Predicting the effect of chemicals on fruit using graph neural networks
Abstract 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 ’glas...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-58991-y |
| _version_ | 1797209345069416448 |
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| author | Junming Han Tong Li Yun He Ziyi Yang |
| author_facet | Junming Han Tong Li Yun He Ziyi Yang |
| author_sort | Junming Han |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-24T09:53:13Z |
| format | Article |
| id | doaj.art-587bc73e17e04765a957b60a9fd87770 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T09:53:13Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-587bc73e17e04765a957b60a9fd877702024-04-14T11:15:43ZengNature PortfolioScientific Reports2045-23222024-04-0114111010.1038/s41598-024-58991-yPredicting the effect of chemicals on fruit using graph neural networksJunming Han0Tong Li1Yun He2Ziyi Yang3College of Food Science and Technology, Yunnan Agricultural UniversityYunnan Agricultural UniversityCollege of Big Data, Yunnan Agricultural UniversityCollege of Agronomy and Biotechnology, Yunnan Agricultural UniversityAbstract 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.https://doi.org/10.1038/s41598-024-58991-yNeural networksComputational chemistryArtificial intelligenceFood quality |
| spellingShingle | Junming Han Tong Li Yun He Ziyi Yang Predicting the effect of chemicals on fruit using graph neural networks Scientific Reports Neural networks Computational chemistry Artificial intelligence Food quality |
| title | Predicting the effect of chemicals on fruit using graph neural networks |
| title_full | Predicting the effect of chemicals on fruit using graph neural networks |
| title_fullStr | Predicting the effect of chemicals on fruit using graph neural networks |
| title_full_unstemmed | Predicting the effect of chemicals on fruit using graph neural networks |
| title_short | Predicting the effect of chemicals on fruit using graph neural networks |
| title_sort | predicting the effect of chemicals on fruit using graph neural networks |
| topic | Neural networks Computational chemistry Artificial intelligence Food quality |
| url | https://doi.org/10.1038/s41598-024-58991-y |
| work_keys_str_mv | AT junminghan predictingtheeffectofchemicalsonfruitusinggraphneuralnetworks AT tongli predictingtheeffectofchemicalsonfruitusinggraphneuralnetworks AT yunhe predictingtheeffectofchemicalsonfruitusinggraphneuralnetworks AT ziyiyang predictingtheeffectofchemicalsonfruitusinggraphneuralnetworks |