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Applications of Big Data and AI-Driven Technologies in CADD (Computer-Aided Drug Design)

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Computational Drug Discovery and Design

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

Abstract

In the field of computer-aided drug design (CADD), there has been dramatic progress in the development of big data and AI-driven methodologies. The expensive and time-consuming process of drug design is related to biomedical complexity. CADD can be used to apply effective and efficient strategies to overcome obstacles in the field of drug design in order to properly design and develop a new medicine. To prepare the raw data for consistent and repeatable applications of big data and AI methodologies, data pre-processing methods are introduced. Big data and AI technologies can be used to develop drugs in areas including predicting absorption, distribution, metabolism, excretion, and toxicity properties as well as finding binding sites in target proteins and conducting structure-based virtual screenings. The accurate and thorough analysis of large amounts of biomedical data as well as the design of prediction models in the area of drug design is made possible by data pre-processing and applications of big data and AI skills. In the biomedical big data era, knowledge on the biological, chemical, or pharmacological structures of biomedical entities relevant to drug design should be analyzed with significant big data and AI approaches.

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Acknowledgments

This article is financially supported by the 2023 College of Public Policy at Korea University.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea

    Seongmin Seo

  2. Department of Big Data Science, College of Public Policy, Korea University, Sejong, Republic of Korea

    Jai Woo Lee

Authors
  1. Seongmin Seo
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  2. Jai Woo Lee
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Corresponding author

Correspondence to Jai Woo Lee .

Editor information

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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Seo, S., Lee, J.W. (2024). Applications of Big Data and AI-Driven Technologies in CADD (Computer-Aided Drug Design). 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_16

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  • DOI: https://doi.org/10.1007/978-1-0716-3441-7_16

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3440-0

  • Online ISBN: 978-1-0716-3441-7

  • eBook Packages: Springer Protocols

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