A REVIEW ON EXPLAINABLE AI IN ProTEIN FUNCTION

A REVIEW ON EXPLAINABLE AI IN ProTEIN FUNCTION | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 5 June 2025 V1 Latest version Share on A REVIEW ON EXPLAINABLE AI IN ProTEIN FUNCTION Author : Aastha Katiyar 0009-0008-1833-5681 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174912536.68729526/v1 400 views 134 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The fast pace of incorporation of artificial intelligence (AI) in bioinformatics, though providing immense advantages, is frequently hindered by the fundamental opaqueness of ”black-box” models. The lack of transparency in predictive models is a significant drawback to the field, preventing the identification of underlying mechanisms that govern predictions, the deficiency of interpretability in these ”black-box” models greatly restricts credibility & applicability for protein design and optimization. This non-transparency prevents trust and slows down the creation of actionable results, especially in key areas like drug discovery, enzyme engineering and precision medicine. In response to this challenge, this review presents an overview on Explainable Artificial Intelligence (XAI) methods and their use in bioinformatics with emphasis on how such methods facilitate the gap between high predictive accuracy and biological interpretability. XAI is not just about understanding, but about accelerating discovery, building trust, and enabling responsible AI in biology. and unlocking a next generation of insight into protein function XAI is transforming protein function prediction from a black-box task into transparent results, bridging the gap between prediction and biological causality. We explored how explainable AI (XAI) is used in bioinformatics, especially for analyzing biological data like proteins and sequence-structure-function relationships. This review synthesizes insights from the growing significance of XAI in improving transparency, interpretability and accountability in protein function determination. Furthermore, will explore evolution around various machine learning and deep learning techniques in protein function prediction, emphasizing transformative role of XAI in enhancing interpretability and guiding future research. Although recognizing current challenges like data sparsity, model generalizability, and domain-specific interpretability gaps. we promote the creation of standardized benchmarks, hybrid knowledge-AI frameworks, and ethical guidelines to guarantee the reproducibility and fairness of predictive models. Finally, this review highlights the revolutionizing promise of XAI in bringing computational predictions closer to biological insight and thus opening the way toward scalable and reliable tools for functional genomics and personalized medicine alongside, it covers the practical application, challenging aspects of XAI that governs the process further. This helps researchers to navigate through the XAI methods and their implications. Supplementary Material File (review paper on bridging ai and bioinformatics.docx) Download 548.35 KB Information & Authors Information Version history V1 Version 1 05 June 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords bioinformatics deep learning explainable ai (xai) explanability interpretability machine learning protein function transparency Authors Affiliations Aastha Katiyar 0009-0008-1833-5681 [email protected] SAGE University View all articles by this author Metrics & Citations Metrics Article Usage 400 views 134 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Aastha Katiyar. A REVIEW ON EXPLAINABLE AI IN ProTEIN FUNCTION. Authorea . 05 June 2025. DOI: https://doi.org/10.22541/au.174912536.68729526/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 . 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