Abstract
It is known that a large number of chemical substances which cause the liver hypertrophy shows the variety of structurally diverse. Therefore, the comprehensive understanding of the toxicological significance of liver hypertrophy has not been elucidated. In this study, we have developed the predicting methods for the liver hypertrophy based on the chemical structure. We made the toxicological database from the risk assessment reports of pesticides, food additives, and veterinary medicinal products that were published by Food Safety Commission of Japan at first. Then, we constructed the prediction Quantitative Structure Activity Relationship (QSAR) model of liver hypertrophy action, based on chemical substance’s descriptors by Deep Learning from this toxicological database. In addition, we compared other machine learning methods, Random forest, and Support vector machines. As a result, the deep learning shows higher performance liver hypertrophy prediction QSAR model compared with other machine learning methods which had the nearly 80% prediction accuracy.
