Prediction of tissue-air partition coefficients: a comparison of structure-based and property-based methods

Rat Tissue and Blood Partition Coefficients for n-Alkanes (C8 to C12)

Rat tissue:air and blood:air partition coefficients (PCs) for octane, nonane, decane, undecane, and dodecane (n-C8 to n-C12 n-alkanes) were determined by vial equilibration. The blood:air PC values for n-C8 to n-C12 were 3.1, 5.8, 8.1, 20.4, and 24.6, respectively. The lipid solubility of n-alkanes increases with carbon length, suggesting that lipid solubility is an important determinant in describing n-alkane blood:air PC values. The muscle:blood, liver: blood, brain:blood, and fat:blood PC values were octane (1.0, 1.9, 1.4, and 247), nonane (0.8, 1.9, 3.8, and 274), decane (0.9, 2.0, 4.8, and 328), undecane (0.7, 1.5, 1.7, and 529), and dodecane (1.2, 1.9, 19.8, and 671), respectively. The tissue:blood PC values were greatest in fat and the least in muscle. The brain:air PC value for undecane was inconsistent with other n-alkane values. Using the measured partition coefficient values of these n-alkanes, linear regression was used to predict tissue (except brain) and blood:air partition coefficient values for larger n-alkanes, tridecane, tetradecane, pentadecane, hexadecane, and heptadecane (n-C13 to n-C17).Good agreement between measured and predicted tissue:air and blood:air partition coefficient values for n-C8 to n-C12 offer confidence in the partition coefficient predictions for longer chain n-alkanes.

Predicting biological activity: computational approach using novel distance based molecular descriptors

Four novel distance based molecular descriptors termed as superpendentic eccentric distance sum indices 1-4 (denoted by:∫P-1EDS, ∫P-2EDS, ∫P-3EDS and ∫P-4EDS) as well as their topochemical counterparts (denoted by:∫cP-1EDS, ∫cP-2EDS, ∫cP-3EDS and ∫cP-4EDS) have been conceptualized and developed in the present study. The sensitivity towards branching, discriminating power, and degeneracy of the proposed novel descriptors were investigated. Utility of these indices was investigated for development of models through decision tree and moving average analysis for the prediction of human corticotropin releasing factor-1 receptor binding affinity of substituted pyrazines. A wide variety of 46 2D and 3D molecular descriptors including proposed indices was employed for development of models through decision tree and moving average analysis. The calculation of most of these descriptors for each compound of the dataset was performed using online E-Dragon software (version 1.0). An in-house computer programme was also employed to calculate additional topological descriptors which did not figure in E-Dragon software. The decision tree classified and correctly predicted the input data with an impressive accuracy of 92% in the training set and 71% during cross-validation. A total of three descriptors, identified by decision tree, were subsequently utilized for development of suitable models using moving average analysis. These models predicted human corticotropin releasing factor-1 receptor binding affinity with an accuracy of ≥85%. The statistical significance of models was assessed through sensitivity, specificity and Matthew's correlation coefficient. High discriminating power, high sensitivity towards branching amalgamated with negligible degeneracy offer proposed descriptors a vast potential for use in the quantitative structure-activity/property/toxicity relationships so as to facilitate drug design.

QUANTITATIVE STRUCTURE–PROPERTY RELATIONSHIPS ON DISSOLVABILITY OF PCDD/Fs USING QUANTUM CHEMICAL DESCRIPTORS AND PARTIAL LEAST SQUARES

The environmental fate of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) has become a major issue in recent decades. Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting the properties of environmental organic pollutants from their structure descriptors. In this study, QSPR models were established for estimating water solubility (- log S W ) and n-octanol/water partition coefficient ( log KOW) of PCDD/Fs. Quantum chemical descriptors computed with density functional theory at the B3LYP/6-31G(d) level and partial least squares (PLS) analysis with an optimizing procedure were used to generate QSPR models for - log S W and log K OW of PCDD/Fs. Optimized models with high correlation coefficients (R2 > 0.983) were obtained for estimating - log S W and log K OW of PCDD/Fs. Both the internal cross validation test [Formula: see text] and external validation test (R2 > 0.965) results showed that the obtained models had high-precision and good prediction capability. The - log S W } and log K OW values predicted by the obtained models are very close to those observed. The PLS analysis indicated that PCDD/Fs with larger electronic spatial extent (R e ), lower molecular total energy (E T ), and smaller energy gap between the lowest unoccupied and the highest occupied molecular orbitals (E LUMO -E HOMO ) tend to be less soluble in water but more lipophilic.

