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

页码：1623 / 1626

页数：4

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