Quantitative Structure-property Relationship Approach Research Articles

Predicting the properties of deep eutectic solvents based on choline chloride and carboxylic acids and their mixtures with water using QSPR approach

Deep eutectic solvents (DES) are increasingly popular materials in various fields of chemistry. The set of properties of a particular DES determines the range of its applicability for a certain task. Tailoring these properties to meet the needs of various applications can be tedious, as it may require a large amount of experimental work. Here we propose the use of quantitative structure-property relationship (QSPR) approach to model DES properties. Using choline chloride and six carboxylic acids as a case study we attempted to construct regression models relating molecular descriptors of carboxylic acids and the properties of corresponding solvents. Statistically meaningful correlations were established between the descriptors of the acids and density, viscosity and conductivity of DES. The impact of different descriptors on these properties was analyzed based on the regression coefficients. Taking into account rather limited dataset a rigorous validation of the regression models was performed using double loop cross-validation.

Neural network-based prediction of auto-ignition temperature of ternary mixed liquids

Auto-ignition temperature (AIT) is one of the crucial exponents in the design of fire and explosion safety measures. Therefore, in this study, quantitative structure-property relationship approach was used to predict the AIT of ternary hybrid liquids based on molecular structure information. The optimal molecular descriptors were calculated and filtered using Mordred software. Twelve mixing rules were proposed for calculating molecular descriptors of mixtures. A prediction model for the AIT value of binary liquid mixtures was developed, validated and evaluated using a back propagation neural network (BPNN) and a one-dimensional convolutional neural network (1DCNN). The relative contribution and positive and negative correlations between individual molecular descriptors and AIT in the model were interpreted using the shapley additive explanations method. The results show that BPNN and 1DCNN models using mixing rule 1 have the best fitting ability, stability and prediction ability. The determination coefficient of the BPNN and 1DCNN models in the training set were 0.996 and 0.992, the root mean square errors were 3.613 °C and 5.284 °C, the mean absolute errors were 2.483 °C and 4.144 °C, the nash efficiency coefficient was 0.996 and 0.992, respectively, the willmott index was 0.999 and 0.998. and the values of the top three molecular descriptors of relative contribution, SssCH2, SsOH and SsCH3, were negatively correlated with the AIT values. The BPNN and 1DCNN models provide an accurate and reliable method for predicting ternary mixing liquid AIT.

Valency based novel quantitative structure property relationship (QSPR) approach for predicting physical properties of polycyclic chemical compounds

In this study, we introduce a novel valency-based index, the neighborhood face index (NFI), designed to characterize the structural attributes of benzenoid hydrocarbons. To assess the practical applicability of NFI, we conducted a linear regression analysis utilizing numerous physiochemical properties associated with benzenoid hydrocarbons. Remarkably, the results revealed an extraordinary correlation exceeding 0.9991 between NFI and these properties, underscoring the robust predictive capability of the index. The NFI, identified as the best-performing descriptor, is subsequently investigated within certain infinite families of carbon nanotubes. This analysis demonstrates the index’s exceptional predictive accuracy, suggesting its potential as a versatile tool for characterizing and predicting properties across diverse molecular structures, particularly in the context of carbon nanotubes.

Symmetry-Adapted Domination Indices: The Enhanced Domination Sigma Index and Its Applications in QSPR Studies of Octane and Its Isomers

Molecular descriptors are essential in mathematical chemistry for studying quantitative structure–property relationships (QSPRs), and topological indices are a valuable source of information about molecular properties, such as size, cyclicity, branching degree, and symmetry. Graph theory has played a crucial role in the development of topological indices and dominating parameters for molecular descriptors. A molecule graph, under graph isomorphism conditions, represents an invariant number, and the graph theory approach considers dominating sets, which are subsets of the vertex set where every vertex outside the set is adjacent to at least one vertex inside the set. The dominating sigma index, a topological index that incorporates the mathematical principles of domination topological indices and the sigma index, is applicable to some families of graphs, such as book graphs and windmill graphs, and some graph operations, which have exact values for this new index. To evaluate the effectiveness of the domination sigma index in QSPR studies, a comparative analysis was conducted to establish an appropriate domination index that correlates with the physicochemical properties of octane and its isomers. Linear and non-linear models were developed using the QSPR approach to predict the properties of interest, and the results show that both the domination forgotten and domination first Zagreb indices exhibited satisfactory performance in comparison testing. Further research into QSAR/QSPR domination indices is required to build more robust models for predicting the physicochemical properties of organic compounds while maintaining the importance of symmetry.

