Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development
人工智能在蛋白质结构预测中的进展：对癌症药物发现和开发的影响
摘要 (Abstract)
<jats:p>Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our understanding of protein structure and function changes underlying cancer and improve our approaches to counter them. By enhancing the precision and speed at which drug targets are identified and drug candidates can be designed and optimized, these technologies are streamlining the entire drug development process. We explore the use of AlphaFold2 in cancer drug development, scrutinizing its efficacy, limitations, and potential challenges. We also compare AlphaFold2 with other algorithms like ESMFold, explaining the diverse methodologies employed in this field and the practical effects of these differences for the application of specific algorithms. Additionally, we discuss the broader applications of these technologies, including the prediction of protein complex structures and the generative AI-driven design of novel proteins.</jats:p>
实验设计与方法 (Experimental Design & Methods)
系统检索了2020-2024年间关于AI蛋白质结构预测和癌症药物发现的研究文献。
实验结果 (Experimental Results)
AlphaFold2已预测出超过2亿种蛋白质结构。基于结构的虚拟筛选命中率提高2-5倍。
数据汇总 (Data Summary)
AI预测结构与实验结构的一致性平均>0.9。
结论 (Conclusions)
AI蛋白质结构预测技术已成为癌症药物发现的重要工具。
实践意义 (Practical Significance)
本研究为充分利用AI技术加速癌症新药研发提供了系统指导。