Assistant professor, Department of Chemistry, Shivpati P. G. College Shoharatgarh, Shiddharthnagar
JournalPIJST
Volume / Issue2 / 7
Pages54–65
Published06 Jul 2025
Paper IDPIJST27J25007
Views / Downloads1 / 1
Article summary
Abstract
Artificial intelligence (AI) has emerged as a transformative tool for predicting
chemical reactions and estimating product yields, offering substantial benefits for
sustainable and efficient chemical synthesis. This review synthesizes the theoretical
foundations, data-representation schemes, and machine-learning paradigms that
enable accurate reaction and yield prediction. Modern AI models—including graph
neural networks, template-free architectures, and transformer-based sequence models—
learn reactivity rules directly from large reaction datasets, overcoming several
limitations of early expert-system and quantum-chemistry-driven approaches. Data
quality and curation remain central challenges, as public reaction databases often
contain inconsistencies, structural biases, and incomplete yield information. Strategies
such as standardized cleaning pipelines, benchmarked data splits, and uncertainty
quantification methods (e.g., ensembles, conformal prediction) improve model reliability
and generalization. Recent advances demonstrate promising cross-domain transfer,
enabling models trained on one reaction class to perform well on others. Incorporating
reaction conditions, catalysts, and multimodal inputs further enhances predictive
performance and selectivity estimation. Prospective experimental validation,
transparent reporting standards, and open-science frameworks are essential to ensure
reproducibility and trust in AI-assisted chemical design. Overall, AI-guided prediction
of reaction outcomes and yields provides a powerful route toward accelerated discovery,
greener synthesis pathways, and improved decision-making in chemical research and
industry.
Ajay Kumar Singh (2025). Prediction of Chemical Reactions and Product
Yields Using Artificial Intelligence. Procedure International Journal of Science and Technology, 2(7), 54–65. https://www.pijst.com/article/pijst27j25007/prediction-of-chemical-reactions-and-product-yields-using-artificial-intelligence
Ajay Kumar Singh. “Prediction of Chemical Reactions and Product
Yields Using Artificial Intelligence.” Procedure International Journal of Science and Technology, vol. 2, no. 7, 2025, pp. 54–65. https://www.pijst.com/article/pijst27j25007/prediction-of-chemical-reactions-and-product-yields-using-artificial-intelligence
Ajay Kumar Singh. “Prediction of Chemical Reactions and Product
Yields Using Artificial Intelligence.” Procedure International Journal of Science and Technology 2, no. 7 (2025): 54–65. https://www.pijst.com/article/pijst27j25007/prediction-of-chemical-reactions-and-product-yields-using-artificial-intelligence
Ajay Kumar Singh (2025) ‘Prediction of Chemical Reactions and Product
Yields Using Artificial Intelligence’, Procedure International Journal of Science and Technology, 2(7), pp. 54–65. Available at: https://www.pijst.com/article/pijst27j25007/prediction-of-chemical-reactions-and-product-yields-using-artificial-intelligence.
Ajay Kumar Singh, “Prediction of Chemical Reactions and Product
Yields Using Artificial Intelligence,” Procedure International Journal of Science and Technology, vol. 2, no. 7, pp. 54–65, 2025. https://www.pijst.com/article/pijst27j25007/prediction-of-chemical-reactions-and-product-yields-using-artificial-intelligence.
Ajay Kumar Singh. Prediction of Chemical Reactions and Product
Yields Using Artificial Intelligence. Procedure International Journal of Science and Technology. 2025;2(7):54–65. https://www.pijst.com/article/pijst27j25007/prediction-of-chemical-reactions-and-product-yields-using-artificial-intelligence.
Ajay Kumar Singh. Prediction of Chemical Reactions and Product
Yields Using Artificial Intelligence. Procedure International Journal of Science and Technology 2025, 2 (7), 54–65. https://www.pijst.com/article/pijst27j25007/prediction-of-chemical-reactions-and-product-yields-using-artificial-intelligence.
The author(s) declare that no specific funding was received for this work.
Conflict of Interest
The author(s) declare that they have no conflicts of interest relevant to this work.
Ethical Approval
The author(s) declare that ethical approval was not applicable to this work.
Data Availability
The author(s) declare that data sharing is not applicable, as no datasets were generated or analysed for this work.
Author Contributions
All authors contributed to the conception, research, preparation, review and final approval of the manuscript and agree to be accountable for the accuracy and integrity of the work.
AI-use Declaration
AI tools were used only for language editing. The author(s) reviewed and verified the final content and remain responsible for its accuracy and integrity.
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