y-Randomization and Its Variants in QSPR/QSAR

By C. Rücker, Gerta Rücker, M. Meringer. Journal of Chemical Information and Modeling, 2007

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A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking (2010, positive): “Such y-scrambling validation is very useful in the validation of QSAR studies (e.g. Rucker et al., 2007), where an optimal set of features is selected over a very large pool Fig.”
Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles (2020, background): “This y-randomization [29] validation shows what proportion of the model accuracy comes from the signal in the data.”
3D-QSAR in drug design--a review. (2010, methodology): “A rigorous alternative to cross-validation and bootstrapping is randomization or y-scrambling in which the biological activity values are re-assigned arbitrarily to different molecules in the same data set, and a new regression is performed [120].”
Machine learning methods in chemoinformatics (2014, methodology): “99,102 The real model is compared with alternative models, which are generated from datasets in which the property values y are repeatedly randomly E u r op e P M C F und e r s A u t ho r M a nu s c r i p t s E u r op e P M C F und e r s A u t ho r M a nu s c r i p t s reassigned amongst the…”