Evolutionary features for task-specific machine-learning applications

被引:1
作者
Laverty, Scott [1 ]
Dey, Sourav [1 ]
Zahrt, Andrew F. [1 ]
机构
[1] Univ Penn, Dept Chem, 231 S 34 th St, Philadelphia, PA 19104 USA
来源
CHEM | 2024年 / 10卷 / 06期
关键词
D O I
10.1016/j.chempr.2024.05.020
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
In the May issue of Chem , Pflu<spacing diaeresis> ger et al. presented an automated, evolutionary approach to constructing feature vectors for molecules. The predictions from EvoMPF are on par with those of most state-of-the-art methods for a broad array of prediction tasks, can be implemented simply, and are inherently chemically interpretable.
引用
收藏
页码:1623 / 1626
页数:4
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