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      Drug Target Prediction and Repositioning using Machine Learning Integration of Network-based, Pathway Enrichment-based and Disease Enrichment-based Analyses

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      RExPO23 Conference
      REPO4EU
      RExPO23
      25-26 October 2023
      target identification, target prioritization, drug repurposing, Machine Learning, Network Analysis, mechanism of action, disease mechanism reconstruction, Artificial Intelligence
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            Abstract

            Conventional drug discovery can involve costly and lengthy studies and choosing the “wrong” target can lead to failures in the clinic, waste of time and of millions of dollars. Selecting a novel target based on a sound biological rationale of its mechanistic role in a disease and on the knowledge of which signalling pathways the target influences, de-risks projects and increases the success rates of drug development. Systems biology and Artificial intelligence (AI) algorithms can evaluate large data sets quickly and extract insightful information to help researchers make informed decisions about which drug candidates to pursue.Clarivate’s pipeline for Drug Target Prediction and Repositioning uses a disease-centric approach: an indication of interest is selected, along with its known biomarkers and targets and, when available, gene expression signatures related to that indication. We analyse these data using network-based, pathway enrichment-based and disease similarity-based methods, leveraging our consortia-built algorithms (Computational Biology for Drug Discovery, CBDD). The output data from the three methods are integrated using Machine Learning to obtain a list of prioritized targets. Top prioritized targets come with a report providing supporting evidence and details about the drugs that modulate its activity, such as its mechanisms of action, the pathways in which it is involved, the highest clinical phase the drug ever reached in a clinical trial, or whether there are known adverse events associated to the drug.The power of our workflow lies in the insightful information extracted from our high-quality manually curated Cortellis Drug Discovery Intelligence (CDDI TM) and MetaBase TM databases, together with related content across Clarivate’s Cortellis preclinical and clinical intelligence tools, key data from publicly available sources, and manual mechanism reconstruction by our experts.Our pipeline has been successfully applied for more than a decade (1, 2) to help Life Science researchers, pharmaceutical industry, Biotech companies and Non-profit organisations (3) prioritize indications and targets that can boost their preclinical research.

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            Author and article information

            Conference
            RExPO23 Conference
            REPO4EU
            29 September 2023
            Affiliations
            [1 ] Clarivate;
            Author notes
            Author information
            https://orcid.org/0000-0003-0694-1867
            https://orcid.org/0000-0001-5159-2518
            https://orcid.org/0000-0001-9182-0810
            https://orcid.org/0000-0001-8581-3876
            Article
            10.58647/REXPO.23000021.v1
            545526f2-a83b-40e1-b5d4-d3487acabb47

            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 .

            RExPO23
            2
            Stockholm, Sweden
            25-26 October 2023
            History
            : 29 September 2023
            Categories

            Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
            Bioinformatics & Computational biology,Life sciences
            target identification,target prioritization,drug repurposing,Machine Learning,Network Analysis,mechanism of action,disease mechanism reconstruction,Artificial Intelligence

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