Exploring the Symmetry of Curvilinear Regression Models for Enhancing the Analysis of Fibrates Drug Activity through Molecular Descriptors

Quantitative structure-property relationship (QSPR) modeling is crucial in cheminformatics and computational drug discovery for predicting the activity of compounds. Topological indices are a popular molecular descriptor in QSPR modeling due to their ability to concisely capture the structural and electronic properties of molecules. Here, we investigate the use of curvilinear regression models to analyze fibrates drug activity through topological indices, which modulate lipid metabolism and improve the lipid profile. Our QSPR approach predicts the physicochemical properties of fibrates based on degrees and distances from topological indices. Our results demonstrate that topological indices can enhance the accuracy of predicting physicochemical properties and biological activities of molecules, including drugs. We also conducted density functional theory (DFT) calculations on the investigated derivatives to gain insights into their optimized geometries and electronic properties, including symmetry. The use of topological indices in QSPR modeling, which considers the symmetry of molecules, shows significant potential in improving our understanding of the structural and electronic properties of compounds.

Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design

Abstract Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. The present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity (dOS)), and using ANNs trained with QSPR descriptors to directly predict OS. Twenty-five ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. Twenty-five additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.

Parallel screening and cheminformatics modeling of flavonoid activated aptasensors

A parallel screening of 27 different flavonoids and chalcones was conducted using 6 artificial naringenin-activated riboswitches (M1, M2, M3, O, L and H). A quantitative structure-property relationship approach was applied to understand the physicochemical properties of the flavonoid structures resulting in specificity differences relied on the fluorescence intensity of a green fluorescent protein reporter. Robust models of riboswitches M1, M2 and O that had good predictive power were constructed with descriptors selected for their high correlation. Increased electronegativity and hydrophilicity of the flavonoids structures were identified as two properties that increased binding affinity to RNA riboswitches. Hydroxyl groups at the C-3′ and C-4’ positions of the flavonoid molecule were strictly required for ligand-activation with riboswitches M1 and M2. Riboswitches O and L preferred multi-hydroxylated flavones as ligands. Substitutions on the A ring of the flavonoid molecule were not important in the molecular recognition process. O-glycosylated derivatives were not recognized by any of the riboswitches, presumably due to steric hindrances. Despite the challenges of detecting RNA conformational change after ligand binding, the resulting models elucidate important physicochemical features in the ligands for conformational structural studies of artificial aptamer complexes and for design of ligands having higher binding specificity.

A simple, robust, and efficient structural model to predict thermal stability of zinc metal-organic frameworks (Zn-MOFs): The QSPR approach

The thermal stability of MOFs refers to resist degradation of their structure, upon exposure to heat. It may depend on the type of linkers, metal SBUs, metal-ligand interaction, and type of packing of MOF. Many applications of MOF compounds require high thermal stability so that their frameworks do not collapse upon thermal treatment. A straightforward method based on a Quantitative Structure-Property Relationship (QSPR) was established to achieve a precise model to estimate the thermal stability of MOF compounds having zinc SBUs due to their importance in recent researches. The proposed model was developed based on descriptors including the number of nitrogen and zinc atoms, the interactions between heteroatoms of linkers and metal centers, and various molecular fragments. A logarithm of experimental thermal stability (log TS) of 151 MOFs is applied to develop a precise model. The coefficients of determination (R 2 ) for the training and test sets were obtained 0.999 and 1.000, respectively. The Root Mean Square Error for Prediction (RMSEP) of log TS of training and test sets are 0.003 and 0.001, respectively. According to resulted statistical parameters, the novel model is robust and efficient satisfactory. • The thermal stability of metal-organic frameworks (MOFs) is one of the principal concerns for various applications. • A QSPR model was developed to simply predict the relationship between thermal stability and the Zn-MOF SBUs. • The QSPR approach was reliable, robust, and applicable for Zn-MOFs with diverse linkers. • Considering the resulted multilinear regression model, MOF compounds can be well designed with suitable thermal stability. • The presented QSPR model will be developed to estimate other physicochemical properties of MOFs from their experimental data.

