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QSPR modelling of dielectric constants of π-conjugated organic compounds by means of the CORAL software
Copyright policy )QSPR modelling of dielectric constants of π-conjugated organic compounds by means of the CORAL software
Simplified molecular input line entry system (SMILES) notations of 116 π-conjugated organic compounds have been used in three random splits to develop single optimal descriptor based quantitative structure–property relationships (QSPR) models for the prediction of dielectric constants by CORAL (CORrelation And Logic). Four kinds of optimal descriptors were obtained based on SMILES, hydrogen suppressed graph (HSG), graph of atomic orbitals (GAO) and hybrid descriptors. The Monte Carlo optimization was carried out for each random split by three different methods: (i) classic scheme; (ii) balance of correlations; and (iii) balance of correlations with ideal slopes. The QSPR models gave reliable and accurate values of dielectric constant for all the π-conjugated organic compounds. SMILES and the hybrid-based QSPR model provided the best accuracy for the prediction of dielectric constants. Statistical characteristics of the QSPR model-1 based on classic scheme method are n = 110, r2 = 0.860, Q2 = 0.860, s = 1.84, MAE = 1.30 and F = 696 (training set), n = 6, r2 = 0.947, Q2 = 0.876, s = 0.955, MAE = 0.647 and F = 71 (test set). These QSPR models are further validated by an external validation set of 25 molecules and the robustness is checked by parameters like k, kk, rm2, r*m2, average rm2, ∆rm2and randomization technique ().
Microsoft Academic Graph classification: Quantitative structure–activity relationship Simplified molecular-input line-entry system Monte carlo optimization Thermodynamics Dielectric Conjugated system Software Atomic orbital Computational chemistry Chemistry business.industry computer.file_format Graph (abstract data type) business computer
Models, Molecular, Quantitative Structure-Activity Relationship, Bioengineering, Drug Discovery, Computer Simulation, Organic Chemicals, Molecular Structure, General Medicine, Molecular Medicine, Monte Carlo Method, Software
Models, Molecular, Quantitative Structure-Activity Relationship, Bioengineering, Drug Discovery, Computer Simulation, Organic Chemicals, Molecular Structure, General Medicine, Molecular Medicine, Monte Carlo Method, Software
Microsoft Academic Graph classification: Quantitative structure–activity relationship Simplified molecular-input line-entry system Monte carlo optimization Thermodynamics Dielectric Conjugated system Software Atomic orbital Computational chemistry Chemistry business.industry computer.file_format Graph (abstract data type) business computer
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Citations provided by BIP!Popularity provided by BIP!Simplified molecular input line entry system (SMILES) notations of 116 π-conjugated organic compounds have been used in three random splits to develop single optimal descriptor based quantitative structure–property relationships (QSPR) models for the prediction of dielectric constants by CORAL (CORrelation And Logic). Four kinds of optimal descriptors were obtained based on SMILES, hydrogen suppressed graph (HSG), graph of atomic orbitals (GAO) and hybrid descriptors. The Monte Carlo optimization was carried out for each random split by three different methods: (i) classic scheme; (ii) balance of correlations; and (iii) balance of correlations with ideal slopes. The QSPR models gave reliable and accurate values of dielectric constant for all the π-conjugated organic compounds. SMILES and the hybrid-based QSPR model provided the best accuracy for the prediction of dielectric constants. Statistical characteristics of the QSPR model-1 based on classic scheme method are n = 110, r2 = 0.860, Q2 = 0.860, s = 1.84, MAE = 1.30 and F = 696 (training set), n = 6, r2 = 0.947, Q2 = 0.876, s = 0.955, MAE = 0.647 and F = 71 (test set). These QSPR models are further validated by an external validation set of 25 molecules and the robustness is checked by parameters like k, kk, rm2, r*m2, average rm2, ∆rm2and randomization technique ().