Flow matching for generative modeling in bioinformatics and computational biology

Flow matching for generative modeling in bioinformatics and computational biology | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 3 December 2025 V4 Latest version Share on Flow matching for generative modeling in bioinformatics and computational biology Authors : Alex Morehead 0000-0002-0586-6191 [email protected] , Lazar Atanackovic , Akshata Hegde , Yanli Wang , Frimpong Boadu , Joel Selvaraj , Alexander Tong , Aditi Krishnapriyan , and Jianlin Cheng Authors Info & Affiliations https://doi.org/10.22541/au.175382408.89466370/v4 2487 views 1568 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Numerous problems in bioinformatics and computational biology can be framed as a task of learning a mapping from one state of a biological system to another relevant state or to explore novel data points across biologically constrained spaces. However, manually deriving such mappings, e.g., to transform cells in a diseased state back into a healthy state, or extrapolating from existing datasets to create new data, is often nontrivial and can require extraordinary domain expertise and resources. Fortunately, the field of generative artificial intelligence (AI) has introduced a new training paradigm referred to as (conditional) flow matching, which has emerged as a promising solution to this problem, with broad applicability in computer vision, natural language processing, and the physical and life sciences. Flow matching is a powerful and principled, data-driven framework for efficiently learning a mapping between arbitrary pairs of high-dimensional data distributions, making it well-suited for addressing problems in molecular and cell biology. In this Review, we characterize the theoretical foundations of flow matching and its applications in biomolecular modeling for proteins, DNA/RNA, small molecules, and their interactions, as well as its uses in single/multi-cellular modeling for cell phenotyping and imaging, each contributing towards the development of an AI-based virtual cell. Lastly, this review highlights open-source flow matching methods and discusses future directions in flow-based generative modeling for bioinformatics and computational biology. Supplementary Material File (_flow_matching_for_generative_modeling_in_bioinformatics_and_computational_biology.pdf) Download 2.01 MB Information & Authors Information Version history V1 Version 1 29 July 2025 V2 Version 2 29 August 2025 V3 Version 3 22 September 2025 V4 Version 4 03 December 2025 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords bioinformatics computational biology flow matching generative ai review Authors Affiliations Alex Morehead 0000-0002-0586-6191 [email protected] Lawrence Berkeley National Laboratory View all articles by this author Lazar Atanackovic Electrical & Computer Engineering, Vector Institute, University of Toronto View all articles by this author Akshata Hegde Electrical Engineering & Computer Science, NextGen Precision Health, University of Missouri View all articles by this author Yanli Wang Electrical Engineering & Computer Science, NextGen Precision Health, University of Missouri View all articles by this author Frimpong Boadu Electrical Engineering & Computer Science, NextGen Precision Health, University of Missouri View all articles by this author Joel Selvaraj Electrical Engineering & Computer Science, NextGen Precision Health, University of Missouri View all articles by this author Alexander Tong Mila -Quebec AI Institute, Université de Montréal View all articles by this author Aditi Krishnapriyan Lawrence Berkeley National Laboratory Computer Science & Chemical Engineering, University of California-Berkeley View all articles by this author Jianlin Cheng Electrical Engineering & Computer Science, NextGen Precision Health, University of Missouri View all articles by this author Funding Information U.S. Department of Energy DDR-ERCAP 0034574 Alex Morehead U.S. Department of Energy DDR-ERCAP 003457 Alex Morehead Metrics & Citations Metrics Article Usage 2487 views 1568 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Alex Morehead, Lazar Atanackovic, Akshata Hegde, et al. Flow matching for generative modeling in bioinformatics and computational biology. Authorea . 03 December 2025. DOI: https://doi.org/10.22541/au.175382408.89466370/v4 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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