Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds

This paper explores the utility of data mining and machine learning algorithms for the induction of
mutagenicity structure−activity relationships (SARs) from noncongeneric data sets. We compare (i) a newly
developed algorithm (MOLFEA) for the generation of descriptors (molecular fragments) for noncongeneric
compounds with traditional SAR approaches (molecular properties) and (ii) different machine learning
algorithms for the induction of SARs from these descriptors. In addition we investigate the optimal parameter
settings for these programs and give an exemplary interpretation of the derived models. The predictive
accuracies of models using MOLFEA derived descriptors is ∼10−15%age points higher than those using
molecular properties alone. Using both types of descriptors together does not improve the derived models.
From the applied machine learning techniques the rule learner PART and support vector machines gave the
best results, although the differences between the learning algorithms are only marginal. We were able to
achieve predictive accuracies up to 78% for 10-fold cross-validation. The resulting models are relatively
easy to interpret and usable for predictive as well as for explanatory purposes.