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Data-Driven and Artificial Intelligence Approaches for System-Wide Prediction of the Drugable Proteome to Drug Discovery in Farm Animals

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Sustainable Agriculture Reviews

Part of the book series: Sustainable Agriculture Reviews ((SARV,volume 62))

Abstract

A druggable proteome is of global interest for the drug development process. Growing livestock for food relies strongly on pharmacologically active chemicals, or medicines, in modern agricultural practice. The use of medications in food animals is critical for the welfare and well-being of the animals and the industry’s economics. Drug consumption, on the other hand, is linked to adverse impacts on public well-being. Therefore it is crucial to test the drug comprehensively before its use in agricultural practice. The identification of druggable proteome requires a non-trivial amount of resources and time, therefore, artificial intelligence and machine learning have emerged as invaluable tools for drawing meaningful perspectives and improving decision making in drug research. In this respect, the overall drug discovery process necessitates a long-term transition and lowers production costs. Artificial intelligence is a promising alternative for dysfunctional drug discovery and development. This chapter outlines the applications of artificial intelligence and machine learning innovations to many other techniques in drug development, such as target recognition, compound screening, lead generation and optimization, drug reaction and synergy prediction, de novo drug design, and drug repurposing.

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Abbreviations

SMILES:

Simplified Molecular Input Line Entry System

DTA:

Drug-Target binding Affinity

DTI:

Drug-Target protein Interaction

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Authors and Affiliations

  1. Independent Researcher, Chennai, Tamil Nadu, India

    A. S. Ben Geoffrey

  2. CBIL Lab, Indian Institute of Technology Ropar Rupnagar, Bara Phool, Punjab, India

    Jitender Singh Virk

  3. Nanosafety Lab, Division of Biochemistry, ICAR-NDRI, Karnal, Haryana, India

    Deepti Mittal

  4. Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia

    Gurjeet Kaur

  5. Mark Wainwright Analytical Centre, Bioanalytical Mass Spectrometry Facility, University of New South Wales, Sydney, NSW, Australia

    Gurjeet Kaur

  6. Steno Diabetes Center Copenhagen, Herlev, Denmark

    Gurjeet Kaur

  7. Cell Biology and Proteomics Lab, Animal Biotechnology Center, ICAR-NDRI, Karnal, Haryana, India

    Syed Azmal Ali

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  1. A. S. Ben Geoffrey
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  2. Jitender Singh Virk
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  3. Deepti Mittal
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  4. Gurjeet Kaur
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Editors and Affiliations

  1. Research Centre, KBK Multispecialty Hospitals, Hyderabad, Telangana, India

    Vinod Kumar Yata

  2. ICAR-Central Institute for Research on Cattle (CIRC), Meerut, Uttar Pradesh, India

    Ashok Kumar Mohanty

  3. Xi’an Jiaotong University, Xian, China

    Eric Lichtfouse

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Ben Geoffrey, A.S., Virk, J.S., Mittal, D., Kaur, G., Ali, S.A. (2024). Data-Driven and Artificial Intelligence Approaches for System-Wide Prediction of the Drugable Proteome to Drug Discovery in Farm Animals. In: Kumar Yata, V., Mohanty, A.K., Lichtfouse, E. (eds) Sustainable Agriculture Reviews . Sustainable Agriculture Reviews, vol 62. Springer, Cham. https://doi.org/10.1007/978-3-031-54372-2_5

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