深度学习
计算机科学
人工智能
机器学习
领域(数学)
生成语法
蛋白质设计
蛋白质结构预测
人工神经网络
计算生物学
蛋白质结构
生物
数学
生物化学
纯数学
作者
Hamed Khakzad,Ilia Igashov,Arne Schneuing,Casper A. Goverde,Michael M. Bronstein,Bruno E. Correia
出处
期刊:Cell systems
[Elsevier]
日期:2023-11-01
卷期号:14 (11): 925-939
被引量:25
标识
DOI:10.1016/j.cels.2023.10.006
摘要
The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of proteins. Along with novel architectures for generative modeling and sequence analysis, they have revolutionized the protein design field in the past few years remarkably by improving the accuracy and ability to identify novel protein sequences and structures. Deep neural networks can now learn and extract the fundamental features of protein structures, predict how they interact with other biomolecules, and have the potential to create new effective drugs for treating disease. As their applicability in protein design is rapidly growing, we review the recent developments and technology in deep learning methods and provide examples of their performance to generate novel functional proteins.
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