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《AlphaFold 3综述:药物设计与治疗学的变革性进展》

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

作者信息

Desai Dev, Kantliwala Shiv V, Vybhavi Jyothi, Ravi Renju, Patel Harshkumar, Patel Jitendra

机构信息

Research, Albert Einstein College of Medicine, New York, USA.

Medicine, Smt. Nathiba Hargovandas Lakhmichand Municipal Medical College, Ahmedabad, IND.

出版信息

Cureus. 2024 Jul 2;16(7):e63646. doi: 10.7759/cureus.63646. eCollection 2024 Jul.

Abstract

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.

摘要

谷歌深度思维技术有限公司(英国伦敦)最近发布了其名为AlphaFold 3的生物分子结构预测人工智能(AI)模型的新版本。该创新在准确性方面更胜一筹,比其前身AlphaFold 2更强大,其能力和速度震惊了世界。确定各种蛋白质的结构以及其形状如何与受体相互作用需要人类数年时间,但AlphaFold 3能在几秒钟内预测出相同的结构。该版本在药物发现、疫苗、酶促过程以及确定不同生物过程的速率和效果等领域的效用难以想象。AlphaFold 3使用了类似的机器学习和深度学习模型,如Gemini(谷歌深度思维技术有限公司)。AlphaFold 3已经成为计算生物化学和药物开发领域以及受体调节和生物分子开发的一个转折点。借助AlphaFold 3以及类似的模型,研究人员将对蛋白质的结构动力学及其相互作用获得无与伦比的见解,为科学家和医生开辟新的途径以造福患者。像AlphaFold 3这样的人工智能模型,在经过针对高标准研究出版物的严格验证后得到加强,必将催化进一步的创新,并让我们 glimpse into the future of biomedicine。(原文此处“glimpse into”有误,应为“glimpse into”的正确形式“glimpse into”,直译为“瞥见”,结合语境意译为“洞察”)

谷歌深度思维技术有限公司(英国伦敦)最近发布了其名为AlphaFold 3的生物分子结构预测人工智能(AI)模型的新版本。该创新在准确性方面更胜一筹,比其前身AlphaFold 2更强大,其能力和速度震惊了世界。确定各种蛋白质的结构以及其形状如何与受体相互作用需要人类数年时间,但AlphaFold 3能在几秒钟内预测出相同的结构。该版本在药物发现、疫苗、酶促过程以及确定不同生物过程的速率和效果等领域的效用难以想象。AlphaFold 3使用了类似的机器学习和深度学习模型,如Gemini(谷歌深度思维技术有限公司)。AlphaFold 3已经成为计算生物化学和药物开发领域以及受体调节和生物分子开发的一个转折点。借助AlphaFold 3以及类似的模型,研究人员将对蛋白质的结构动力学及其相互作用获得无与伦比的见解,为科学家和医生开辟新的途径以造福患者。像AlphaFold 3这样的人工智能模型,在经过针对高标准研究出版物的严格验证后得到加强,必将催化进一步的创新,并为生物医学的未来带来新的洞察。

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