Fuzzy clustering as rational partition method for QSAR

- Fuzzy minimal as partition method for QSAR was compared with classical methods.
- Fuzzy minimal partition QSAR models present good predictive performance.
- Fuzzy minimal partition for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods.

Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method QLOO2 and then the coefficient of the external test set Qext2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD.

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