Predicting the effect of chemicals on fruit using graph neural networks

被引:3
作者
Han, Junming [1 ]
Li, Tong [2 ]
He, Yun [3 ]
Yang, Ziyi [4 ]
机构
[1] Yunnan Agr Univ, Coll Food Sci & Technol, Kunming 650201, Peoples R China
[2] Yunnan Agr Univ, Kunming 650201, Peoples R China
[3] Yunnan Agr Univ, Coll Big Data, Kunming 650201, Peoples R China
[4] Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Peoples R China
关键词
Neural networks; Computational chemistry; Artificial intelligence; Food quality; DEEP; CLASSIFICATION; MODEL;
D O I
10.1038/s41598-024-58991-y
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The neural network method is a type of machine learning that has made significant advances over the past few years in a variety of fields, particularly text, speech, images, videos, etc. In areas where data is unstructured, traditional machine learning has not been able to surpass the 'glass ceiling'; therefore, researchers have turned to neural networks as auxiliary tools to achieve significant breakthroughs or develop new research methods. An array of computational chemistry challenges can be addressed using neural networks, including virtual screening, quantitative structure-activity relationships, protein structure prediction, materials design, quantum chemistry, and property prediction, among others. This paper proposes a strategy for predicting the chemical properties of fruits by using graph neural networks, and it aims to provide some guidance to researchers and streamline the identification process.
引用
收藏
页数:10
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