CADASTER QSPR Models for Predictions of Melting and Boiling Points of Perfluorinated Chemicals

Abstract

Quantitative structure property relationship (QSPR) studies on per- and polyfluorinated chemicals (PFCs) on melting point (MP) and boiling point (BP) are presented. The training and prediction chemicals used for developing and validating the models were selected from Syracuse PhysProp database and literatures. The available experimental data sets were split in two different ways: a) random selection on response value, and b) structural similarity verified by self-organizing-map (SOM), in order to propose reliable predictive models, developed only on the training sets and externally verified on the prediction sets. Individual linear and non-linear approaches based models developed by different CADASTER partners on 0D-2D Dragon descriptors, E-state descriptors and fragment based descriptors as well as consensus model and their predictions are presented. In addition, the predictive performance of the developed models was verified on a blind external validation set (EV-set) prepared using PERFORCE database on 15 MP and 25 BP data respectively. This database contains only long chain perfluoro-alkylated chemicals, particularly monitored by regulatory agencies like US-EPA and EU-REACH. QSPR models with internal and external validation on two different external prediction/validation sets and study of applicability-domain highlighting the robustness and high accuracy of the models are discussed. Finally, MPs for additional 303 PFCs and BPs for 271 PFCs were predicted for which experimental measurements are unknown.

Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks

“…Machine learning approaches are typically used to map the structure of compounds to their properties, a method called quantitative structure-activity relationship (QSAR). Common algorithms include multiple linear regression, random forest or support vector machine in combination with circular fingerprints or molecular properties to describe the molecules [3][4][5][6][7]. Water solubility and melting point are two endpoints for which a lot of previous modeling was published [3][4][5][8][9][10][11][12][13].…”

In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning

“…32–35 Numerous computational approaches have been proposed to construct QSPR models, and these methods can be generally categorized into three classes: models based on other experimentally determined physicochemical properties; 35–37 models based on calculated molecular descriptors; 32–34,38 and models based on group contributions. 39–43 The third approach was the foundation of initial work in QSPR development.…”

Global and local quantitative structure–property relationship models to predict the impact sensitivity of nitro compounds

“…Computer capabilities are in constant increase, and developing accurate predictive methods to estimate the properties of such compounds is now possible. In particular, quantitative structure-property relationships (QSPR) are increasingly used to predict properties of chemicals [3][4][5][6][7][8][9][10]. This approach consists in correlating an experimental property with descriptors of the molecular structure.…”