To keep up with the pace of rapid discoveries in biomedicine, a plethora of research endeavors had been directed toward Rational Drug Development that slowly gave way to Structure-Based Drug Design (SBDD). In the past few decades, SBDD played a stupendous role in identification of novel drug-like molecules that are capable of altering the structures and/or functions of the target macromolecules involved in different disease pathways and networks. Unfortunately, post-delivery drug failures due to adverse drug interactions have constrained the use of SBDD in biomedical applications. However, recent technological advancements, along with parallel surge in clinical research have led to the concomitant establishment of other powerful computational techniques such as Artificial Intelligence (AI) and Machine Learning (ML). These leading-edge tools with the ability to successfully predict side-effects of a wide range of drugs have eventually taken over the field of drug design. ML, a subset of AI, is a robust computational tool that is capable of data analysis and analytical model building with minimal human intervention. It is based on powerful algorithms that use huge sets of ‘training data’ as inputs to predict new output values, which improve iteratively through experience. In this review, along with a brief discussion on the evolution of the drug discovery process, we have focused on the methodologies pertaining to the technological advancements of machine learning. This review, with specific examples, also emphasises the tremendous contributions of ML in the field of biomedicine, while exploring possibilities for future developments.
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In-cell and in vitro study of protein folding has been significantly advanced by using biophysical approaches including FRET, NMR, CEST-MRI and optical tweezers. Read more about this in the review by Zhang et al. (pp. 29–38) of the special biophysics issue, ‘Emerging trends in biophysics and their applications in modern biology’, guest edited by Kakoli Bose (ACTREC, India).
Review Article|
April 07 2021
Remodelling structure-based drug design using machine learning
Shubhankar Dutta;
Shubhankar Dutta
1Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Kharghar, Navi Mumbai 410210, India
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Kakoli Bose
1Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Kharghar, Navi Mumbai 410210, India
2Homi Bhabha National Institute, Training School Complex, Anushaktinagar, Mumbai 400094, India
Correspondence: Kakoli Bose (kbose@actrec.gov.in)
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Publisher: Portland Press Ltd
Received:
January 28 2021
Revision Received:
March 17 2021
Accepted:
March 30 2021
Online ISSN: 2397-8562
Print ISSN: 2397-8554
© 2021 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society and the Royal Society of Biology
2021
Emerg Top Life Sci (2021) 5 (1): 13–27.
Article history
Received:
January 28 2021
Revision Received:
March 17 2021
Accepted:
March 30 2021
Citation
Shubhankar Dutta, Kakoli Bose; Remodelling structure-based drug design using machine learning. Emerg Top Life Sci 14 May 2021; 5 (1): 13–27. doi: https://doi.org/10.1042/ETLS20200253
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