Inhaltszusammenfassung

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 investigat...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 percentage 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.» weiterlesen» einklappen

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DFG Fachgebiet:
4.43 - Informatik

DDC Sachgruppe:
Informatik