%0 Journal Article %A Chen, Hongming %A Carlsson, Lars %A Eriksson, Mats %A Varkonyi, Peter %A Norinder, Ulf %A Nilsson, Ingemar %D 2013 %T Beyond the Scope of Free-Wilson Analysis: Building Interpretable QSAR Models with Machine Learning Algorithms %U https://acs.figshare.com/articles/journal_contribution/Beyond_the_Scope_of_Free_Wilson_Analysis_Building_Interpretable_QSAR_Models_with_Machine_Learning_Algorithms/2402689 %R 10.1021/ci4001376.s001 %2 https://acs.figshare.com/ndownloader/files/4042396 %K prediction %K data sets %K ECFP 6 fingerprints %K drug discovery project %K signature SVM model %K SVM modeling method %K analysis %K Building Interpretable QSAR Models %K Machine Learning AlgorithmsA novel methodology %X A novel methodology was developed to build Free-Wilson like local QSAR models by combining R-group signatures and the SVM algorithm. Unlike Free-Wilson analysis this method is able to make predictions for compounds with R-groups not present in a training set. Eleven public data sets were chosen as test cases for comparing the performance of our new method with several other traditional modeling strategies, including Free-Wilson analysis. Our results show that the R-group signature SVM models achieve better prediction accuracy compared with Free-Wilson analysis in general. Moreover, the predictions of R-group signature models are also comparable to the models using ECFP6 fingerprints and signatures for the whole compound. Most importantly, R-group contributions to the SVM model can be obtained by calculating the gradient for R-group signatures. For most of the studied data sets, a significant correlation with that of a corresponding Free-Wilson analysis is shown. These results suggest that the R-group contribution can be used to interpret bioactivity data and highlight that the R-group signature based SVM modeling method is as interpretable as Free-Wilson analysis. Hence the signature SVM model can be a useful modeling tool for any drug discovery project. %I ACS Publications