Predicting the acute ecotoxicity of chemical substances by machine learning using graph theory. (January 2020)

• Record Type: Journal Article
• Authors: Takata, Michiyoshi; Lin, Bin-Le; Xue, Mianqiang; Zushi, Yasuyuki; Terada, Akihiko; Hosomi, Masaaki
• Is Part Of: Chemosphere. Volume 238(2020)
• Publication Date: 2020-01
• Subjects: Ecotoxicity prediction -- Chemical substance clustering -- Machine learning -- Graph theory -- ECOSAR -- AIST-MeRAM; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2019.124604
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: Accurate in silico predictions of chemical substance ecotoxicity has become an important issue in recent years. Most conventional methods, such as the Ecological Structure-Activity Relationship (ECOSAR) model, cluster chemical substances empirically based on structural information and then predict toxicity by employing a log P linear regression model. Due to empirical classification, the prediction accuracy does not improve even if new ecotoxicity test data are added. In addition, most of the conventional methods are not appropriate for predicting the ecotoxicity on inorganic and/or ionized compounds. Furthermore, a user faces difficulty in handling multiple Quantitative Structure-Activity Relationship (QSAR) formulas with one chemical substance. To overcome the flaws of the conventional methods, in this study a new method was developed that applied unsupervised machine learning and graph theory to predict acute ecotoxicity. The proposed machine learning technique is based on the large AIST-MeRAM ecotoxicity test dataset, a software program developed by the National Institute of Advanced Industry Science and Technology for Multi-purpose Ecological Risk Assessment and Management, and the Molecular ACCess System (MACCS) keys that vectorize a chemical structure to 166-bit binary information. The acute toxicity of fish, daphnids, and algae can be predicted with good accuracy, without requiring log P and linear regression models in existing methods. Results from the new method were cross-validated and compared with ECOSAR predictions and show that the new method provides better accuracy for a wider range of chemical substances, including inorganic and ionized compounds. Graphical abstract: Image 1 Highlights: This study developed in silico method to predict the acute ecotoxicity of chemical substances, based on graph theory. The acute toxicity of fish, daphnids, and algae can be predicted with good accuracy, without linear regression models. It provides better prediction accuracy than conventional methods for wider range including inorganic and ionized compounds.