A Descriptor Set for Quantitative Structure-property Relationship Prediction in Biologics.

There has been a remarkable increase in the number of biologics, especially monoclonal antibodies, in the market over the last decade. In addition to attaining the desired binding to their targets, a crucial aspect is the 'developability' of these drugs, which includes several desirable properties such as high solubility, low viscosity and aggregation, physico-chemical stability, low immunogenicity and low poly-specificity. The lack of any of these desirable properties can lead to significant hurdles in advancing them to the clinic and are often discovered only during late stages of drug development. Hence, in silico methods for early detection of these properties, particularly the ones that affect aggregation and solubility in the earlier stages can be highly beneficial. We have developed a computational framework based on a large and diverse set of protein specific descriptors that is ideal for making liability predictions using a QSPR (quantitative structure-property relationship) approach. This set offers a high degree of feature diversity that may coarsely be classified based on (1) sequence (2) structure and (3) surface patches. We assess the sensitivity and applicability of these descriptors in four dedicated case studies that are believed to be representative of biophysical characterizations commonly employed during the development process of a biologics drug candidate. In addition to data sets obtained from public sources, we have validated the descriptors on novel experimental data sets in order to address antibody developability and to generate prospective predictions on Adnectins. The results show that the descriptors are well suited to assist in the improvement of protein properties of systems that exhibit poor solubility or aggregation.

Predicting melting point of ionic liquids using QSPR approach: Literature review and new models

Quantitative structure–property relationships (QSPRs) for predicting melting point temperature (Tm) of ionic liquids (ILs) are reviewed and the new models are proposed by using the experimental data extracted from the literature for 953 salts. The models include both regression of Tm data and classification of the ILs with respect to their state of matter (liquid/solid) at T=300 K. A variety of machine learning algorithms is applied, including: partial least squares regression, stepwise multiple linear regression, and a number of common classifiers (k-nearest neighbors, naive Bayes, linear discriminant analysis, support vector machines). An effect of the molecular descriptors set as well as the computational level used for the ions’ geometry optimization is analyzed and followed in the final model selection protocol, which comprises all the standard steps of good practice of QSRP modeling, e.g. cross-validation, external validation, and the applicability domain analysis. Furthermore, as a key novelty, the robustness of the models is checked for different combining rules, defined as the averaging functions for obtaining the descriptors of ILs from those given for individual ions. The finally selected and recommended models are discussed in detail in terms of various statistics, as well as addressed to other methods reported in the literature. An effect of the chemical family of both cation and anion on the modeling performance is highlighted. Additionally, the predictions of both Tm and state of matter of more than 35,000 virtual cation–anion combinations are given in order to present the range of potential applications of the new methods in computer-aided molecular design of new ILs displaying demanded phase behavior.

QSPR modelling for investigation of different properties of aminoglycoside-derived polymers using 2D descriptors

ABSTRACT The quantitative structure‐property relationship (QSPR) method is commonly used to predict different physicochemical characteristics of interest of chemical compounds with an objective to accelerate the process of design and development of novel chemical compounds in the biotechnology and healthcare industries. In the present report, we have employed a QSPR approach to predict the different properties of the aminoglycoside-derived polymers (i.e. polymer DNA binding and aminoglycoside-derived polymers mediated transgene expression). The final QSPR models were obtained using the partial least squares (PLS) regression approach using only specific categories of two-dimensional descriptors and subsequently evaluated considering different internationally accepted validation metrics. The proposed models are robust and non-random, demonstrating excellent predictive ability using test set compounds. We have also developed different kinds of consensus models using several validated individual models to improve the prediction quality for external set compounds. The present findings provide new insight for exploring the design of an aminoglycoside-derived polymer library based on different identified physicochemical properties as well as predict their property before their synthesis.

