Jim Mason

MedSci Publisher of Sophia Publishing Plateform, Richmond, BC, V7A 4Z5, Canada
Author    Correspondence author
International Journal of Molecular Medical Science, 2024, Vol. 14, No. 3   
Received: 10 May, 2024    Accepted: 16 May, 2024    Published: 20 May, 2024

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The integration of artificial intelligence (AI) in structural biology has revolutionized medicine design, notably through AlphaFold 3's accurate prediction of biomolecular interactions. With AI predicting over 600 million protein structures, the vast database enhances the identification of novel drug targets and optimization of therapeutic molecules. However, AI's limitations in capturing protein dynamics highlight the continued need for experimental validation. The synergy between AI's predictive power and empirical methods like cryo-EM and NMR spectroscopy fosters comprehensive drug design, accelerating the development of personalized medicine. This perspective underscores the necessity of balancing AI and experimental approaches to unlock unprecedented therapeutic innovations.

AI; AlphaFold; Drug discovery; Experimental validation; Personalized medicine