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Application of Artificial Intelligence and Machine Learning in Drug Discovery

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Artificial Intelligence in Drug Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2390))

Abstract

Machine Learning (ML) and Deep Learning (DL) are two subclasses of Artificial Intelligence (AI), that, in this day and age of big data provides significant opportunities to pharmaceutical discovery research and development by translating data to information and ultimately to knowledge. Machine Learning or AI is not really new but over last few years, application of better methods have emerged and they have been successfully applied for drug discovery and development. This chapter would provide an overview of these methods and how they have been applied across various work streams, e.g., generative chemistry, ADMET prediction, retrosynthetic analysis, etc. within drug discovery process. This chapter would also attempt to provide caution and pit falls in utilizing these methods blindly while summarizing challenges and limitations.

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Author information

Authors and Affiliations

  1. Novartis Institutes for BioMedical Research, Cambridge, MA, USA

    Rishi R. Gupta

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  1. Rishi R. Gupta
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Editors and Affiliations

  1. Computational Drug Discovery, Evotec (UK) Ltd., Abingdon, Oxfordshire, UK

    Alexander Heifetz

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Gupta, R.R. (2022). Application of Artificial Intelligence and Machine Learning in Drug Discovery. In: Heifetz, A. (eds) Artificial Intelligence in Drug Design. Methods in Molecular Biology, vol 2390. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1787-8_4

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  • DOI: https://doi.org/10.1007/978-1-0716-1787-8_4

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1786-1

  • Online ISBN: 978-1-0716-1787-8

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