Nonequivalent Effects of Diverse LogP Algorithms in Three QSAR Studies

Abstract

AbstractDespite of the availability and facility of accessing several algorithms for calculation of LogP in QSA(P)R studies, articles typically do not describe the selection procedure for the method used. Therefore, three studies to verify the influence of different LogP algorithms on building QSAR models were performed. Two QSAR data sets from the literature (forty‐two tricyclic phtalimide inhibitors of HIV‐integrase and fourty‐six TIBO derivatives inhibitors of HIV‐reverse transcriptase) were used together with LogP calculated by thirteen algorithms, and several regression models were constructed and compared. A new QSAR study for 4,5‐dihydroxypyrimidine carboxamides inhibitors of HIV‐1 integrase was also performed. The explained and predicted variance, results from external validation, leave‐N‐out cross‐validation and y‐randomization test were analyzed for all models from the three data sets. Despite the same physicochemical meaning, LogP's calculated by distinct methods may show different levels of contribution to the model. This observation comes out from the comparison of validated models. These results indicate that the arbitrary choice of one specific algorithm for LogP calculation, as is usual in QSA(P)R studies , does not necessarily lead to the highest quality model for the analyzed data set.