Internal Test Set (Its) Method: A New Cross-Validation Technique to Assess the Predictive Capability of Qsar Models. Application to a Benchmark Set of Steroids

Abstract

A new internal cross-validation method is presented for assessing the true predictive capability of QSAR models. The test is general and can be applied in many QSAR/QSPR approaches. In this work, the method is tested on a well-known benchmark set of steroids. In order to make the calculations, Topological Quantum Similarity Indices and Multiple Linear Regression models were considered.

Fast Modeling of Binding Affinities by Means of Superposing Significant Interaction Rules (SSIR) Method

“…Both tests are iterative and require the generation of predictions either for single analogues (L1O) or pairs of them (BL2O). All the L1O and BL2O cross-validation cycles have been designed according the Internal Test Sets (ITS) method [ 24 , 25 , 26 ] consisting of generating all the models from the beginning, i.e. , selecting all the rules from scratch as if the left out cross-validated analogue(s) were not present in the original library.…”

Superposing Significant Interaction Rules (SSIR) Method: A simple Procedure for Rapid Ranking of Congeneric Compounds

“…Our code is always based on the Internal Test Sets (ITS) paradigm. [17,[25][26][27][28] This means that every cross-validated training to be done over a subset must start from scratch and the variable selection must be totally independent of the other cross-validation loops or subsequent prediction over a test or validation set. This is accomplished by the above algorithm because the training and the model application are done in distinct molecular and mutually exclusive sets.…”