ICML 2025Tinglin Huang, Tianyu Liu, Mehrtash Babadi, Wengong Jin, Rex Ying
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Stream-level Flow Matching with Gaussian Processes
ICML 2025Ganchao Wei, Li Ma
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Flow Matching for Few-Trial Neural Adaptation with Stable Latent Dynamics
ICML 2025Puli Wang, Yu Qi, Yueming Wang, Gang Pan
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Riemannian Flow Matching for Brain Connectivity Matrices via Pullback Geometry
NeurIPS 2025Antoine Collas, Ce Ju, Nicolas Salvy, Bertrand Thirion
Table 5 and 6 in our survey paper Flow Matching Meets Biology and Life Science: A Survey.
Please cite the following paper if you find the resource helpful for your research:
@article{li2026flow,
title={Flow matching meets biology and life science: a survey},
author={Li, Zihao and Zeng, Zhichen and Lin, Xiao and Fang, Feihao and Qu, Yanru and Xu, Zhe and Liu, Zhining and Ning, Xuying and Wei, Tianxin and Liu, Ge and others},
journal={npj Artificial Intelligence},
volume={2},
number={1},
pages={17},
year={2026},
publisher={Nature Publishing Group UK London}
}This project is licensed under the MIT License - see the LICENSE file for details.