ESTIMATION OF SOIL SORPTION COEFFICIENTS OF POLYCYCLIC AROMATIC HYDROCARBONS BY QUANTUM CHEMICAL DESCRIPTORS

Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic soil sorption coefficients (log K OC ) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed using density functional theory at the B3LYP/6-31G(d) level and partial least squares (PLS) analysis with an optimizing procedure were used to generate QSPR models for log K OC of PAHs. The correlation coefficient of the optimal model was 0.993, and the results of a cross-validation test ([Formula: see text]) showed this optimal model had high fitting precision and good predicting ability. The log K OC values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent tend to more easily adsorb and accumulate in soils and sediments, whereas those with higher molecular total energy and larger energy gap between the lowest unoccupied and the highest occupied molecular orbital adsorb and accumulate in soils and sediments less readily.

ESTIMATION OF DISSOLVABILITY OF CHLORIC AND ALKYL BENZENE DERIVATIVES USING QUANTUM CHEMICAL DESCRIPTORS AND PARTIAL LEAST SQUARES

Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports two optimal QSPR models for estimating water solubility ( log S W ) and n-octanol/water partition coefficient ( log K OW ) of chloric and alkyl benzene derivatives. Quantum chemical descriptors computed with density functional theory at B3LYP/6-31G(d) level and partial least squares (PLS) analysis with optimizing procedure were used for generating QSPR models for log S W and log K OW of chloric and alkyl benzene derivatives. The correlation coefficients of the optimal models for log S W and log K OW were 0.973 and 0.990, respectively. The results of internal cross-validation test and external validation test showed that both of the optimal models had high fitting precision and good predicting ability. The log S W and log K OW values predicted by the optimal models are very close to those observed. The PLS analysis indicated that chloric and alkyl benzene derivatives with larger electronic spatial extent and lower molecular total energy tend to be more hydrophobic and lipophilic, and smaller energy gap between the lowest unoccupied and the highest occupied molecular orbitals leads to larger dissolvability.

TOPOCHEMICAL MODELS FOR THE PREDICTION OF LIPOPHILICITY OF 1,3-DISUBSTITUTED PROPAN-2-ONE ANALOGS

In the present study, the relationship between the topochemical indices and log P values of 1,3-disubstituted propan-2-one analogs has been investigated. Three topochemical indices, Wiener's topochemical index — a distance-based topochemical descriptor, molecular connectivity topochemical index — an adjacency-based topochemical descriptor, and eccentric connectivity topochemical index — an adjacency-cum-distance-based topochemical descriptor, were used for the present investigation. The values of the Wiener's topochemical index, molecular connectivity topochemical index, and eccentric connectivity topochemical index were computed for each of the 45 analogs constituting the data set using an in-house computer program. The predicted log P values using leave-one-out (LOO) procedure exhibited a q2 of 0.72, 0.70, and 0.71 with reported log P values for Wiener's topochemical index, molecular connectivity topochemical index, and eccentric connectivity topochemical index, respectively. Separate models were developed using training set and log P of each analog in the independent test set was predicted using these models. The correlation of predicted log P values with the reported values, for independent test set, were in good agreement with those predicted using LOO procedure.