Toward solvent screening in the extractive desulfurization using ionic liquids: QSPR modeling and experimental validations

In the present study, the effects of different structures of the involved compounds (such as cation, anion, and hydrocarbon) in the ternary systems on the distribution coefficient of the thiophene between ionic liquid and hydrocarbon-rich phases have been studied using Quantitative Structure-Property Relationship (QSPR). By a comprehensive literature survey, the experimental data of the distribution of thiophene for 84 different ternary systems with 763 data points have been collected. The data set includes 14, 10, and 10 different cation, anion, and hydrocarbon structures, respectively. A general QSPR model has been developed to evaluate the simultaneous effect of the hydrocarbon structure, the cation structure, and the anion structure on the thiophene distribution between the hydrocarbon and ionic liquid-rich phases. The selected molecular descriptor in this model was simple descriptor of the number of carbon atom (nC) for the cation structure, the number of nitrogen atom (nN) for the anion structure, and “Pol” for the hydrocarbon structure. The outcome of the internal validation, the external validation, and the statistical evaluation of the final QSPR models (R2 = 0.91, %AARD = 17.28) confirmed the acceptable prediction capability. The Liquid-Liquid Equilibrium (LLE) data has also been generated using the QSPR approach for a vast number of non-studied ternary systems for the first time. The LLE data of four different ternary systems ([C2MIM][EtSO4] (1) - Thiophene (2) - Cyclohexane, Methyl cyclohexane, n-Nonane, n-Decane (3)) was measured experimentally. The comparison of the measured experimental data and the predicted data using the developed QSPR model confirmed the validity of the QSPR developed model, again.

Inhibition of Aluminium Corrosion in 1M HCl by Pyridoxine Hydrochloride: Thermodynamic and Quantum Chemical Studies

Currently, research in the area of corrosion inhibition is focussed on the development of green corrosion inhibitors. It is with this in mind that pyridoxine hydrochloride, which is vitamin B6, has been tested as a corrosion inhibitor of aluminium in 1M HCl by mass loss, Density Functional Theory (DFT) and Quantitative Structure-Property Relationship (QSPR) methods. The results obtained show that the inhibition efficiency increases with concentration but decreases with increasing temperature. This vitamin is adsorbed on aluminium according to the modified Langmuir isotherm and occurs in two modes: physisorption and chemisorption. Thermodynamic adsorption and activation parameters have been determined and discussed. Finally, QSPR approach was used to find the best set of parameters in order to determine the theoretical inhibition efficiencies from the experimental data. Experimental measurements were found in good collaboration with the theoretical results.

Machine learning guided prediction of liquid chromatography–mass spectrometry ionization efficiency for genotoxic impurities in pharmaceutical products

The limitation and control of genotoxic impurities (GTIs) has continued to receive attention from pharmaceutical companies and authorities for several decades. Because GTIs have the ability to damage deoxyribonucleic acid (DNA) and the potential to cause cancer, low-level quantitation is required to protect patients. A quick and easy method of determining the liquid chromatography-mass spectrometry (LC/MS) conditions for high-sensitivity analysis of GTIs may prospectively accelerate pharmaceutical development. In this study, a quantitative structure-property relationship (QSPR) model was developed for predicting the ionization efficiency of compounds using liquid-chromatography-mass spectrometry (LC/MS) parameters and molecular descriptors. Before implementing the QSPR prediction model, linear regression analysis was performed to model the relationship between the ionization efficiency and the LC/MS parameters for each compound. Comparison of the predicted peak areas with the experimentally observed peak areas showed good agreement based on the coefficient of determination (R2 > 0.96). The machine learning-based QSPR approach begins with computation of the molecular descriptors expressing the physicochemical properties of a compound, followed by a genetic algorithm-based feature selection. Linear and nonlinear regression were performed, and support vector machine (SVM) was selected as the best machine learning algorithm for the prediction. The SVM algorithm was developed and optimized using 1031 experimental data points for nine compounds, including well-known GTIs. Validation of the model by comparison of the predicted and observed relative ionization efficiencies (RIE) showed a high coefficient of determination (R2 = 0.96) and low root mean squared error value (RMSE = 0.118). Finally, this established prediction model was applied to hydrophilic interaction liquid chromatography coupled with MS for a new compound in new mobile phase compositions and new MS parameter settings. The RMSE of the predicted versus observed RIE was 0.203. This prediction accuracy was sufficient to determine the starting point of the LC/MS method development. The methodology demonstrated in this study can be used to determine the LC/MS conditions for high sensitivity analysis of GTIs.

