AI and Machine Learning in Biotechnology: A Paradigm Shift in Biochemical Innovation

Author(s): Praveen Chakravarthi G, Ram Babu V, Ramamurty DSVNM, Rahul G, Prasad SVGVA

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in biotechnology and biochemistry is driving a paradigm shift, revolutionizing research and applications across these fields. This review explores how AI and ML are reshaping traditional methods by improving the accuracy, efficiency, and scalability of complex biochemical processes. Key advancements include AI-driven genome sequencing, protein structure prediction, drug discovery, and bioprocess optimization. In biochemistry, AI enhances the analysis of high-throughput data, enables better prediction of chemical reactions, and supports metabolomics and proteomics studies. The role of AI in personalized medicine, including disease diagnostics, pharmacogenomics, and precision treatments, is also highlighted. While AI and ML promise unprecedented opportunities, challenges such as data quality, model interpretability, and ethical concerns remain significant hurdles. Looking forward, AI-driven innovations are poised to further transform biotechnology, fostering interdisciplinary collaborations and sustainable biochemical practices. This article delves into these advancements, challenges, and future prospects, underscoring AI and ML's pivotal role in advancing biotechnology and biochemistry into new frontiers.