Prediction of melting points of a diverse chemical set using fuzzy regression tree

The classification and regression trees (CART) possess the advantage of being able to handle large data sets and yield readily interpretable models. In spite to these advantages, they are also recognized as highly unstable classifiers with respect to minor perturbations in the training data. In the other words methods present high variance. Fuzzy logic brings in an improvement in these aspects due to the elasticity of fuzzy sets formalism. ACS, which is a meta-heuristic algorithm and derived from the observation of real ants, was used to optimize fuzzy parameters. The purpose of this study was to explore the use of fuzzy regression tree (RT) for modeling of melting points of a large variety of chemical compounds. To test the ability of the resulted tree, a set of approximately 4173 structures and their melting points were used (3000 compounds as training set and 1173 as validation set). Further, an external test set contains of 277 drugs were used to validate the prediction ability of the tree. Comparison the results obtained from both trees showed that the fuzzy RT performs better than that produced by recursive partitioning procedure.