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Machine Learning Approaches to Rational Drug Design

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Computer-Aided Drug Design

Abstract

Pharmaceutical industries are multibillionaire setups with a diligent team of scientists, researchers, technical manpower, and investors. A major concern of such industries is to always curtail the time and cost factor associated with them. Bioinformatics involving machine learning (ML) methods have come to the forefront to address this problem. The predictive and statistical efficacy of ML methodologies has even proven to propose better leads than a wet lab pipeline. This chapter aims to give a brief overview of underlying principles of mainly GAs and ANNs as popular ML algorithms and deeper insight into their robust applications in the field of modern day drug design. It also attempts to share the future prospects of such ML techniques and their limitations with possible solutions hereafter.

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Competing Interest

The authors declare that there are no competing interests.

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Authors and Affiliations

  1. Department of Bioengineering, Integral University, Lucknow, India

    Salman Akhtar, M. Kalim A. Khan & Khwaja Osama

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  1. Salman Akhtar
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  2. M. Kalim A. Khan
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  3. Khwaja Osama
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Editors and Affiliations

  1. Department of Biotechnology, Institute of Biosciences and Biotechnology, Chhatrapati Shahu Ji Maharaj University, Kanpur, Uttar Pradesh, India

    Dev Bukhsh Singh

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Akhtar, S., Khan, M.K.A., Osama, K. (2020). Machine Learning Approaches to Rational Drug Design. In: Singh, D.B. (eds) Computer-Aided Drug Design. Springer, Singapore. https://doi.org/10.1007/978-981-15-6815-2_12

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