Prediction of Fish Acute Ecotoxicity of Inorganic and Ionized Chemical Substances by Machine Learning

Abstract

International concern on in silico methodology development of ecotoxicity prediction of chemical substances become one of the hot topics these years. To classify chemical substances based on their structure information and then predict ecotoxicity with Log Kow linear regression empirically for the chemical with a similar structure is the most seen conventional methods. Nevertheless, it is challenging to predict the ecotoxicity of the inorganic and ionized chemical substances with multiple functional groups. We previously developed an in silico prediction method by machine learning on the fingerprint of those chemicals with known ecotoxicity test data from AIST-MeRAM to overcome these problems. Our developed method can provide better prediction accuracy than conventional methods for a broader range of chemical substances including inorganic and ionized compounds. To further improve and explain the prediction ability on inorganic and ionized chemical substances, this study investigated the contribution of the structural feature to the prediction of fish acute ecotoxicity with supervised machine learning by using two kinds of target variables. We found that the ecotoxicity of metal compounds was mainly predicted based on their hydrophilicity that structural related to the numbers of oxygen, benzene ring, and methyl groups. Moreover, the prediction accuracy of this method proved to be better than our previous method.