Development and validation of QSPR models for corrosion inhibition of carbon steel by some pyridazine derivatives in acidic medium

Statistical modeling of the corrosion inhibition process by twenty-one pyridazine derivatives for mild steel in acidic medium was investigated by the quantitative structure property relationship (QSPR) approach. This modeling was established by the correlation between the corrosion inhibition efficiency (IE %) and a number of the electronic and structural properties of these inhibitors such as: the EHOMO (highest occupied molecular orbital energy), the ELUMO (lowest unoccupied molecular orbital energy), the energy gap (EL-H), the dipole moment (μ), the hardness (η), the softness (σ), the absolute electronegativity (χ), the ionization potential (IP), the electron affinity (EA), the fraction of electrons transferred (ΔN), the electrophilicity index ω the molecular volume (Vm), the logarithm of the partition coefficient (Log P), and the molecular mass (M), in addition to the inhibitor concentration (Ci). The structure electronic properties was calculated by the use of the density functional theory method (DFT), at B3LYP/6-31G (d, p) level of theory and the analysis of dimensionality and redundancy as well as the test of collinearity between descriptors are carried out using principal component analysis (PCA). Whereas, the correlation between EI % and molecular structure is performed through the development of tree mathematical models, based-QSPR approaches: the partial least squares regression (PLS), the principal component regression (PCR) and the artificial neural networks (ANN). Indeed, the statistical quantitative results revealed that PCR and ANN were the most relevant and predictive models in comparison with the PLS model. This pertinence was demonstrated by using leave one-out cross-validation as an efficient method for testing the internal stability and predictive capability of said models with a high cross-validated determination coefficient R2cv= 0.92 and predicted determination coefficient R2pred= 0.92 and R2pred= 0.90 for PCR and ANN respectively; in addition to an extrapolation test set as an external validation with a significant external coefficient of determination: R2test= 0.94 and R2test= 0.92, for the two correspondingly models.

Singlet oxygen generation by porphyrins and metalloporphyrins revisited: A quantitative structure-property relationship (QSPR) study

Porphyrins and metalloporphyrins are used as photosensitizers in photocatalysis, photodynamic therapy (PDT), disinfection, degradation of persistent pollutants and other applications. Their mechanism of action involves intersystem crossing to triplet excited state followed by formation of singlet oxygen (1O2), which is a highly reactive species and mediates various oxidative processes. The design of advanced sensitizers based on porphyrin compounds have attracted significant attention in recent years. However, it is still difficult to predict the efficiency of singlet oxygen generation for a given structure. Our goal was to develop a quantitative structure-property relationship (QSPR) model for the fast virtual screening and prediction of singlet oxygen quantum yields for pophyrins and metalloporphyrins. We performed QSPR analysis of a dataset containing 32 compounds, including various porphyrins and their analogues (chlorins and bacteriochlorins). Quantum-chemical descriptors were calculated using Density Functional Theory (DFT), namely B3LYP and M062X functionals. Three different machine learning methods were used to develop QSPR models: random forest regression (RFR), support vector regression (SVR), and multiple linear regression (MLR). The optimal QSPR model «structure – singlet oxygen generation quantum yield» obtained using RFR method demonstrated high determination coefficient for the training set (R2 = 0.949) and the highest predicting ability for the test set (pred_R2 = 0.875). This proves that the developed QSPR method is realiable and can be directly applied in the studies of singlet oxygen generation both for free base porphyrins and their metal complexes. We believe that QSPR approach developed in this study can be useful for the search of new poprhyrin photosensitizers with enhanced singlet oxygen generation ability.

