跳跃者
人工智能
蛋白质结构预测
计算机科学
创造力
蛋白质结构
计算生物学
机器学习
生物
心理学
生物化学
社会心理学
操作系统
标识
DOI:10.1073/pnas.2311128120
摘要
This year's Lasker Basic Science Award recognizes the invention of AlphaFold, a revolutionary advance in the history of protein research which for the first time offers the practical ability to accurately predict the three-dimensional arrangement of amino acids in the vast majority of proteins on a genomic scale on the basis of sequence alone [J. Jumper et al., Nature596, 583-589 (2021) and K. Tunyasuvunakool et al.,Nature596, 590-596 (2021)]. This extraordinary achievement by Demis Hassabis and John Jumper and their coworkers at Google's DeepMind and other collaborators was built on decades of experimental protein structure determination (structural biology) as well as the gradual development of multiple strategies incorporating biologically inspired statistical approaches. But when Jumper and Hassabis added a brew of innovative neural network-based machine learning approaches to the mix, the results were explosive. Realizing the half-century-old dream of predicting protein structure has already accelerated the pace and creativity of many areas of Chemistry, Biology, and Medicine.
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