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Quantitative structure–activity relationship (QSAR) method was used to predict the pIC50 value of 58 derivatives of 6-methoxy benzamides in this work. The artificial neural network (ANN) and multiple linear regressions (MLR) were used to construct the non-linear and linear QSAR models, respectively. The standard errors in the prediction of pIC50 for training, internal and external test sets, are; 0.280, 0.446 and 0.382 by MLR model and are; 0.175, 0.326 and 0.296 by ANN model, respectively. Also these models were further examined by cross-validation methods which produce the statistics of Q2 = 0.8340 and SPRESS = 0.322 for MLR model and Q2 = 0.8055 and SPRESS = 0.219 for ANN model.

A practicable quantitative structure property relationship (QSPR) model for predicting aqueous solubility, S w , of 23 polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was developed. Linear artificial neural network (L-ANN) was used to develop the calibration model of S w . The input variables of L-ANN were selected from 11 structural descriptors of the investigated PCDD/Fs by using stepwise regression. Leave one out cross validation and split-sample validation were carried out to assess the predictive performance of the developed model. The results of leave one out cross validation and split-sample validation are both satisfactory, which verify the reliability and practicability of the developed model. It is demonstrated that L-ANN combined with stepwise regression is a practicable method for developing QSPR model for S w of PCDD/Fs. Additionally, stepwise regression is shown to be a practicable approach for the selection of input variables when developing a QSPR model with L-ANN.

In the present work, a quantitative structure property relationship (QSPR) methodology has been applied to predict the cetane number (CN) of hydrocarbons that are likely to be found in diesel fuels. A database containing 147 molecules has been set up with experimental CNs available in the literature. The prediction of the CN was improved by dividing the database into four chemical families: (i) linear (n-) and branched (iso-) paraffins, (ii) naphthenes, (iii) aromatics, and (iv) n- and iso-olefins. A genetic algorithm working on molecular descriptors was used to build specific CN models for each of these classes. The predictive models return CN values roughly in the range from 0 to 100, which is in line with the definition of CN, with average absolute deviations similar to the experimental reproducibility (3−5 points).

In the present work, a quantitative structure-activity relationship (QSAR) method was used to predict the psychometric activity values (as mes- caline unit, log MU) of 48 phenylalkylamine derivatives from their density functional theory (DFT) calculated molecular descriptors and an artificial neu- ral network (ANN). In the first step, the molecular descriptors were obtained by DFT calculation at the 6-311G  level of theory. Then the stepwise multiple linear regression method was employed to screen the descriptor spaces. In the next step, an artificial neural network and multiple linear regressions (MLR) models were developed to construct nonlinear and linear QSAR models, res- pectively. The standard errors in the prediction of log MU by the MLR model were 0.398, 0.443 and 0.427 for training, internal and external test sets, res- pectively, while these values for the ANN model were 0.132, 0.197 and 0.202, respectively. The obtained results show the applicability of QSAR approaches by using ANN techniques in prediction of log MU of phenylalkylamine deri- vatives from their DFT-calculated molecular descriptors.

A quantitative structure - property relationship (QSPR) was used to predict the critical micelle concentration (cmc) of nonionic surfactants. The relation was developed for a diverse set of 23 nonionic surfactants (r = 0.99, F-test = 1042.5) employing the connectivity and valence connectivity indices only. The molecular connectivity indices were calculated for the whole molecule which was a simple and general approach.

A quantitative structure-property relationship (QSPR) model was proposed on the basis of the molecular structure of 43 aromatic explosives and their density. The best linear model composed of five molecular descriptors which were selected from 522 constitutional, geometrical, topological, and quantum-mechanical types of descriptors. Statistical characteristics of the multiple linear model (R2=0.892, F=60.95, RMS=0.0539, AARD=2.41%) indicated satisfactory stability and predictive ability. The model may give some insight into the main structural factors that modulate the density of the investigated compounds.

