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      Advancing Drug Discovery through Integrative Computational Models and AI Technologies

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            Abstract

            The intricate, costly, and time-intensive nature of traditional drug discovery processes hinders the development of novel pharmaceuticals. This research illustrates a comprehensive approach integrating advanced computational models, artificial intelligence (AI), and molecular biology techniques to streamline these processes and enhance pharmacological research. By transcending conventional static models, this project focuses on dynamic protein behavior, multiscale molecular dynamics, and Fully Connected Graph Neural Networks (FCGNN) employment for a more nuanced understanding of drug discovery mechanisms.

            This work addresses several critical limitations of current methodologies: the oversimplification of receptor-ligand interactions, the static representation of protein structures, and the neglect of electronic distribution changes in molecular interactions. By developing novel simulation techniques that emphasise protein dynamics, proposing a multiscale analysis for real-time molecular characteristic updates, and applying FCGNN for the comprehensive mapping of molecular effects, this research aims to provide profound insights into the mechanistic processes underlying drug-protein interactions.

            Ultimately, this research seeks to establish a new framework for biomedical research and pharmacology, where biological systems' dynamic and complex nature is accurately modelled and understood, leading to the discovery of more effective, tailored medical treatments.

            Content

            Author and article information

            Conference
            RExPO24 Conference
            REPO4EU
            9 April 2024
            Affiliations
            [1 ] SoftMining Srl, Salerno, Italy;
            [2 ] University of Salerno, Salerno, Italy ( https://ror.org/0192m2k53)
            Author notes
            Author information
            https://orcid.org/0000-0002-3102-1918
            https://orcid.org/0000-0002-1461-9301
            Article
            10.58647/REXPO.23000034.v1
            40dc7ea2-1b8f-488d-9e88-2faeb98e5d9d

            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 .

            RExPO24
            3
            Munich, Germany
            3-5 July 2024
            History
            : 9 April 2024
            Funding
            Funded by: funder-id http://dx.doi.org/10.13039/501100007065, Università degli Studi di Salerno;
            Categories

            All data generated or analysed during this study are included in this published article (and its supplementary information files).
            Computational chemistry & Modeling,Pharmaceutical chemistry,Artificial intelligence,Pharmacology & Pharmaceutical medicine
            Drug Discovery,Protein Dynamics,Pharmacology,Computational Chemistry,Molecular Dynamics,Graph Neural Networks,Artificial Intelligence

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