Quantitative structure-activity relationships for cycloguanil analogs as PfDHFR inhibitors using mathematical molecular descriptors

Computed molecular descriptors were used to develop quantitative structure-activity relationships (QSARs) for binding affinities (K(i)) for a set of 58 cycloguanil (2,4-diamino-1,6-dihydro-1,3,5-triazine) analogues for dihydrofolate reductase (DHFR) enzyme extracted from wild and A16V+S108T mutant type (a double mutation) malaria parasite Plasmodium falciparum (Pf). High-quality models were obtained in both cases. The results of statistical analyses show that ridge regression (RR) outperformed the two other modelling methods, principal component regression (PCR) and partial least squares (PLS). For both enzymes, recognition of the inhibitors was based on four broad categories of descriptors encoding information on: (1) the electronic character of the various atoms in the molecule, (2) the size and shape of the structure, (3) the degree of branching in the molecular skeleton, and (4) two to five atom molecular fragments with aliphatic carbon at one end and aliphatic or aromatic carbon or nitrogen at the other end. The subsets of influential descriptors underlying the QSARs for the wild versus the mutant DHFR are quite non-overlapping. This indicates that the two enzymes recognize the inhibitor molecules on the basis of mutually distinct structural attributes. Such differential QSARs can be useful in the design of novel drugs active against malaria parasites which are growing in resistant to existing chemotherapeutic agents.

QSAR of aminopyrido[2,3-d]pyrimidin-7-yl derivatives: anticancer drug design by computed descriptors

A series of aminopyrido[2,3-d]pyrimidin-7-yl derivatives acting as potential tyrosine kinase inhibitors having anticancer activities have been considered in the present investigation for the quantitative structure-activity relationship studies based on 2D and 3D QSAR approaches. For this purpose, various theoretical molecular descriptors were computed solely from the structures of these compounds. As the number of molecular descriptors greatly exceeds the number of observations, conventional regression does not produce reliable models and therefore, ridge regression methodology was used to solve this problem. The influence of different classes of molecular descriptors on the activity has been predicted and the most significant descriptors were obtained using the ridge regression models. Partial least squares (PLS) models were developed based on the training set for the 3D QSAR models of the above compounds. The influences of steric and electrostatic field effects generated by the contribution plots are discussed.

Predicting the vapour pressure of chemicals from structure: a comparison of graph theoretic versus quantum chemical descriptors

In this paper a set of graph theoretic molecular descriptors was used to predict the normal vapour pressure of a collection of 121 chlorinated organic chemicals. The easily calculated topological descriptors resulted in a robust quantitative structure-property relationship (QSPR) model with q(2) of 0.988, which is comparable to a model published previously developed using the computationally expensive density functional theory (DFT) method at the B3LYP level (Becke three-parameter exchange, Lee-Yang-Parr correlation). The addition of computer-intensive quantum chemical descriptors, including polarizability, to the set of topological descriptors did not improve the predictive ability of the model.

QSPR analysis of air-to-blood distribution of volatile organic compounds

Quantitative structure property relationship (QSPR) models for the prediction of human blood:air partition coefficient (log K(blood)) of volatile organic compounds (VOCs) has been developed based on the linear heuristic method (HM) and non-linear radial basis function neural networks (RBFNNs). Molecular descriptors that are calculated from the structures alone were used to represent the characteristics of the compounds. HM was used both to pre-select the whole descriptor sets and to build the linear model. RBFNN was performed to obtain more accurate models. Both the linear and the non-linear models can give very satisfactory prediction results: the correlation coefficient R was 0.964 and 0.979, and the root-mean-square (RMS) error was 0.3303 and 0.2542 for the whole data set, respectively. The prediction result of the non-linear model is better than that obtained by the linear model. In addition, this paper provides an effective method for predicting log K(blood) from its structures and gives some insight into the structural features related to the solubility of VOCs in human blood.