A Quantitative Structure–Property Relationship (QSPR) study was carried out to model the melting points for a diverse set of 288 potential Ionic Liquids (ILs) including pyridinium bromides, imidazolium bromides, benzimidazolium bromides, and 1-substituted 4-amino-1,2,4-triazolium bromides. Based on the calculated descriptors by CODESSA program, a Principal Component Analysis (PCA) was performed on the whole data to detect the homogeneities in the dataset and to assist the separation of the data into representative training and test sets. Heuristic Method (HM) and Projection Pursuit Regression (PPR) were used to develop linear and nonlinear models between the descriptors and the melting points. The PPR model gave a high predictive correlation coefficient (R2) of 0.810 and an Average of Absolute Relative Deviation (AARD) of 17.75%, which are better than those by HM model (R2=0.712, AARD=24.33%) indicating that PPR is better for the prediction of the melting points. In addition, the descriptors selected by HM can give some insight into factors that can affect the melting points, i.e., benzene ring structure, rotatable bonds, branching, symmetry, and intramolecular electronic effects. This information would be very useful in the design of the potential ILs with desired melting points.

The half-wave reduction potential is an important electrochemical property used for the characterization of organic compounds. This property, which is a characteristic constant for a reversible oxidation–reduction system, can be useful for predicting electrochemical properties of other organic compounds. In this work, quantitative structure–property relationship (QSPR) models have been introduced for estimating polarographic half-wave reduction potentials of 21 chlorinated organic compounds. Two QSPR models have been developed based on genetic algorithm–partial least squares (GA-PLS) and stepwise regression–partial least squares (SR-PLS) to predict half-wave potentials (E1/2) of some chlorinated organic compounds. Variable selection is very important for QSPR modelling. In the present study, two selection variables methods were compared to choose molecular descriptors for the construction of a model by the PLS method. Both GA-PLS and SR-PLS methods resulted in accurate prediction, with more accurate resul...

One of the industrially important thermodynamic properties of polymer solutions is the upper critical solution temperature at the limit of infinite chain length of polymers, which is used to realiz...

A quantitative structure property relationship (QSPR) analysis of some organic compounds (imines or Schiff bases) is studied. The corrosion inhibition efficiencies of these imines have been studied by using AM1 (Austin model 1) Hamiltonian SCF-MO method and QSPR analysis. One of the most promising semi-empirical methods for predicating geometries and electronic properties is AM1. The geometry of all compounds 1-11 was optimized and all data obtained for building the multiple regression model. The proposed equations were applied to predict the corrosion inhibition efficiency of some related structures to select molecules of possible activity from a presumable library of compounds was obtained from the regression coefficients for the model.

Densities of ionic liquids have been estimated using a quantitative structure–property relationship (QSPR) method that involved 11 molecular descriptors. Density data of 238 experimental data sets for 163 ionic liquids (3020 data points) were used to obtain the QSPR model for a wide range of temperatures and pressures: 258–393 K and 0.09–207 MPa. Results show that the QSPR model to estimate the liquid density (R2 = 0.9307; AARD = 2.00) is accurate enough and gives lower deviations than any other similar QSPR model available in the literature. Copyright © 2009 John Wiley & Sons, Ltd.

A quantitative structure–property relationship (QSPR) model for predicting the skin sensitization effects of chemical compounds has been developed. An extensive database of test results from three exclusive test procedures was used for QSPR model development. Since the experimental procedure and end-point ranking of data for local lymph node assay (LLNA), guinea pig maximization test (GPMT), and Federal Institute for Health Protection of Consumers and Veterinary Medicine (BgVV) are different, three separate QSPR models were developed. Effective non-linear regression models were used for QSPR model development. The predictive capability of the final QSPR models was further improved by using a combination of literature-recommended and structural descriptors. The resultant QSPR models are capable of predicting skin sensitization of the diverse set of molecules considered with accuracies of 90%, 95%, and 90% for the LLNA, GPMT, and BgVV datasets, respectively.

