• S.Moorthi

Accurate predictions of biochemical reactions are essential for progress in drug discovery, metabolic engineering, and synthetic biology. Traditional rule-based approaches often struggle with the complexity inherent in biochemical data. Neural networks have become influential tools due to their ability to learn complex patterns effectively, offering promising solutions for predicting these reactions. This review examines different neural network architectures, techniques for preparing data, and strategies used in predictive biochemical modeling. Recent advances in deep learning have led to substantial enhancements in predicting reaction outcomes as well as designing pathways and forecasting enzyme-catalyzed reactions. Challenges such as limited data availability, high computational costs, and difficulties with interpretability are also addressed here. Looking ahead, integrating multimodal data sources along with developing explainable AI models represent key areas for future development. Keywords: Biochemical Reaction Prediction, Neural Networks, Deep Learning, Machine Learning, Biochemical Databases, Metabolic Engineering, Drug Discovery, Explainable AI, Reaction Mechanism Modeling, Bioinformatics.

"Biochemical Reaction Prediction Using Neural Networks: A Comprehensive Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.n725-n744, April-2025, Available :http://www.jetir.org/papers/JETIR2504D89.pdf

"Biochemical Reaction Prediction Using Neural Networks: A Comprehensive Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppn725-n744, April-2025, Available at : http://www.jetir.org/papers/JETIR2504D89.pdf