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A I Predicts the Shapes of Molecules to Come

DeepMind has given 3-D structure to 350,000 proteins, including every one made by humans, promising a boon for medicine and drug design. Even before DeepMind began openly sharing its technology and data, AlphaFold was feeding a wide range of projects. University of Colorado researchers are using the technology to understand how bacteria like E. coli and salmonella develop a resistance to antibiotics, and to develop ways of combating this resistance. At the University of California, San Francisco, researchers have used the tool to improve their understanding of the coronavirus. help from AlphaFold, the researchers have improved their understanding of one key protein and are hoping the technology can help increase their

DeepMind 再创里程碑,公开35万种蛋白质预测结构,这五位国内专家怎么看?

目前,AlphaFold 对于模型输入侧的同源序列信息仍然存在一定的依赖,可能会出现预测不够精确的问题;同时,对于三维结构更多依赖于异构接触(heterotypic contacts)而非链内接触或同构接触的蛋白质,也可能出现精度问题,这也是作者在论文中所提到的未来改进方向之一。 在 AlphaFold(以及David Baker组的RoseTTAFold)发布之前,国内已有一些将端到端学习引入到蛋白质结构预测中的初步尝试(包括我们tFold),在预测精度方面,基本可以达到与CASP14中排名第二的Baker组相仿的水平,但与AlphaFold 仍存在一定差距。在 AlphaFold 论文及代码公开后,相信国内这方面的研究将在短期内迎来较大的提升幅度。 在蛋白质结构预测领域,AlphaFold 的出现,在一定程度上降低了基于实验数据的蛋白质结构测定的工作量;同时,通过结合真实实验数据和 AlphaFold 预测出的�

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平均一只 小白鼠 卖200多,成立才4年的集萃药康冲科创板,毛利率畸高,高瓴、红杉、鼎晖纷纷入股|人民币|毛利率
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