The research builds on well-established principles from the pharmaceutical industry known as Quantitative structure-activity relationships (QSARs) in which the type of atoms and how they are connected together can be correlated with the activity of a drug molecule. Certain molecular shapes and types are soluble in water, for instance, or interact in a certain way with different enzymes and other proteins in the body, leading to their overall activity. Different molecular features will make a similar molecule behave in a different way - more or less soluble, stronger or weaker acting. The team has now turned the QSAR around so that instead of searching for the features in a molecule that make it of benefit in medicine they look for the atomic groups and the type of bonds that hold them together to find associations with toxicity.
The team points out that few earlier attempts at predicting toxicity of chemicals have proved successful, most approaches are no better than random guessing. The team's new statistical approach combines "Random Forest" selection with "Nave Bayes" statistical analysis to boost the predictions well beyond random. They team saw prediction accuracy in 2 out of 3 chemicals tested. Given that there are around 100,000 industrial chemicals that need toxicity profiling, this result should allow the industry and regulators to focus on a large number of the most pressing of those, the ones predicted to have greatest toxicity and leave the less likely until additional resources are available.
The researchers are now tuning the algorithm to work faster and with greater precision so that it
|Contact: Albert Ang|