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AI-Driven Enhancements in Drug Screening and Optimization

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Computational Drug Discovery and Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2714))

Abstract

The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline.

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Funding

S.P. and D.B.A. were supported by an Investigator Grant from the National Health and Medical

Research Council (NHMRC) of Australia (GNT1174405 to D.B.A.). This research was supported in part by the Victorian Government’s Operational Infrastructure Support Program.

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Authors and Affiliations

  1. School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia

    Adam Serghini, Stephanie Portelli & David B. Ascher

  2. Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia

    Adam Serghini & David B. Ascher

Authors
  1. Adam Serghini
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  2. Stephanie Portelli
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  3. David B. Ascher
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Corresponding authors

Correspondence to Stephanie Portelli or David B. Ascher .

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Editors and Affiliations

  1. Department of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, Karnataka, India

    Mohini Gore

  2. Department of Botany, Rajaram College Kolhapur, Kolhapur, Maharashtra, India

    Umesh B. Jagtap

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© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Serghini, A., Portelli, S., Ascher, D.B. (2024). AI-Driven Enhancements in Drug Screening and Optimization. In: Gore, M., Jagtap, U.B. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 2714. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3441-7_15

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  • DOI: https://doi.org/10.1007/978-1-0716-3441-7_15

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3440-0

  • Online ISBN: 978-1-0716-3441-7

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