An assessment of submissions made to the Predictive Toxicology Evaluation Challenge

Constructing "good" models for chemical carcinogenesis was identified in IJCAI-97 as providing a substantial challenge to "knowledge discovery" programs. Attention was drawn to a comparative exercise which called for predictions on the outcome of 30 rodent carcinogenicity bioassays. This -- the Predictive Toxicology Evaluation (or PTE) Challenge -- was seen to provide AI programs with an opportunity to participate in an enterprise of scientific merit, and a yardstick for comparison against strong competition. Here we provide an assessment of the best machine learning (ML) submissions made. Models submitted are assessed on: (1) their accuracy, in comparison to models developed with expert collaboration; and (2) their explanatory value for toxicology. The principal findings were: (a) using structural information available from a standard modelling package, layman-devised features, and outcomes of established biological tests, results from MLderived models were at lea..