title: Beyond the scope of Free-Wilson analysis: building interpretable QSAR models with machine learning algorithms.
abstract: 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.
authors.affiliation: Chemistry Innovation Center, Discovery Sciences, AstraZeneca R&D Mölndal, Sweden. hongming.chen@astrazeneca.com
authors.forename: Hongming
authors.initials: H
authors.lastname: Chen
authors.forename: Lars
authors.initials: L
authors.lastname: Carlsson
authors.forename: Mats
authors.initials: M
authors.lastname: Eriksson
authors.forename: Peter
authors.initials: P
authors.lastname: Varkonyi
authors.forename: Ulf
authors.initials: U
authors.lastname: Norinder
authors.forename: Ingemar
authors.initials: I
authors.lastname: Nilsson
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D055808:Drug Discovery; D004364:Pharmaceutical Preparations / Q000737:chemistry; D010600:Pharmacology; D021281:Quantitative Structure-Activity Relationship
chemical_list: D004364:Pharmaceutical Preparations