Transforming Biochemistry with Artificial Intelligence: Applications in Protein Structure Prediction, Drug Discovery, Clinical Diagnostics and Laboratory Automation
DOI:
https://doi.org/10.66132/mr2103Keywords:
Artificial Intelligence, Clinical Biochemistry, Drug Discovery, Laboratory Automation, Protein Structure PredictionAbstract
Artificial Intelligence is rapidly transforming the landscape of biochemistry and enabling the smart interpretation of biological complexity in clinical, laboratory, and research settings. The review provides an overview of various applications of AI in molecular modelling, drug discovery, clinical diagnostics, lab automation, and emerging technologies, along with associated challenges, ethical implications, and future research avenues. The literature was searched in the Scopus, Google Scholar, PubMed, and Web of Science databases (2015–2025) using MeSH Terms and keyword combinations. A narrative-systematic synthesis of 312 articles was completed for a total of 95 full-text publications that met the inclusion criteria. From protein structure prediction to biomarker discovery, metabolic pathway analysis, drug design, enzyme engineering, clinical decision support, and many new applications such as synthetic biology and federated learning, AI has made a difference. Moreover, machine learning models achieve high performance in disease risk stratification for most diseases. Although challenges exist in data quality, interpretation, regulation and ethics, AI can offer unprecedented advances in biochemical research, diagnostics and create the capacity to tailor and customise patient care.
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