Use of quantitative-structure property relationship (QSPR) and artificial neural network (ANN) based approaches for estimating the octanol-water partition coefficients of the 209 chlorinated trans-azobenzene congeners

Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches

Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.

A complete vibrational study on a potential environmental toxicant agent, the 3,3',4,4'-tetrachloroazobenzene combining the FTIR, FTRaman, UV-Visible and NMR spectroscopies with DFT calculations

In this study 3,3',4,4'-tetrachloroazobenzene (TCAB) was prepared and then characterized by infrared, Raman, multidimensional nuclear magnetic resonance (NMR) and ultraviolet-visible spectroscopies. The density functional theory (DFT) together with the 6-31G(*) and 6-311++G(**) basis sets were used to study the structures and vibrational properties of the two cis and trans isomers of TCAB. The harmonic vibrational wavenumbers for the optimized geometries were calculated at the same theory levels. A complete assignment of all the observed bands in the vibrational spectra of TCAB was performed combining the DFT calculations with the scaled quantum mechanical force field (SQMFF) methodology. The molecular electrostatic potentials, atomic charges, bond orders and frontier orbitals for the two isomers of TCAB were compared and analyzed. The comparison of the theoretical ultraviolet-visible spectrum with the corresponding experimental demonstrates a good concordance while the calculated (1)H and (13)C chemicals shifts are in good conformity with the corresponding experimental NMR spectra of TCAB in solution. The npp(*) transitions for both forms were studied by natural bond orbital (NBO) while the topological properties were calculated by employing Bader's Atoms in the Molecules (AIM) theory. This study shows that the cis and trans isomers exhibit different structural and vibrational properties and absorption bands.

QSPR modeling of n-octanol/air partition coefficients and liquid vapor pressures of polychlorinated dibenzo-p-dioxins

The molecular geometries of 75 polychlorinated dibenzo-p-dioxins (PCDDs) were optimized using B3LYP/6-31G(*) method. The calculated structural parameters were taken as theoretical descriptors to establish two new novel QSPR models for n-octanol/air partition coefficients (log K(OA)) and subcooled liquid vapor pressure (log P(L)) of PCDDs. The R(2) values of the two models are 0.983 and 0.985 respectively. Their standard deviations of prediction in modeling (SD) are 0.174 and 0.230 respectively. The results of leave-one-out (LOO) cross-validation for training set show that the two models exhibited optimum stability and good predictive power.

The aqueous solubility of some herbicidal by-side toxic impurities: predicted data of the 399 chlorinated trans-azoxybenzene congeners

The quantitative structure - property relationship (QSPR) and the artificial neural networks (ANNs) methods were used to estimate aqueous solubility (log S and μg/L) of polychlorinated trans-azoxybenzenes (PCt-ABs). These QSPR and ANN models are based on geometry optimalization and quantum-chemical structural descriptors, which were computed on the level of density functional theory (DFT) using B3LYP functional and 6-311++G** basis set in Gaussian 03 software and the semi-empirical quantum chemistry method for property parameterization (RM1) in the molecular orbital package (MOPAC) software. The predicted solubility of PCt-AOBs by RM1 and DFT models and depending on a congener varied within a homologue class between 47-19498 and 371-1738 μg/L for Mono-; 33-11481 and 7.9-3630 μg/L for Di-; 6.1-4786 and 4.7-12882 μg/L for Tri-; 1.3-1174 and 0.3-14791 μg/L for Tetra-; 0.4-646 and 0.1-38904 μg/L for Penta-; 0.1-155 and 0.2-63096 μg/L for Hexa-; 0.2-27 and 0.1-646 μg/L for Hepta-; < 0.1-6.2 and 0.8-282 μg/L for Octa-; 0.6-2.6 and 0.8-12 μg/L for NonaCt-AOBs; and 1.2 and 0.5 μg/L for DecaCt-AOB, respectively. Both computational models used were characterized by good predictive abilities and small errors, while calculations by RM1 method were highly competitive compared to a much more time-consuming and expensive method by DFT.

Estimation of K(OA) values of 209 polychlorinated trans-azobenzenes by PM6 and DFT methods

The octanol-air partition coefficients (K(OA)) of all 209 PCt-ABs were determined computationally to fill gaps on their environmentally relevant physical and chemical properties. These properties have been determined using two computational approaches: the semi-empirical quantum chemistry method for property parameterization (PM6) of the molecular orbital package (MOPAC) and density functional theory (DFT) method using B3LYP functional and 6-311++G** basis set in Gaussian 03 software and artificial neural network (ANN) predicting abilities. Both computational methods enabled estimation of log K(OA) partition coefficients of PCt-ABs with a similar accuracy and precision. The PM6 method compared to DFT was highly superior because it requires much less time, manpower and cost of hardware. The determined log K(OA) values of the investigated PCt-ABs for standard condition (25 °C) varied between 8.30 and 8.75 for Mono-; 8.71 and 9.92 for Di-; 9.58 and 10.72 for Tri-; 10.11 and 11.34 for Tetra-, 10.83 and 11.85 for Penta-; 11.24 and 12.36 for Hexa-; 11.87 and 12.66 for Hepta-; 12.31 and 12.97 for Octa-; 12.89 and 13.21 for Nona-Ct-ABs; and 13.17- and 13.49 for Deca-Ct-AB. PCt-ABs, in view of these log K(OA) values, can be classified as compounds of relatively low (Mono-, Di- and some of Tri- Ct-ABs with values of log K(OA) around 8 to 10) environmental mobility (most of Tri- to Nona-Ct-ABs and Deca-Ct-AB homologues with values of log K(OA) >10), and with a potential to be adsorbed by soil particles.

QSPR for prediction of subcooled vapor pressures (log PL) of polychlorinated trans-azobenzenes

In this study the values of subcooled vapor pressures (log P(L)) were estimated for 209 trans chloroazobenzenes (Ct-ABs) that fill some gaps in analytical and experimental data on these compounds. There are 209 chloro derivatives of trans azobenzenes that are relatively stable and more environmentally relevant than 209 chloro cis congeners. The calculations models were based on the Quantitative Structure-Property Relationship (QSPR) scheme using the semi-empirical method (PM6) in molecular package (MOPAC) software and density functional theory (DFT) method using B3LYP functional and 6-311++G** basis set) in Gaussian 03 software method and the artificial neural networks (ANNs) prediction. The values of log P(L) predicted by models used varied between -3.94 to -2.66 for Mono-; -4.85 to -2.97 for Di-; -5.18 to -3.17 for Tri-; -6.02 to -3.77 for Tetra-; -6.64 to -4.64 for Penta-; -7.36 to -4.76 for Hexa-; -7.54 to -5.79 for Hepta-; -7.75 to -6.64 for Octa-; -7.89 to -7.44 for Nona-Ct-Abs; and -8.09 and -8.13 for Deca-Ct-AB. Based on these values Ct-ABs can be grouped localized among relatively low (log P(L) -4 to -2) and low (log P(L) < -4) mobile Persistent Organic Pollutants (POPs). Both the calculation methods employed were characterized by similar prediction ability of subcooled vapor pressure values of Ct-ABs, while those of PM6 are much more efficient due to a cheaper hardware used and around 300-fold less time spent on calculations.