Gaurang Rai,
- Student, CSE Department, VIT Bhopal University, Kothrikalan, Sehore, Madhya Pradesh, India
Abstract
The chemical industry, a key growth indicator of the global manufacturing ecosystem, is experiencing a digital transformation driven mainly by advancements in Artificial Intelligence (AI) and Machine Learning (ML) in this sector. These technologies are totally revolutionizing current and traditional methodologies by significantly improving process efficiency, reducing costs of manufacturing, accelerating R&D, and improving safety and sustainability standards. Proper utilization of Artificial intelligence (AI) and machine learning (ML) in chemical industry can improve manufacturing efficiency, productivity greatly and sustainability. However, applying AI in commercial production also presents several difficulties, like problems with data capturing and date management, human resources, infrastructure, and associated security risks, trust, and implementation hurdles. For example, capture of the data required to train AI models can be difficult for rare events or costly for large datasets that may need labelling. AI models may also pose security risks during integration into existing industrial control systems. Also, some industries may be hesitant to use AI due to a lack of knowledge, trust and understanding of its working. Despite these challenges, AI has shown the potential to be greatly helpful in chemical manufacturing, sector specially in applications like predictive maintenance, quality assurance, and process optimization. It is necessary to consider the specific needs and capabilities of each manufacturing scenario when deciding how to utilize and deploy AI in chemical manufacturing. As such, early trends suggest that AI/ML can lead to significant cost and efficiency benefits in chemical industries, especially when coupled with the ability to capture huge amounts of data from manufacturing systems and databases. This review explores the current landscape of AI and ML applications in the chemical industry, discussing case studies, technological trends, challenges, and future directions.
Keywords: Artificial intelligence (AI), machine learning, chemical industry, drug discovery, quality assurance, predictive analysis, R&D
[This article belongs to International Journal of Cheminformatics ]
Gaurang Rai. AI and ML in the Chemical Industry: A Review of Transformative Applications and Future Prospects. International Journal of Cheminformatics. 2025; 03(02):1-6.
Gaurang Rai. AI and ML in the Chemical Industry: A Review of Transformative Applications and Future Prospects. International Journal of Cheminformatics. 2025; 03(02):1-6. Available from: https://journals.stmjournals.com/ijci/article=2025/view=216784
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| Volume | 03 |
| Issue | 02 |
| Received | 27/06/2025 |
| Accepted | 09/07/2025 |
| Published | 14/07/2025 |
| Publication Time | 17 Days |
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