Integrated Decision-tree Testing Strategies for Acute Systemic Toxicity and Toxicokinetics with Respect to the Requirements of the EU REACH Legislation

Liverpool John Moores University and FRAME conducted a joint research project, sponsored by Defra, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with REACH. This paper focuses on the use of alternative (non-animal) methods (both in vitro and in silico) for acute systemic toxicity and toxicokinetic testing. The paper reviews in vitro tests based on basal cytotoxicity and target organ toxicity, along with QSAR models and expert systems available for this endpoint. The use of PBPK modelling for the prediction of ADME properties is also discussed. These tests are then incorporated into a decision-tree style, integrated testing strategy, which also includes the use of refined in vivo acute toxicity tests, as a last resort. The implementation of the strategy is intended to minimise the use of animals in the testing of acute systemic toxicity and toxicokinetics, whilst satisfying the scientific and logistical demands of the EU REACH legislation.

Estimation of n-octanol/water partition coefficients of polycyclic aromatic hydrocarbons by quantum chemical descriptors

Quantitative structure-property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic n-octanol/water partition coefficients (log K OW) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed with density functional theory at B3LYP/6-31G(d) level and partial least squares (PLS) analysis with optimizing procedure were used for generating QSPR models for log K OW of PAHs. The squared correlation coefficient (R 2) of the optimal model was 0.990, and the results of crossvalidation test (Q 2cum=0.976) showed this optimal model had high fitting precision and good predictability. The log K OW values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent and lower total energy values tend to be more hydrophobic and lipophilic.

Integrated decision-tree testing strategies for acute systemic toxicity and toxicokinetics with respect to the requirements of the EU REACH legislation

Liverpool John Moores University and FRAME conducted a joint research project, sponsored by Defra, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with REACH. This paper focuses on the use of alternative (non-animal) methods (both in vitro and in silico) for acute systemic toxicity and toxicokinetic testing. The paper reviews in vitro tests based on basal cytotoxicity and target organ toxicity, along with QSAR models and expert systems available for this endpoint. The use of PBPK modelling for the prediction of ADME properties is also discussed. These tests are then incorporated into a decision-tree style, integrated testing strategy, which also includes the use of refined in vivo acute toxicity tests, as a last resort. The implementation of the strategy is intended to minimise the use of animals in the testing of acute systemic toxicity and toxicokinetics, whilst satisfying the scientific and logistical demands of the EU REACH legislation.

QSPR checking and validation: a case study with hydroxy radical reaction rate constant

Traditionally, QSAR and QSPR models have been fitted by splitting the available compounds into separate learning and validation sets. The model is then fitted to the learning set and assessed using the validation set. Cross-validation (CV) uses all available compounds for both purposes, so that the full body of available information is brought to bear on both the learning and the validation portions of the study. The price paid for this additional information is a substantially greater computational load. A common mistake in using CV is to omit some of the repetitive computations. This mistake leads to substantial bias in the assessment. A hydroxyl radical reaction rate dataset is used to illustrate the superiority of CV and the pitfalls from its improper execution when modeling using nearest neighbors, paralleling behavior in the well-studied linear model setting.

An integrated QSPR-PBPK modelling approach for in vitro-in vivo extrapolation of pharmacokinetics in rats

In vitro data on metabolism and partitioning may be integrated within physiologically-based pharmacokinetic (PBPK) models to provide simulations of the kinetics and bioaccumulation of chemicals in intact organisms. Quantitative structure-property relationship (QSPR) modelling of available in vitro data may be performed to predict metabolism rates and partition coefficients (PCs) for developing in vivo PBPK models. The objective of the present study was to develop an integrated QSPR-PBPK modelling approach for the conduct of in vitro to in vivo extrapolation. For this purpose, data on rat blood:air (P(b)) and fat:air (P(f)) PCs, as well as intrinsic metabolic clearance (CL(int)) obtained using rat liver slices for some C(5)-C(10) volatile organic compounds (VOCs) were compiled from the literature. Multilinear additive QSPR models for P(f), P(b) and CL(int) were developed based on the number and nature of molecular fragments in these VOCs (CH(3), CH(2), CH, C, C=C, H, benzene ring and H in benzene ring structure). The mean estimated/experimental (est/exp) ratios (+/-SD; range) were 1.0 (+/-0.04; 0.93 - 1.06) for log P(f), 1.08 (+/-0.26; 0.70 - 1.62) for log P(b), and 1.07 (+/- 0.21; 0.80 - 1.44) for CL(int). By accounting for the difference in the content of neutral lipids in fat and other tissues, the liver : air and muscle : air PCs of the compounds investigated in this study, with the excerption of n-decane, were adequately predicted from P(f). Integrating the QSPRs for P(f), P(b) and CL(int) within a rat PBPK model, simulations of inhalation pharmacokinetics of several VOCs were generated on the basis of molecular structure, for a given exposure scenario. The integrated QSPR-PBPK model developed in this study is a potentially useful tool for predicting in vivo kinetics and bioaccumulation of chemicals in rats under poor data situations.

