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    Review of 'Machine Learning and Artificial Intelligence in drug repurposing – challenges and perspectives'

    Machine Learning and Artificial Intelligence in drug repurposing – challenges and perspectivesCrossref
    All in all, a good summary of ML techniques in drug repurposing
    Average rating:
        Rated 4 of 5.
    Level of importance:
        Rated 4 of 5.
    Level of validity:
        Rated 4 of 5.
    Level of completeness:
        Rated 4 of 5.
    Level of comprehensibility:
        Rated 4 of 5.
    Competing interests:
    I am a co-inventor on WO/2021/239623 / US 11,480,583 and WO/2023/204698. I am a consortium member of REPO-TRIAL which conduts the REPO-STROKE and REPO-HFpFF clinical trials.

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    • Abstract: found
    • Article: found
    Is Open Access

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

    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.

      Review 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.

      Bioinformatics & Computational biology,Artificial intelligence,Pharmacology & Pharmaceutical medicine
      artificial intelligence,drug repurposing,machine learning,neural networks

      Review text

      INTRODUCTION - 3rd paragraph: "(...) aims to reduce the cost and time of development,
      additionally seeing a much lower rate of clinical trial failure. Drug repurposing thus has the potential to dramatically decrease the time safe therapies may take to reach patients in need of a treatment[13]."
      It should be mentioned that savings in time-to-market, cost, and clinical trial success rates are realized only in the early stages of (re)development, which are the fastest and cheapest already. The costs and timelines for the Phase III program (the most expensive part by far)  are unchanged.

      Section by Ziaurrehman Tanoli:
      Paragraph #4: "In cross-validation, the XGBoost prediction algorithm within RepurposeDrugs demonstrated a significant correlation of 0.75 for single drug-disease associations and 0.56 for drug combinations."
      It would be useful to briefly describe what "cross-validation" means in this context. Also, has RepurposeDrugs been validated against real-life data, i.e., to which extent can it retrospectively predict actual drug repurposing successes?

      No comments on the sections by Lucía Prieto Santamaría and Judith Bernett / Markus List.

      The section by Adrian Freeman does not add additional perspectives to the previous sections in its present form, and should be expanded with more concrete content.



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