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

      In review

            Revision notes

            1. Revision by Hermann Mucke:
              1. introduction: we added that costs for Phase III programs essentially remain the same.
              2. section by T.Ziaurrehman: the author added a description of what cross-validation is, in the context of the method, and also addressed how RepurposeDrugs was validated.
              3. section by A.Freeman: because of time limitation (additional paragraphs would need to go through legal review and the journal aims to get published by the end of June), we chose to leave the section as is and not add more content.
            2. Revision by Jordi Quintana:
              1. All comments were addressed, except the following sentence (M.List, J.Bernett): "The hope is that deep learning strategies will learn latent data structures" remains as is, as the models are supposed to learn the latent structure of the data.

            We want to thank the reviewers for taking the time to read and comment the review.


            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 addressing the existing challenges and providing 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.


            Author and article information

            27 May 2024
            [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

            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 .

            : 12 March 2024

            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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