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      Machine Learning and Artificial Intelligence in drug repurposing – challenges and perspectives

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            Abstract

            Artificial Intelligence (AI) and Machine Learning (ML) techniques play an increasingly crucial role in the field of drug repurposing.As the number of computational tools grows, it is essential to not only understand and carefully select the method itself, but also consider the input data used for building predictive models.

            This review aims to take a dive into current computational methods that leverage AI and ML to drive and accelerate compound and drug target selection, in addition to address the existing challenges and provide perspectives.While there is no doubt that AI and ML-based tools are transforming traditional approaches, especially with recent advancements in graph-based methods, they present novel challenges that require the human eye and expert intervention. The growing complexity of OMICs data further emphasizes the importance of data standardization and quality.

            Content

            Author and article information

            Journal
            DrugRxiv
            REPO4EU
            12 March 2024
            Affiliations
            [1 ] Discovery and Translational Sciences (DTS), Clarivate Analytics, Barcelona (Spain);
            [2 ] Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising (Germany);
            [3 ] Discovery Sciences, Research and Early Development, BioPharmaceuticals R&D, AstraZeneca, Cambridge (UK);
            [4 ] Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid (Spain), Centro de Tecnología Biomédica, Universidad Politécnica de Madrid (Spain);
            [5 ] Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki (Finland), BioICAWtech, Helsinki (Finland);
            Author notes
            Author information
            https://orcid.org/0000-0003-0694-1867
            https://orcid.org/0000-0001-5812-8013
            https://orcid.org/0009-0007-0413-5657
            https://orcid.org/0000-0002-0941-4168
            https://orcid.org/0000-0003-1545-3515
            https://orcid.org/0000-0003-2435-9862
            https://orcid.org/0000-0001-5159-2518
            Article
            10.58647/DRUGARXIV.PR000007.v1
            ee3bc77a-ec7a-4175-a871-4bc2dcf065f1

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            History
            : 12 March 2024
            Categories

            Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
            Bioinformatics & Computational biology,Artificial intelligence,Pharmacology & Pharmaceutical medicine
            machine learning,neural networks,artificial intelligence,drug repurposing

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