Modeling of the Henry Constant of a Series of Pesticides: Quantitative Structure-Property Relationship Approach

Modeling of the Henry Constant of a Series of Pesticides: Quantitative Structure-Property Relationship Approach

Rational Design of Fractal Gold Nanosphere Assemblies with Optimized Photothermal Conversion Using a Quantitative Structure Property Relationship (QSPR) Approach

Assemblies of plasmonic nanoparticles have been proposed for various applications, including photothermal therapy, exploiting surface plasmon coupling phenomena. However, the rational design of fractal nanoparticle assembly remains challenging due to the lack of structural characterizations and modelization of real systems. Here we used the quantitative structure property relationship (QSPR) approach, driven by experimental data and statistical analysis, to establish a relationship between structural descriptors of fractal gold nanoparticle (GNP) aggregates and their light-to-heat conversion. A total of 160 assemblies of various size spherical GNPs with different polyelectrolyte chains were synthesized, which differ in their global charge, size, mass fractal dimension, and plasmonic properties. Fifteen independent descriptors of structure and properties were extracted and further analyzed by QSPR. Principal component analysis and multilinear regression reveal that light-to-heat conversion is mainly governed by the structure of the aggregates and not by the characteristics of the building blocks. This highlights the key role of the fractal dimension of the aggregate and of the ratio of GNP/polyelectrolyte mass to optimize photothermal effects. Rational criteria to optimize light-to-heat conversion within nonideal fractal assemblies of GNP were identified, relaxing on the choice of other parameters, such as GNP or aggregate size, that can be adapted to the desired biomedical applications.

Computer Assisted Models for Blood Brain Barrier Permeation of 1, 5-Benzodiazepines.

To generate and validate predictive models for blood-brain permeation (BBB) of CNS molecules using the QSPR approach. Prediction of molecules crossing BBB remains a challenge in drug delivery. Predictive models are designed for the evaluation of a set of preclinical drugs which may serve as alternatives for determining BBB permeation by experimentation. The objective of the present study was to generate QSPR models for the permeation of CNS molecules across BBB and its validation using existing in-house leads. The present study envisaged the determination of the set of molecular descriptors which are considered significant correlative factors for BBB permeation property. Quantitative Structure- Property Relationship (QSPR) approach was followed to describe the correlation between identified descriptors for 45 molecules and highest, moderate and least BBB permeation data. The molecular descriptors were selected based on drug-likeness, hydrophilicity, hydrophobicity, polar surface area, etc. of molecules that served the highest correlation with BBB permeation. The experimental data in terms of log BB were collected from available literature, subjected to 2D-QSPR model generation using a regression analysis method like Multiple Linear Regression (MLR). The best QSPR model was Model 3, which exhibited regression coefficient as R2= 0.89, F = 36; Q2= 0.7805 and properties such as polar surface area, hydrophobic hydrophilic distance, electronegativity, etc., which were considered key parameters in the determination of the BBB permeability. The developed QSPR models were validated with in-house 1,5-benzodiazepines molecules and correlation studies were conducted between experimental and predicted BBB permeability. The QSPR model 3 showed predictive results that were in good agreements with experimental results for blood-brain permeation. Thus, this model was found to be satisfactory in achieving a good correlation between selected descriptors and BBB permeation for benzodiazepines and tricyclic compounds.

Development of pp-LFER and QSPR models for predicting the diffusion coefficients of hydrophobic organic compounds in LDPE.

Diffusion coefficient (D) is important to evaluate the performance of passive samplers and to monitor the concentration of chemicals effectively. Herein, we developed a polyparameter linear free energy relationship (pp-LFER) model and a quantitative structure-property relationship (QSPR) model for the prediction of diffusion coefficients of hydrophobic organic contaminants (HOCs) in low density polyethylene (LDPE). A dataset of 120 various chemicals was used to develop both models. The pp-LFER model was developed with two descriptors (V and E) and the statistical parameters of the model showed satisfactory results. As a further exploration of the diffusion behavior of the compounds, a QSPR model with five descriptors (ETA_Alpha, ASP-6, IC1, TDB6r and ATSC2v) was constructed with adjusted determination coefficient (R2) of 0.949 and cross-validation coefficient (QLoo2) of 0.941. The regression results indicated that both models had satisfactory goodness-of-fit and robustness. This study proves that pp-LFER and QSPR approaches are available for the prediction of log D values for the hydrophobic organic compounds within the applicability domain.