AI for Scientific Discovery: Automating Hypothesis Generation

AI for Scientific Discovery: Automating Hypothesis Generation | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 20 June 2025 V1 Latest version Share on AI for Scientific Discovery: Automating Hypothesis Generation Author : Andrei McCall 0009-0008-8268-507X [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175044376.61922933/v1 990 views 283 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The accelerating pace of scientific advancement has underscored the need for innovative tools to assist researchers in navigating the ever-growing volumes of data and literature. Artificial Intelligence (AI) has emerged as a transformative force in this domain, particularly in automating the process of hypothesis generation-a core component of scientific discovery. This paper explores the current capabilities and methodologies of AI systems designed to formulate plausible, testable scientific hypotheses by synthesizing patterns, identifying anomalies, and integrating multi-domain knowledge. Emphasis is placed on machine learning algorithms, knowledge graphs, and natural language processing tools that mimic cognitive reasoning. Through case studies in biomedical and material science fields, we demonstrate the potential of these systems to accelerate discovery and enhance research efficiency. The study concludes by addressing the epistemological and ethical implications of delegating aspects of scientific reasoning to machines and offers a roadmap for future advancements in hybrid human-AI discovery systems. Supplementary Material File (ai for scientific discovery.pdf) Download 334.49 KB Information & Authors Information Version history V1 Version 1 20 June 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords artificial intelligence automation cognitive computing hypothesis generation knowledge graphs machine learning natural language processing scientific discovery Authors Affiliations Andrei McCall 0009-0008-8268-507X [email protected] Ladoke Akintola University of Technology View all articles by this author Metrics & Citations Metrics Article Usage 990 views 283 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Andrei McCall. AI for Scientific Discovery: Automating Hypothesis Generation. Authorea . 20 June 2025. DOI: https://doi.org/10.22541/au.175044376.61922933/v1 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 . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Russell C. Rockne, Morten Andersen, Alexander R. A. Anderson, David Basanta, Angela Bentivegna, Sebastien Benzekry, Sergio Branciamore, Sarah C. Brüningk, Martina Conte, Farnoush Farahpour, Aleksandra Karolak, Alvaro Köhn-Luque, Guillermo Lorenzo, Babgen Manookian, Andrei S. Rodin, Lara Schmalenstroer, Juan Soler, Cristian Tomasetti, Konstancja Urbaniak, The future of mathematical oncology in the age of AI, npj Systems Biology and Applications, 12 , 1, (2026). https://doi.org/10.1038/s41540-026-00656-9 Crossref Zarrindokht Emami-Karvani, Anahita Jenab, Kouroush Jenab, Microbial Minds and Machine Models: Harnessing Artificial Intelligence to Revolutionize Microbiological Research, BiotechIntellect, 2 , 1, (2025). https://doi.org/10.61882/BiotechIntellect.2.1.7 Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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