Journal Article

Weighted Feature Significance: A Simple, Interpretable Model of Compound Toxicity Based on the Statistical Enrichment of Structural Features

Ruili Huang, Noel Southall, Menghang Xia, Ming-Hsuang Cho, Ajit Jadhav, Dac-Trung Nguyen, James Inglese, Raymond R. Tice and Christopher P. Austin

in Toxicological Sciences

Volume 112, issue 2, pages 385-393
Published in print December 2009 | ISSN: 1096-6080
Published online October 2009 | e-ISSN: 1096-0929 | DOI: http://dx.doi.org/10.1093/toxsci/kfp231
Weighted Feature Significance: A Simple, Interpretable Model of Compound Toxicity Based on the Statistical Enrichment of Structural Features

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In support of the U.S. Tox21 program, we have developed a simple and chemically intuitive model we call weighted feature significance (WFS) to predict the toxicological activity of compounds, based on the statistical enrichment of structural features in toxic compounds. We trained and tested the model on the following: (1) data from quantitative high–throughput screening cytotoxicity and caspase activation assays conducted at the National Institutes of Health Chemical Genomics Center, (2) data from Salmonella typhimurium reverse mutagenicity assays conducted by the U.S. National Toxicology Program, and (3) hepatotoxicity data published in the Registry of Toxic Effects of Chemical Substances. Enrichments of structural features in toxic compounds are evaluated for their statistical significance and compiled into a simple additive model of toxicity and then used to score new compounds for potential toxicity. The predictive power of the model for cytotoxicity was validated using an independent set of compounds from the U.S. Environmental Protection Agency tested also at the National Institutes of Health Chemical Genomics Center. We compared the performance of our WFS approach with classical classification methods such as Naive Bayesian clustering and support vector machines. In most test cases, WFS showed similar or slightly better predictive power, especially in the prediction of hepatotoxic compounds, where WFS appeared to have the best performance among the three methods. The new algorithm has the important advantages of simplicity, power, interpretability, and ease of implementation.

Keywords: modeling; toxicity prediction; structural features; cell viability; caspase-3,7 activation; in vivo toxicity

Journal Article.  6814 words.  Illustrated.

Subjects: Medical Toxicology ; Toxicology (Non-medical)

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