Sigma profiles in deep learning: towards a universal molecular descriptor

被引：38

作者：

Abranches, Dinis O. ^{[1
]}

Zhang, Yong ^{[1
]}

Maginn, Edward J. ^{[1
]}

Colon, Yamil J. ^{[1
]}

机构：

[1] Univ Notre Dame, Dept Chem & Biomol Engn, Notre Dame, IN 46556 USA

来源：

CHEMICAL COMMUNICATIONS | 2022年 / 58卷 / 37期

关键词：

PRINCIPLES;

D O I：

10.1039/d2cc01549h

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

This work showcases the remarkable ability of sigma profiles to function as molecular descriptors in deep learning. The sigma profiles of 1432 compounds are used to train convolutional neural networks that accurately correlate and predict a wide range of physicochemical properties. The architectures developed are then exploited to include temperature as an additional feature.

引用

页码：5630 / 5633

页数：4

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