Predicting the properties of fruit chemicals using neural networks

As a kind of machine learning method, neural networks have made significant breakthroughs in various fields in the past few years, especially in the fields of text, speech, images, videos, etc., where data is unstructured, traditional machine learning hardly exceeded ’glass ceiling’, So many researchers have turned to using neural networks as auxiliary tools, achieve significant breakthroughs, or found new research methods. In the computational chemistry field, neural networks have ubiquity and broad applicability to a range of challenges, including virtual screening, quantitative structure-activity relationship, protein structure prediction, materials design, quantum chemistry, and property prediction. So, this paper proposes an efficient way that use graph neural networks to predict the properties of chemicals in fruits, and this research aims to provide some guidance to researchers and shorten the cycle of identifying chemicals.