How ML transforms drug discovery
1 Institute of Biological Sciences, University of Rajshahi, Rajshahi-6205, Bangladesh.
2 San Francisco Bay University, Fremont, California, USA.
3 Medical Assistant Training School, Bagerhat, Bangladesh.
3 Bandhan Private Hospital, Faridpur, Bangladesh.
Review Article
Global Journal of Engineering and Technology Advances, 2024, 21(01), 197-203.
Article DOI: 10.30574/gjeta.2024.21.1.0163
Publication history:
Received on 02 August 2024; revised on 22 October 2024; accepted on 25 October 2024\
Abstract:
The process of drug discovery focuses on identifying novel compounds with specific chemical properties to treat various diseases. In recent years, the field has increasingly incorporated computational techniques, driven by the rapid rise of machine learning technologies and their widespread accessibility. With the goals outlined by the Precision Medicine initiative and the emerging challenges in drug discovery, there is a growing need for robust, standardized, and reproducible computational methodologies to meet these objectives. Machine learning-based predictive models have become particularly significant in the early stages of drug development, prior to preclinical studies, offering the potential to significantly reduce both costs and research timelines. This review article explores the application of these advanced methodologies in recent research, providing insights into the current state of the field. By examining recent advancements, it aims to shed light on the future direction of cheminformatics, its limitations, and the positive outcomes achieved so far.
Keywords:
Machine Learning; Drug Discovery; Cheminformatics; QSAR; Molecular Descriptors; COVID-19
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0