GGL-Tox: Geometric Graph Learning for Toxicity Prediction

• Published in: Journal of chemical information and modeling Vol. 61; no. 4; pp. 1691 - 1700
• Main Authors: Jiang, Jian, Wang, Rui, Wei, Guo-Wei
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 26.04.2021
• Subjects: Algorithms; Chemical Information; Decision trees; Machine learning; Toxicity; Toxicology
• ISSN: 1549-9596; 1549-960X; 1549-960X
• DOI: 10.1021/acs.jcim.0c01294

Toxicity analysis is a major challenge in drug design and discovery. Recently significant progress has been made through machine learning due to its accuracy, efficiency, and lower cost. US Toxicology in the 21st Century (Tox21) screened a large library of compounds, including approximately 12 000 environmental chemicals and drugs, for different mechanisms responsible for eliciting toxic effects. The Tox21 Data Challenge offered a platform to evaluate different computational methods for toxicity predictions. Inspired by the success of multiscale weighted colored graph (MWCG) theory in protein–ligand binding affinity predictions, we consider MWCG theory for toxicity analysis. In the present work, we develop a geometric graph learning toxicity (GGL-Tox) model by integrating MWCG features and the gradient boosting decision tree (GBDT) algorithm. The benchmark tests of the Tox21 Data Challenge are employed to demonstrate the utility and usefulness of the proposed GGL-Tox model. An extensive comparison with other state-of-the-art models indicates that GGL-Tox is an accurate and efficient model for toxicity analysis and prediction.