Review of AlphaFold 3: Transformative Advances in Drug Design and Therapeutics

医学 转化式学习 药品 工程伦理学 药理学 教育学 工程类 心理学
作者
Dev Desai,Shiv V Kantliwala,Jyothi Vybhavi,Renju Ravi,Harshkumar Patel,Jitendra S. Patel
出处
期刊:Cureus [Cureus, Inc.]
卷期号:16 (7): e63646-e63646 被引量:71
标识
DOI:10.7759/cureus.63646
摘要

Google DeepMind Technologies Limited (London, United Kingdom) recently released its new version of the biomolecular structure predictor artificial intelligence (AI) model named AlphaFold 3. Superior in accuracy and more powerful than its predecessor AlphaFold 2, this innovation has astonished the world with its capacity and speed. It takes humans years to determine the structure of various proteins and how the shape works with the receptors but AlphaFold 3 predicts the same structure in seconds. The version's utility is unimaginable in the field of drug discoveries, vaccines, enzymatic processes, and determining the rate and effect of different biological processes. AlphaFold 3 uses similar machine learning and deep learning models such as Gemini (Google DeepMind Technologies Limited). AlphaFold 3 has already established itself as a turning point in the field of computational biochemistry and drug development along with receptor modulation and biomolecular development. With the help of AlphaFold 3 and models similar to this, researchers will gain unparalleled insights into the structural dynamics of proteins and their interactions, opening up new avenues for scientists and doctors to exploit for the benefit of the patient. The integration of AI models like AlphaFold 3, bolstered by rigorous validation against high-standard research publications, is set to catalyze further innovations and offer a glimpse into the future of biomedicine.
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