A quantitative structure-property relationship (QSPR) study was performed to predict the molecular diffusivity of pure chemicals in water. A genetic-algorithm-based multivariate linear regression (GA-MLR) was applied to select the most statistically effective molecular descriptors for modelling the molecular diffusivity of pure chemicals in water. Based on the selected molecular descriptors, a three-layer feed forward neural network (FFNN) was constructed to predict the property. The obtained results showed that the FFNN was able to predict the molecular diffusivity of pure chemicals in water.

The quantitative structure-property relationship (QSPR) methodology is applied to estimate the binding affinity of lithium, sodium, potassium, copper, and silver cations to the 20 common amino acids. The proposed model, nonlinearly derived from computational neural networks (CNN), contains seven descriptors and was validated by an external prediction set. Good results are obtained with correlation coefficients, R(2), and root-mean-square errors (rms) (kJ/mol) of 0.998 (3.89), 0.999 (2.86), and 0.997 (3.90) for the training, prediction, and validation sets, respectively. Five of the descriptors of the model correspond to the amino acids and the other two to the cations; they encode information clearly related to the cation-amino acid interactions responsible for the binding affinity values analyzed. A detailed analysis of results shows that, despite the different nature of the bonding between the metal cations and the amino acids, the neural networks used are capable of predicting accurately the property studied.

Models to predict binding constant (logK) to bovine serum albumin (BSA) should be very useful in the pharmaceutical industry to help speed up the design of new compounds, especially as far as pharmacokinetics is concerned. We present here an extensive list of logK binding constants for thirty-five compounds to BSA determined by florescence quenching from the literature. These data have allowed us the derivation of a quantitative structure-property relationship (QSPR) model to predict binding constants to BSA of compounds on the basis of their structure. A stepwise multiple linear regression (MLR) was performed to build the model. The statistical parameter provided by the MLR model (R = 0.9200, RMS = 0.3305) indicated satisfactory stability and predictive ability for the model. Using florescence quenching spectroscopy, we also experimentally determined the binding constants to BSA for two bioactive components in traditional Chinese medicines. Using the proposed model it was possible to predict the binding constants for each, which were in good agreement with the experimental results. This QSPR approach can contribute to a better understanding of structural factors of the compounds responsible for drug-protein interactions, and be useful in predicting the binding constants of other compounds.

The aqueous solubility of poorly water-soluble drugs is an important property of many factors affecting their bioavailability such as the solubility and rate of dissolution in water. The quantitative structure-property relationship approach using genetic algorithm was applied to make models for predicting some poorly water-soluble drugs such as ursodeoxycholic acid, diphenyl hydrantoin and biphenyl dimethyl dicarboxylate. The experimental solubility data of 3518 chemical structures were collected from the web and used to build a model. Three data sets of 50 compounds were extracted according to their structural similarity with each drug. A fast and predictive similarity based approach was developed and validated with conventional method. This can be used to predict the aqueous solubility for drugs by using a small set of compounds, especially for poorly water-soluble compounds. Moreover, the estimation values of various sets were further compared with fine grinding experiment data.

Interactions of solutes with diverse ionic liquid solvents have been investigated by quantitative structure–property relationship (QSPR) methodology. Ostwald solubility coefficients and partition coefficients of organic solutes in eight different ionic liquids are correlated by molecular descriptors calculated solely from their structures. Two- to four-parameter best multilinear regression models were obtained with coefficients of determination ranging from 0.913 to 0.992. Additionally, several models were obtained with the same descriptors for all eight ionic liquids (ILs). Charge-related type descriptors contributed significantly to most of the models. The QSPR models were validated using the leave-one-out cross validation method.

