Artificial Intelligence in Chemistry: A Transformative Review

The advent of Artificial Intelligence (AI) is ushering in a new era across scientific disciplines, with chemistry being one of the most profoundly impacted. This review article explores the multifaceted integration of AI, particularly machine learning (ML) and deep learning (DL), into various branches of chemistry, including drug discovery, materials science, chemical synthesis, and analytical chemistry. We delve into how AI is accelerating the pace of discovery, optimizing processes, and enabling novel insights that were previously unattainable through traditional experimental or computational methods. Furthermore, this article discusses the current methodologies, highlights significant results and breakthroughs, and addresses the prevailing challenges and future prospects of AI in revolutionizing chemical research and industrial applications. The ethical implications and the need for explainable AI (XAI) in chemistry are also critically examined.

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Publication Details

Journal
Journal for Research in Applied Sciences and Biotechnology
Published
2025-06-30
DOI
https://doi.org/10.55544/jrasb.4.3.20
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
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article

Artificial Intelligence in Chemistry: A Transformative Review

Sanyogita Shahi, Parinita Tripathy, Suryanarayana Kumar Yadav
Journal for Research in Applied Sciences and Biotechnology
Computational Drug Discovery Methods
article

Artificial Intelligence in Chemistry: A Transformative Review

Sanyogita Shahi, Parinita Tripathy, Suryanarayana Kumar Yadav
article en

Abstract

The advent of Artificial Intelligence (AI) is ushering in a new era across scientific disciplines, with chemistry being one of the most profoundly impacted. This review article explores the multifaceted integration of AI, particularly machine learning (ML) and deep learning (DL), into various branches of chemistry, including drug discovery, materials science, chemical synthesis, and analytical chemistry. We delve into how AI is accelerating the pace of discovery, optimizing processes, and enabling novel insights that were previously unattainable through traditional experimental or computational methods. Furthermore, this article discusses the current methodologies, highlights significant results and breakthroughs, and addresses the prevailing challenges and future prospects of AI in revolutionizing chemical research and industrial applications. The ethical implications and the need for explainable AI (XAI) in chemistry are also critically examined.

Journal for Research in Applied Sciences and BiotechnologyVol. 4(3)
Kalinga University (IN)
Openalex Percentile: Top 79%
Computational Drug Discovery Methods
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