Conformation-independent quantitative structure-property relationships study on water solubility of pesticides

Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks

“…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].…”

Monte Carlo based QSGFEAR: prediction of Gibb's free energy of activation at different temperatures using SMILES based descriptors

“…20 Rigorous validation and a well-defined applicability domain are the critical components for the future success of a QSPR/QSAR model, regardless of model structure or fitting technique. 21,22 QSPR/QSAR modelling plays a significant role in predicting various physicochemical properties 23 that is evident from the numerous reports available in the literature such as pK a , 24 aqueous solubility, 25 melting point, 26,27 flammability limit, 28 soil sorption coefficient, 29 refractive indices, 30,31 adsorption coefficient, 32 octanol-air partition coefficients, 33 Henry's law constant, 34 dielectric constant, 35 critical temperature, decomposition temperature, 36,37 thermal conductivity, 38 viscosity, enthalpy of vaporisation, 39 Gibbs free energy of solvation 40 and sublimation, 41 enthalpy of formation, 42 heat capacity (C) at variable temperatures 43 and many others. Gibb's free energy of activation (DG ‡ ) is an important physicochemical property for dynamic systems and QSPR/QSAR provides an accurate, robust and reliable mathematical equation to predict its values.…”

Foodinformatic prediction of the retention time of pesticide residues detected in fruits and vegetables using UHPLC/ESI Q-Orbitrap

“…Thus, several QSPR studies were reported in the literature to predict the t R of pesticide residues (Dashtbozorgi et al, 2013;Torrens & Castellano, 2014;Zdravković et al, 2018). Our research group has also been interested in QSPR studies for the prediction of chromatographic retention indices in the field of food science (foodinformatics) (Rojas et al, 2019;Rojas et al, 2018), as well as the in silico modeling of the water solubility of pesticides (Fioressi et al, 2019).…”