A simple quantitative structure property relationship (QSPR) for correlating the plasticization efficiency of 25 polyvinylchloride (PVC) plasticizers was obtained using molecular modeling. The plasticizers studied were-aromatic esters (phthalate, terephthalate, benzoate, trimellitate), aliphatic esters (adipate, sebacate, azelate), citrates and a phosphate. The low temperature flex point, Tf, of plasticized polyvinylchloride resins was considered as an indicator of plasticization efficiency. Initially, we attempted to predict plasticization efficiency of PVC plasticizers from physical and structural descriptors derived from the plasticizer molecule alone. However, the correlation of these descriptors with Tf was not very good with R=0.78 and r2=0.613. This implied that the selected descriptors were unable to predict all the interactions between PVC and plasticizer. Hence, to account for these interactions, a model containing two polyvinylchloride (PVC) chain segments along with a plasticizer molecule in a simulation box was constructed, using molecular mechanics. A good QSPR equation correlating physical and structural descriptors derived from the model to Tf of the plasticized resins was obtained with R=0.954 and r2=0.909.

In the present work, a Quantitative Structure–Property Relationship (QSPR) study was performed to predict the Watson characterization factor of hydrocarbon components. A Genetic Algorithm-based Multivariate Linear Regression (GA-MLR) was applied to select the most statistically effective molecular descriptors of the Watson characterization factor. Then, based on the selected molecular descriptors by GA-MLR, a three-layer Feed Forward Neural Network (FFNN) was constructed to predict the Watson characterization factor. The obtained results showed that the constructed FFNN can accurately predict the Watson characterization factor of hydrocarbon components.

In this work, a general Quantitative Structure–Property Relationship (QSPR) model (, , and ) for the prediction of flash points of 1030 pure compounds is developed. Genetic Algorithm-based Multivariate Linear Regression (GA-MLR) technique is used to select four chemical structure-based molecular descriptors from a pool containing 1664 molecular descriptors.

The retention of proteins in hydrophobic interaction chromatographic (HIC) systems was investigated using the preferential interaction theory in concert with quantitative structure property relationship (QSPR) modeling. The numbers of water molecules and salt ions released upon protein binding were determined from ln (k′) vs. salt concentration plots for a large number of proteins with a range of properties. The effect of salt type on protein binding was also studied by comparing the number of water molecules released in the presence of different salts. Quantitative structure property relationship (QSPR) models based on a support vector machine (SVM) approach were successfully generated for predicting the water molecules released values. These models employed protein crystal structure and primary sequence information as well as a set of hydrophobicity descriptors based on the solvent accessible surface area of the proteins. In addition to successfully predicting the water release values, the selected descriptors provide insights into the protein physicochemical properties which influence protein affinity in HIC system.

With quantum chemical parameters, topological indexes, and physical chemistry parameters as descriptors, a quantitative structure-property relationship (QSPR) has been found for the cloud points of four series of nonionic surfactants (a total of 65 surfactants). The best-regressed model includes six descriptors, and the correlation coefficient of multiple determination is as high as 0.962.

Here, we present the AA-QSPR Db (Amino Acid Quantitative Structure Property Relationship Database): a novel, freely available web-resource of data pertaining to amino acids, both engineered and naturally occurring. In addition to presenting fundamental molecular descriptors of size, charge and hydrophobicity, it also includes online visualization tools for users to perform instant, interactive analyses of amino acid sub-sets in which they are interested. The database has been designed with extensible markup language technology to provide a flexible structure, suitable for future development. In addition to providing easy access for queries by external computers, it also offers a user-friendly web-based interface that facilitates human interactions (submission, storage and retrieval of amino acid data) and an associated e-forum that encourages users to question and discuss current and future database contents.

