Toxicity-indicating structural patterns

We describe a toxicity alerting system for uncharacterized compounds, which is based upon comprehensive tables of substructure fragments that are indicative of toxicity risk. These tables were derived computationally by analyzing the RTECS database and the World Drug Index. We provide, free of charge, a Java applet for structure drawing and toxicity risk assessment. In an independent investigation, we compared the toxicity classification performance of naive Bayesian clustering, k next neighbor classification, and support vector machines. To visualize the chemical space of both toxic and druglike molecules, we trained a large self-organizing map (SOM) with all compounds from the RTECS database and the IDDB. In summary, we found that a support vector machine performed best at classifying compounds of defined toxicity into appropriate toxicity classes. Also. SOMs performed excellently in separating toxic frorn nontoxic substances. Although these two methods are limited to compounds that are structurally similar to known toxic substances, our fragment-based approach extends predictions to compounds that are structurally dissimilar to compounds used in the training set.