QSPR study on refractive indices of solvents commonly used in polymer chemistry using flexible molecular descriptors

Fioressi, Silvina E.; Bacelo, Daniel E.; Cui, Weigang; Saavedra, Laura; Duchowicz, Pablo R.

doi:10.1080/1062936x.2015.1064472

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…Despite the much larger number of descriptors than for Katritzky's QSPR model, the improvement in fit was rather small, especially since the Katristky 125-liquid dataset contained more complex molecules, e.g., with multiple cycles. Several other QSPR models based on molecular descriptors, limited to sets of a few hundred compounds, have been proposed for refractive index prediction [13][14][15][16][17]. For datasets that are not only hydrocarbons, the best results were obtained with a model based on associative neural networks.…”

Section: Introductionmentioning

confidence: 99%

Fast and Accurate Prediction of Refractive Index of Organic Liquids with Graph Machines

Duprat,

Ploix,

Aubry

et al. 2023

Molecules

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The refractive index (RI) of liquids is a key physical property of molecular compounds and materials. In addition to its ubiquitous role in physics, it is also exploited to impart specific optical properties (transparency, opacity, and gloss) to materials and various end-use products. Since few methods exist to accurately estimate this property, we have designed a graph machine model (GMM) capable of predicting the RI of liquid organic compounds containing up to 16 different types of atoms and effective in discriminating between stereoisomers. Using 8267 carefully checked RI values from the literature and the corresponding 2D organic structures, the GMM provides a training root mean square relative error of less than 0.5%, i.e., an RMSE of 0.004 for the estimation of the refractive index of the 8267 compounds. The GMM predictive ability is also compared to that obtained by several fragment-based approaches. Finally, a Docker-based tool is proposed to predict the RI of organic compounds solely from their SMILES code. The GMM developed is easy to apply, as shown by the video tutorials provided on YouTube.

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Section: Introductionmentioning

confidence: 99%

Fast and Accurate Prediction of Refractive Index of Organic Liquids with Graph Machines

Duprat,

Ploix,

Aubry

et al. 2023

Molecules

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…The OCWLGI has been tested as a tool to build up quantitative structure‐property/activity relationships (QSPRs/QSARs) with various endpoints …”

Section: Introductionmentioning

confidence: 99%

Does the Index of Ideality of Correlation Detect the Better Model Correctly?

Toropova

Toropov

2019

Molecular Informatics

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This paper is dedicated to Prof. Paola Gramatica on the occasion of her retirement. Abstract:The CORAL software is a tool to build up predictive models for various endpoints by means of Quantitative Structure-Property/Activity Relationships (QSPRs/QSARs). A new criterion for assessment of the predictive potential of QSPR/QSAR models, so-called Index of Ideality of Correlation (IIC) is applied to improve the software. The ability of the IIC to detect models with better predictive potential is checked up with groups of random splits of data into the structured training set and extrenal validation set. To this end, two endpoints are examined (i) Toxicity towards Fathead minnow (Pimephales promelas); and (ii) drug load capasity of samples "micelle-polymer". Applications of the IIC for endpoint represented by traditional Simplified Molecular Input-Line Entry System (SMILES) together with so-called quasi-SMILES has shown the suitability of the IIC be a tool to detect better model.

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QSPR study on refractive indices of solvents commonly used in polymer chemistry using flexible molecular descriptors

Cited by 8 publications

References 27 publications

Fast and Accurate Prediction of Refractive Index of Organic Liquids with Graph Machines

Fast and Accurate Prediction of Refractive Index of Organic Liquids with Graph Machines

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

Does the Index of Ideality of Correlation Detect the Better Model Correctly?

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