title: Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters.
abstract: Recent implementations of QSAR modelling software provide the user with numerous models and a wealth of information. In this work, we provide some guidance on how one should interpret the results of QSAR modelling, compare and assess the resulting models, and select the best and most consistent ones. Two QSAR datasets are applied as case studies for the comparison of model performance parameters and model selection methods. We demonstrate the capabilities of sum of ranking differences (SRD) in model selection and ranking, and identify the best performance indicators and models. While the exchange of the original training and (external) test sets does not affect the ranking of performance parameters, it provides improved models in certain cases (despite the lower number of molecules in the training set). Performance parameters for external validation are substantially separated from the other merits in SRD analyses, highlighting their value in data fusion.
authors.affiliation: a Plasma Chemistry Research Group , Hungarian Academy of Sciences , Budapest , Hungary.|b Department of Applied Chemistry , Corvinus University of Budapest , Budapest , Hungary.
authors.forename: A
authors.initials: A
authors.lastname: Rácz
authors.affiliation: c Medicinal Chemistry Research Group , Hungarian Academy of Sciences , Budapest , Hungary.
authors.forename: D
authors.initials: D
authors.lastname: Bajusz
authors.affiliation: a Plasma Chemistry Research Group , Hungarian Academy of Sciences , Budapest , Hungary.
authors.forename: K
authors.initials: K
authors.lastname: Héberger
mesh_terms: D000581:Amidohydrolases / Q000037:antagonists & inhibitors / Q000737:chemistry; D000818:Animals; D001555:Benzene Derivatives / Q000737:chemistry* / Q000633:toxicity; D003530:Cyprinidae; D003661:Decision Support Techniques; D006801:Humans; D008301:Maleimides / Q000737:chemistry* / Q000633:toxicity; D015233:Models, Statistical; D062105:Molecular Docking Simulation; D008994:Monoacylglycerol Lipases / Q000037:antagonists & inhibitors / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship; D012984:Software; D000099252:Fatty Acid Amide Hydrolases
chemical_list: D000581:Amidohydrolases; D001555:Benzene Derivatives; D008301:Maleimides; D008994:Monoacylglycerol Lipases; D000099252:Fatty Acid Amide Hydrolases
keywords: cross-validation; model selection; performance parameters; ranking; sum of ranking differences