Development of Quantitative Structure-Property Relationships (QSPR) using calculated descriptors for the prediction of the physico-chemical properties (nD, r, bp, e and h) of a series of organic solvents.

Quantitative structure-property relationship (QSPR) models were derived for predicting boiling point (at 760 mmHg), density (at 25 \ub0C), viscosity (at 25 \ub0C), static dielectric constant (at 25 \ub0C), and refractive index (at 20 \ub0C) of a series of pure organic solvents of structural formula X-CH2CH2-Y. A very large number of calculated molecular descriptors were derived by quantum chemical methods, molecular topology, and molecular geometry by using the CODESSA software package. A comparative analysis of the multiple linear regression techniques (heuristic and best multilinear regression) implemented in CODESSA, with the multivariate PLS/GOLPE method, has been carried out. The performance of the different regression models has been evaluated by the standard deviation of prediction errors, calculated for the compounds of both the training set (internal validation) and the test set (external validation). Satisfactory QSPR models, from both predictive and interpretative point of views, have been obtained for all the studied properties

Usporedba QSPR modela zasnovanih na vodikom-popunjenim molekularnim grafovima i na grafovima atomskih orbitala

QSPR models are studied for normal boiling points of alkanes, alkylbenzenes, and polyaromatic hydrocarbons, in terms of optimized correlation weights of local invariants of the hydrogen- -filled graphs (HFGs) and of the graphs of atomic orbitals (GAOs). Morgan extended connectivities of the zeroth, first, and second order of the HFGs and GAOs were employed. The best QSPR model obtained is based on optimized correlation weights of the extended connectivity of the first order of the GAO. The statistical characteristics of this model are: n = 70, r superscript(2) = 0.9988, s = 5.8 °C, F = 57437 (training set); n = 70, r superscript(2) = 0.9985, s = 6.7 °C, F = 45154 (test set).Istraživani su QSPR modeli za normalnu točku vrelišta alkana, alkilbenzena i poliaromatskih ugljikovodika, zasnovani na optimiziranim korelacijskim te`inama lokalnih invarijanti vodikom-popunjenih molekularnih grafova (HFG) i grafova atomskih orbitala (GAO). Primjenjeni su Morganovi indeksi proširene povezanosti nultoga, prvoga i drugoga reda, kako za HFG tako i za GAO. Najbolji QSPR model je dobiven na osnovi optimiziranih korelacijskih težina za proširenu povezanost prvoga reda za GAO. Statističke karakteristike ovoga modela su: n = 70, r superscript(2) = 0.9988, s = 5.8 °C, F = 57437 (training set); n = 70, r superscript(2) = 0.9985, s = 6.7 °C, F = 45154 (test set)

QSPR Models for Prediction of Aqueous Solubility: Exploring the Potency of Randić-type Indices

The development of QSPR models to predict aqueous solubility (logS) is presented. A structurally diverse set of over 1600 compounds with experimentally determined solubility values (AqSolDB database) is used for building the data-driven models based on multiple linear regression (MLR) and artificial neural network (ANN) methods to predict aqueous solubility. Molecular structures are encoded by numerous structural descriptors, including the connectivity index developed by Randić in 1975, and many later derived variations. To evaluate the potency of Randić-like descriptors in the structure-property relationship, we developed models based on two sets of descriptors, first using only Randić-like descriptors calculated with Dragon, and second using 17 commonly applied descriptors available in the AqSolDB database. All models were validated with external prediction sets, with the RMSE ranging from 0.8 to 1.1. Interestingly, the RMSE of predicted LogS values of models based only on the Randić-like descriptors were in average just 0.1 larger than the models with 17 descriptors preselected as suitable for modelling logS. This work is licensed under a Creative Commons Attribution 4.0 International License

Prediction of physico-chemical properties for REACH based on QSPR models

International audienceQuantitative Structure Property Relationship models have been developed for the prediction of flash points of two families of organic compounds selected in the PREDIMOL French Project: amines and organic peroxides. If the model dedicated to amines respected all OECD validation principles with excellent performances in predictivity, the one dedicated to organic peroxides was not validated on an external validation set, due to the low number of available data, but already presented high performances in fitting and robustness. This work highlighted the need of gathering experimental data, as in progress in the PREDIMOL project, to achieve validated reliable models that could be used in a regulatory framework, like REACH. Such models are expected to be submitted to the European Joint Research Comity (JRC) and to existing tools (like the OECD ECHA QSAR Toolbox) to be available for use by industrials and regulatory instances