Warmr: a data mining tool for chemical data

Data mining techniques are becoming increasingly important in chemistry as databases become too large to examine manually. Data mining methods from th e field of Inductive Logic Programming (ILP) have potential advantages for structural chemical data. In this paper we present Warmr, the first ILP dat a mining algorithm to be applied to chemoinformatic data. We illustrate the value of Warmr by applying it to a well studied database of chemical compo unds tested for carcinogenicity in rodents. Data mining was used to find al l frequent substructures in the database, and knowledge of these frequent s ubstructures is shown to add value to the database. One use of the frequent substructures was to convert them into probabilistic prediction rules rela ting compound description to carcinogenesis. These rules were found to be a ccurate on test data, and to give some insight into the relationship betwee n structure and activity in carcinogenesis. The substructures were also use d to prove that there existed no accurate rule, based purely on atom-bond s ubstructure with less than seven conditions, that could predict carcinogeni city. This results put a lower bound on the complexity of the relationship between chemical structure and carcinogenicity. Only by using a data mining algorithm, and by doing a complete search, is it possible to prove such a result. Finally the frequent substructures were shown to add value by incre asing the accuracy of statistical and machine learning programs that were t rained to predict chemical carcinogenicity. We conclude that Warmr, and ILP data mining methods generally, are an important new tool for analysing che mical databases.