Gas-to-RTIL (room-temperature ionic liquid) partition coefficients have been compiled for 592 different solute-RTIL combinations. These partition coefficients were converted into water-to-RTIL partition coefficients using the corresponding gas-to-water partition coefficients. Both sets of partition coefficients were analyzed using the Abraham solvation parameter model with cation-specific and anion-specific equation coefficients. The derived equations correlated the experimental gas-to-RTIL and water-to-RTIL partition coefficient data to within 0.10 and 0.14 log units, respectively. The 8 sets of calculated cation-specific equation coefficients and 4 sets of calculated anion-specific equation coefficients can be combined to yield expressions capable of predicting the partition coefficients of solutes in 32 different RTILs.

The quantitative structure-property relationship (QSPR) study was performed between descriptors representing the molecular structures and refractive indices for a set of 35 pi-conjugated polymers. A seven-descriptor correlation was developed for the prediction of refractive indices with R = 0.936 and s = 0.028 by stepwise multilinear regression analysis. The average relative error for the calculation of refractive indices was 1.022%. The stability of the proposed model was validated using leave-one-out cross-validation and randomization experiments. Since the model requires only molecular descriptors derived solely from the repeating unit structures of conjugated polymers, it has better predictive capability comparing with the existing group-contribution methods.

Density functional theory and ab initio molecular orbital calculations have been carried out in order to get insights into the structure, chemical reactivity and property information for the series of chloroanisoles. Descriptors such as energy, hardness, chemical potential, electrophilicity index and polarizability provide vital information about the global reactivity of chloroanisoles. Further, we utilized our recently proposed multiphilic descriptor defined as the difference between nucleophilic and electrophilic condensed philicity functions to understand the local reactivity/site selectivity on the series of chloroanisoles. Also, structure-property relationship in chloroanisoles shows the importance of the electrophilicity index in the property prediction.

The quantitative structure-property relationship was used for estimating the activity of influenza virus neuraminidase inhibitors, cyclohexene and cyclopentane derivatives. Application of two models, topological and geometric, showed that in both cases the estimated activity is well consistent with the experimental data, but the geometric model is necessary when dealing with stereoisomers. The structural elements of the inhibitor “responsible” for the high activity were revealed.

A structure-property relationship was used to estimate the free energy of formation of host-guest complexes of α-cyclodextrin with monosubstituted benzene and phenol derivatives and with symmetrical 1,4-disubstituted benzene derivatives, in which various nonvalent interactions (hydrophobic, van der Waals, donor-acceptor) play a significant role. The calculated values are well consistent with the experiment.

A set of new molecular descriptors, R SC , S MC , D HB and M PE , which are obtained directly from polystyrenes monomer structures, are used to predict the Tg values of polystyrenes and generate a QSPR model by multiple linear stepwise regression analysis, with R = 0.959 and s = 15.20 K. Compared with the existing QSPR models, the proposed model requires only four descriptors, which can be obtained by simple calculation and makes it more valuable to predict the T g values of polymers.

The previously suggested quantitative structure-property relationship was used for predicting properties of aliphatic carboxylic acids. The boiling points, octanol-water partition factors (logP), and toxicities [log(1/IGC50)] were calculated for a series of monocarboxylic, dicarboxylic, and unsaturated monocarboxylic acids. The calculated values are well consistent with the experimental data.

生理活性物質の一つである糖のフェニルホウ酸と四級アンモニウム塩を用いた抽出分離に計算化学的手法を応用した．まず半経験的分子軌道計算に基づきフェニルホウ酸と単糖類の水相での錯体の生成熱を計算し，実測した安定度定数と比較した．この場合，ピラノース（六員環）型あるいはフラノース（五員環）型のいずれの構造の糖がフェニルホウ酸との錯形成に寄与しているかは明確にはできなかったが，その糖との選択性の順序は十分に説明できた．次にフェニルホウ酸と四級アンモニウム塩による単糖類の溶媒抽出に関して，混合有機溶媒中での生成錯体の生成熱を計算し，実験的に得られたみかけの抽出平衡定数と比較したところ，良好な相関関係が得られた．また抽出平衡定数では四級アンモニウム塩の構造変化に伴う選択性の変化を予測することが可能であった．このように計算化学的手法を援用することにより，抽出挙動を定量的に予測することが可能となった．

