Carcinogenesis Predictions Using ILP

Obtaining accurate structural alerts for the causes of chemical cancers is a problem of great scientiic and humanitarian value. This paper follows up on earlier research that demonstrated the use of Induc-tive Logic Programming (ILP) for predictions for the related problem of mutagenic activity amongst nitroaromatic molecules. Here we are concerned with predicting carcinogenic activity in rodent bioassays using data from the U.S. National Toxicology Program conducted by the National Institute of Environmental Health Sciences. The 330 chemicals used here are signiicantly more diverse than the previous study, and form the basis for obtaining Structure-Activity Relationships (SARs) relating molecular structure to cancerous activity in rodents. We describe the use of the ILP system Progol to obtain SARs from this data. The rules obtained from Progol are comparable in accuracy to those from expert chemists, and more accurate than most state-of-the-art toxicity prediction methods. The rules can also be interpreted to give clues about the biological and chemical mechanisms of carcinogenesis, and make use of those learnt by Progol for mutagenesis. Finally, we present details of, and predictions for, an ongoing international blind trial aimed speciically at comparing prediction methods. This trial provides ILP algorithms an opportunity to participate at the leading-edge of scientiic discovery.