AlphaFold’s Predictive Revolution in Precision Oncology

作者：Xiyu Zhao, Victor Yang, Arjun K. Menta, Jacob Blum, Adam Wahida, Vivek Subbiah · 发表于：AI in Precision Oncology · 年份：2024 · DOI：10.1089/aipo.2024.0010 · 被引用次数：13 · 研究领域：Cancer Genomics and Diagnostics、Bioinformatics and Genomic Networks、Lung Cancer Treatments and Mutations

Over the past three decades, genomic medicine has undergone a transformative journey driven by groundbreaking technologies such as the Human Genome Project and CRISPR-Cas9. Now, artificial intelligence (AI) has taken center stage with AlphaFold, a remarkable example of AI’s computational capabilities in predicting protein structures. In this review, we delve into AlphaFold’s recent advancements and explore its potential impact on predictive medicine. The latest iteration of AlphaFold represents a major milestone in structural biology, showcasing AI’s unprecedented ability to accurately predict intricate protein structures. This shift toward predictive medicine envisions an era where AI, integrated with genomic data, revolutionizes our understanding of diseases, facilitates drug design, and enables personalized therapeutics. However, this evolution comes with challenges such as predicting quaternary structures, incorporating posttranslational modifications, and simulating the dynamic and complex environmental interactions of proteins. Addressing these challenges could enhance AlphaFold’s predictive accuracy and open new avenues for targeted drug design within specific cellular compartments. This review underscores the importance of predicting protein functions, binding kinetics, and thermodynamic properties for effective drug development. Integration with drug discovery platforms and algorithms for virtual screening and molecular docking can optimize the design of novel therap...