The previously suggested structure-property relationship was applied to estimating the properties of methane and silane derivatives C(Si)H e X m Y4−e−m in which substituents X and Y at the same central atom differently interact with each other. In these compounds, the two-fragment 1⋯3 interactions, multifragment interactions, and interactions corresponding to the electrostatic contribution to intermolecular interactions play a significant role. The boiling points were calculated for the F, Cl, and F,Cl derivatives of methane, silane, and methylsilanes; for the methane derivatives, the octanol-water partition coefficients (log P) systems and heat capacities at constant pressure (C p) were also calculated. The results obtained are well consistent with the experimental data.

The quantitative structure-property relationship was used to calculate the boiling points of 74 aliphatic monohydric alcohols of various structures; the trends in the properties of isomers and their compositionally related compounds were explained with account for the “hot ends” effect.

General empirical relationships have been developed for estimating the Krafft point of anionic surfactants and cloud point of nonionic surfactants using the quantitative structure‐property relationship (QSPR) method. For the Krafft point, a six‐parameter structure‐property relationship (R2=0.941) was obtained for a diverse set of 46 anionic hydrocarbon surfactants and a three‐parameter relationship (R2=0.864) for 19 linear fluorocarbon anionic surfactants by employing topological, thermodynamic, and structural descriptors. For the cloud point of 64 nonionic surfactants with six representative structures, the best multiple linear regression model involved five parameters and had a correlation coefficient of R2=0.921. From these relations Krafft point or cloud point of surfactants can be estimated based on their molecular structure.

A phenomenological study of solubility has been conducted using a combination of quantitative structure-property relationship (QSPR) and principal component analysis (PCA). A solubility database of 4540 experimental data points was used that utilized available experimental data into a matrix of 154 solvents times 397 solutes. Methodology in which QSPR and PCA are combined was developed to predict the missing values and to fill the data matrix. PCA on the resulting filled matrix, where solutes are observations and solvents are variables, shows 92.55% of coverage with three principal components. The corresponding transposed matrix, in which solvents are observations and solutes are variables, showed 62.96% of coverage with four principal components.

The simple approach suggested previously for estimating properties of molecules RX was applied to constructing quantitative relationships between the structure of alkyl substituent R and the properties of stable complexes and transition states considered as supermolecules RX. The enthalpies and free energies of complexation of a series of aliphatic amines with trimethylboron, the logarithms of the relative rates of esterification of aliphatic carboxylic acids, and the Taft steric constants were calculated.

The structure-property correlation was used to calculate the boiling points of 56 chloroaliphatic compounds containing from 1 to 8 chlorine atoms located irregularly, at the same carbon atom inclusive. The calculation made use of the experimental boiling points of isoelectronic molecules of saturated hydrocarbons with corresponding corrections. The calculated values nicely fit experimental.

The quantitative structure-property relationship was applied to calculate the enthalpy of formation of aliphatic polynitro compounds, which was estimated from data for alkane molecules using appropriate corrections. Two alternatives of the suggested approach were considered, in which the molecule of a nitro compound is constructed by replacement of an H atom or CH3 group with the NO2 group in a definite position of a saturated hydrocarbon.

