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      Cloudscreen: A “one-stop-shop” Platform for Drug Repurposing

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            Abstract

            Drug repurposing is a state-of-the-art pipeline that expands pharmaceutical research and development portfolia. Despite computational tools are rapidly advancing, they predominantly rely on the examination of textual (1D) data. Herein, we present Cloudscreen ®, a "one-stop-shop" platform for drug repurposing, which seamlessly integrates 3D (structural data through predictive protein-ligand modeling) and 1D data alongside state-of-the-art machine learning algorithms. Our platform leverages a knowledge graph database, curated from diverse sources, including publicly available repositories and databases coupled to in-house computations. We are harnessing the power of artificial intelligence to predict and assess the efficacy and safety of repurposed biomolecules for novel therapeutic indications while interrogating the human variome. Moreover, Cloudscreen ® expands such prediction capabilities based on AlphaFold models, ADMETox and pharmacogenomics (emphasis on missense variants). Cloudscreen ® is a powerful tool that results from the synergy of wet- and dry-lab validated datasets and hence, provides “one-stop-shop” predictive insights into the uncharted therapeutic possibilities for repurposed drugs.

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            Author and article information

            Conference
            RExPO23 Conference
            REPO4EU
            30 September 2023
            Affiliations
            [1 ] Cloudpharm Private Company, Athens, (Greece);
            [2 ] Institute of Chemical Biology, National Hellenic Research Foundation, Athens, (Greece);
            [3 ] Institute of Computer Science, FORTH, Crete (Greece);
            Author notes
            Author information
            https://orcid.org/0000-0002-4642-8163
            https://orcid.org/0000-0003-1563-2302
            https://orcid.org/0000-0001-7541-6885
            https://orcid.org/0000-0002-7811-3764
            https://orcid.org/0009-0004-2773-0916
            https://orcid.org/0000-0003-4276-0115
            https://orcid.org/0000-0001-9348-6078
            https://orcid.org/0000-0002-6263-4231
            Article
            10.58647/REXPO.23000029.v1
            9e5e9eee-2bc4-4773-9d4a-ba400f4b81b7

            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
            : 30 September 2023
            Categories

            The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
            Bioinformatics & Computational biology
            Drug Repurposing,Machine Learning,Chemoinformatics,Knowledge Graph

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