In this article, we consider cross-validation of the quantitative structure-property relationship models for reactions and show that conventional k-fold (CV) procedure gives an ‘optimistically’ biased assessment prediction performance. To address issue, suggest two strategies model cross-validation, ‘transformation-out’ CV, ‘solvent-out’ CV. Unlike approach does not nature objects, proposed pro...

Quantitative structure-property relationships (QSPR) of critical temperatures with small numbers of physically significant molecular descriptors are developed using the CODESSA (comprehensive descriptors for structural and statistical analysis) technique. A highly significant one-parameter model correlates to the critical temperatures of 76 hydrocarbons at R2 ) 0.953. A successful three-paramet...

• QSPR models were developed based on in vitro corneal permeability measurements of 189 compounds. Partial least squares regression was applied for the model building. Classical molecular descriptors and ECFP fingerprints used. Corneal membrane retention values predicted by models. The capability to predict physicochemical parameters has always been a desirable objective ophthalmic drug develop...

We present a novel probabilistic mean quantitative structure–property relationship (M-QSPR) method for the prediction of jet fuel properties considering two-dimensional gas chromatography measurements. Fuels are represented as one pseudo-structure that is inferred by weighted average over structures 1866 molecules could be in individual fuel. The allows training models on both data pure compone...

A greater number of toxicity data are becoming publicly available allowing for in silico modeling. However, questions often arise as to how to incorporate data quality and how to deal with contradicting data if more than a single datum point is available for the same compound. In this study, two well-known and studied QSAR/QSPR models for skin permeability and aquatic toxicology have been inves...

The search for multivariate linear regression (MLR) in quantitative structure-property relationships (QSPR) is a hard problem, due to the dimension of the entire search space. A genetic algorithm (GA) was developed and assessed, to select proper descriptors for predicting the octan-1-ol/H2O partition coefficient of polychlorinated biphenyls. The GA was implemented as a Windows based FreePascal ...