A quantitative structure-property relationship (QSPR) was developed to predict drug solubility in binary mixtures of polyethylene glycol (PEG) 400 and water. The ability of the QSPR model to predict solubility was assessed and compared to the classic log-linear cosolvency model.The solubility of 122 drugs, ranging in log P from -2.4 to 7.5, was determined in 0%, 25%, 50%, and 75% PEG (v/v in water) by the shake-flask method. Solubility data from 84 drugs were fit by linear regression using the following molecular descriptors: molecular weight, volume, radius of gyration, density, number of rotatable bonds, hydrogen-bond donors, and hydrogen-bond acceptors. The multiple linear regression model was optimized by a genetic algorithm guided selection method. The remaining 38 compounds were used to test the predictability of the model.QSPR-based models developed at each volume fraction with the training set compounds showed a reasonable correlation coefficient (r) of approximately 0.9 and a root mean square (rms) error of0.5 log unit. The model predicted solubility values of approximately 78% of the testing set compounds within 1 log unit. The log-linear model was as effective as the QSPR-based model in predicting the testing set solubilities; however, many drugs, as expected, showed significant deviation from log-linearity.The QSPR model requires only the chemical structure of the drug and has utility for guiding vehicle identification for early preclinical in vivo studies, especially when compound availability is limited and experimental data such as aqueous solubility and melting point are unknown. When experimental data are available, the log-linear model was verified to be a useful predictive tool.

Halogenated aliphatic compounds have many technical uses, but substances within this group are also ubiquitous environmental pollutants that can affect the ozone layer and contribute to global warming. The establishment of quantitative structure-property relationships is of interest not only to fill in gaps in the available database but also to validate experimental data already acquired. The three-dimensional structures of 240 compounds were modeled with molecular mechanics prior to the generation of empirical descriptors. Two bilinear projection methods, principal component analysis (PCA) and partial-least-squares regression (PLSR), were used to identify outliers. PLSR was subsequently used to build a multivariate calibration model by extracting the latent variables that describe most of the covariation between the molecular structure and the boiling point. Boiling points were also estimated with an extension of the group contribution method of Stein and Brown.

As part of our general QSPR treatment of solubility (started in the preceding paper), we now present quantitative relationships between solvent structures and the solvation free energies of individual solutes. Solvation free energies of 80 diverse organic solutes are each modeled in a range from 15 to 82 solvents using our CODESSA PRO software. Significant correlations (in terms of squared correlation coefficient) are found for all the 80 solutes: the best fit is obtained for n-propylamine (R(2) = 0.996); the lowest R(2) corresponds to toluene (0.604).

Primary data mining on alkanes for seeking accurate quantitative relationship between molecular structure and retention indices of gas chromatography is developed in this paper. Based on the results obtained from projection pursuit (PP), a new variable named class distance variable, which essentially describes the branching structure of the alkanes, is proposed. With the help of the new variable, both fitting and prediction accuracy of the regression model can be dramatically improved. The results obtained in this work show that the technique of PP developed in statistics is a quite promising tool for seeking accurate quantitative structure-activity relationship (QSAR) and/or quantitative structure-property relationship (QSPR) researches.

The previously suggested quantitative structure-property relationship was applied to estimate the properties of derivatives of saturated hydrocarbons and benzene, containing several polar groups. The boiling points, logP values (where P is the distribution factor in the octanol-water system), and solvatochromic parameters were calculated for a series of polyhydric alcohols, polyphenols, and isoelectronic chloro derivatives of saturated hydrocarbons and benzene. The calculated values are in good agreement with the experiment. A satisfactory correlation between these properties allows in some cases estimation of properties using a quantitative property-property relationship instead of a structure-property relationship.

Two group-contribution methods have been developed to estimate heat capacities of organic solids at ambient pressure. The power-law (PL) method utilizes an empirical temperature dependence, while the partition function (PF) method is based on the Einstein−Debye partition function for crystals with a modified frequency distribution function. Both methods have a fixed temperature functionality but utilize group contributions to obtain the compound-specific constants in the predictive equations. The training set for the group-contribution correlations consisted of 455 compounds and 7967 heat capacity data points. Both methods can be used for temperatures above 50 K, and they correlate the training set to within approximately 9.4 J·mol-1·K-1, corresponding to an average deviation of 6.8% for the PL method and 8.0% for the PF method. The PL method gives better results at lower temperatures, and the PF method at higher temperatures. Tests on the methods' extrapolation capabilities suggest that at 298 K they hav...