Machine Learning and Artificial Intelligence: A Paradigm Shift in Big
Data-Driven Drug Design and Discovery

Purvashi      Pasrija; Prakash      Jha; Pruthvi      Upadhyaya; Mohd.   Shoaib   Khan; Madhu      Chopra
doi:10.2174/1568026622666220701091339
Abstract

Background: The lengthy and expensive process of developing a novel medicine often takes many years and entails a significant financial burden due to its poor success rate. Furthermore, the processing and analysis of quickly expanding massive data necessitate the use of cutting-edge methodologies. As a result, Artificial Intelligence-driven methods that have been shown to improve the efficiency and accuracy of drug discovery have grown in favor.
Objective: The goal of this thorough analysis is to provide an overview of the drug discovery and development timeline, various approaches to drug design, and the use of Artificial Intelligence in many aspects of drug discovery.
Methods: Traditional drug development approaches and their disadvantages have been explored in this paper, followed by an introduction to AI-based technology. Also, advanced methods used in Machine Learning and Deep Learning are examined in detail. A few examples of big data research that has transformed the field of medication discovery have also been presented. Also covered are the many databases, toolkits, and software available for constructing Artificial Intelligence/Machine Learning models, as well as some standard model evaluation parameters. Finally, recent advances and uses of Machine Learning and Deep Learning in drug discovery are thoroughly examined, along with their limitations and future potential.
Conclusion: Artificial Intelligence-based technologies enhance decision-making by utilizing the abundantly available high-quality data, thereby reducing the time and cost involved in the process. We anticipate that this review would be useful to researchers interested in Artificial Intelligencebased drug development.
Keywords: Medicinal chemistry, Quantitative structure-activity relationship, Drug discovery, Computer-aided drug design, Big data, Artificial intelligence, Machine learning, Deep learning.
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DOI https://dx.doi.org/10.2174/1568026622666220701091339	Print ISSN 1568-0266
Publisher Name Bentham Science Publisher	Online ISSN 1873-4294
Current Topics in Medicinal Chemistry

Machine Learning and Artificial Intelligence: A Paradigm Shift in Big Data-Driven Drug Design and Discovery

Abstract

Graphical Abstract

Adaptogens—History and Future Perspectives

AlphaFold in Medicinal Chemistry: Opportunities and Challenges

Artificial intelligence for Natural Products Discovery and Development

Chemistry Based on Natural Products for Therapeutic Purposes

Current Topics in Medicinal Chemistry

Machine Learning and Artificial Intelligence: A Paradigm Shift in Big Data-Driven Drug Design and Discovery

Abstract

Graphical Abstract

Call for Papers in Thematic Issues

Adaptogens—History and Future Perspectives

AlphaFold in Medicinal Chemistry: Opportunities and Challenges

Artificial intelligence for Natural Products Discovery and Development

Chemistry Based on Natural Products for Therapeutic Purposes

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