Predicting the acute ecotoxicity of chemical substances by machine learning using graph theory.

Clicks: 223
ID: 14796
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Popular

Ranked #287 of 409 articles by views in Chemosphere

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 409 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
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.
Reference Key
takata2019predictingchemosphere Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Takata, Michiyoshi;Lin, Bin-Le;Xue, Mianqiang;Zushi, Yasuyuki;Terada, Akihiko;Hosomi, Masaaki;
Journal Chemosphere
Year 2019
DOI
S0045-6535(19)31828-4
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.