健身景观
计算机科学
遗传适应性
人工智能
机器学习
作者
Neil Thomas,Lucy J. Colwell
出处
期刊:Cell systems
[Elsevier]
日期:2021-11-17
卷期号:12 (11): 1019-1020
标识
DOI:10.1016/j.cels.2021.10.004
摘要
Machine-learning-guided protein design is rapidly emerging as a strategy to find high-fitness multi-mutant variants. In this issue of Cell Systems, Wittman et al. analyze the impact of design decisions for machine-learning-assisted directed evolution (MLDE) on its ability to navigate a fitness landscape and reliably find global optima.
科研通智能强力驱动
Strongly Powered by AbleSci AI