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 approximately 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.
Institute for Computer Science, Machine Learning Lab, University Freiburg, Georges Köhler Allee 79, D-79110 Freiburg/Br., Germany. helma@informatik.uni-freiburg.de
Christoph
C
Helma
Tobias
T
Cramer
Stefan
S
Kramer
Luc
L
De Raedt
D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D009152:Mutagenicity Tests / Q000706:statistics & numerical data*; D009153:Mutagens / Q000737:chemistry / Q000633:toxicity; D013329:Structure-Activity Relationship
D009153:Mutagens