Implementing Molecular Connectivity Theory, a Basic Tool in Modeling Drugs

The concepts of chain graph, general graph, and complete graph have been used to implement the graph framework of molecular connectivity (MC) theory. Some concepts of this theory have been addressed using "external" theoretical concepts belonging mostly to quantum or structural chemistry, with no direct counterpart in graph theory. Thus, while the concept of chain graph can be used to tackle the cis-trans isomerism problem, the concept of pseudograph, or general graph can be used to tackle the description of the sigma-, pi-, and nonbonding n-electrons. The concept of complete graph can instead be used to tackle the electron core problem of the atoms of a molecule. Graph concepts can also be used to tackle the problem of the hydrogen contribution in hydrogen depleted graphs, which are encoded by the aid of a perturbation parameter, which differentiates between compounds with similar hydrogen-suppressed chemical graphs, like the graphs of CH(3)F and BH(2)F. These concepts have allowed redesign of a central parameter of MC theory, the valence delta, giving MC indices with improved model quality as exemplified here with different properties for each treated topic.

A quantitative structure-activity relationship (QSAR) study of dermal absorption using theoretical molecular descriptors

Quantitative structure-activity relationship (QSAR) models were developed for the prediction of dermal absorption based on experimental log Kp data for a diverse set of 101 chemicals obtained from the literature. Molecular descriptors including topostructural (TS), topochemical (TC), shape or three-dimensional (3D) and quantum chemical (QC) indices were calculated. Based on this information, a generic predictive model was created using the diverse set of 101 compounds. In addition, two submodels were prepared for subsets of 79 cyclic and 22 acyclic chemicals. A modified Gram-Schmidt variable reduction algorithm for descriptor thinning was followed by regression analyses using ridge regression (RR), principal components regression (PCR) and partial least squares regression (PLS). The RR results were found to be superior to PLS and PCR regressions. The cross-validated correlation coefficients for the full set and subsets were 0.67-0.87. Computational methods such as QSAR modelling can be used to augment existing data to prioritise chemicals that need to be studied further for toxicological evaluation and risk assessment.

Prediction of h5-HT2A receptor antagonistic activity of arylindoles: Computational approach using topochemical descriptors

Relationship between the topochemical indices and h5-HT2A receptor antagonistic activity of arylindoles has been investigated. Three topochemical indices, Wiener's topochemical index--a distance-based topochemical descriptor, molecular connectivity topochemical index--an adjacency-based topochemical descriptor and eccentric connectivity topochemical index--an adjacency-cum-distance based topochemical descriptor, were used for the present investigation. A data set comprising 31 differently substituted arylindoles was selected for the present study. The values of the Wiener's topochemical index, molecular connectivity topochemical index and eccentric connectivity topochemical index were computed for all the analogues involved in the data set using an in-house computer program. Resultant data was analyzed and suitable models were developed after identification of the active ranges. Subsequently, a biological activity was assigned to each analogue using these models, which was then compared with the reported h5-HT2A receptor antagonistic activity. Accuracy of prediction was found to vary from a minimum of approximately 81% to a maximum of approximately 84%.

Prediction of tissue: air partition coefficients--theoretical vs. experimental methods

Predictive QSAR models for rat and human tissue : air partition coefficients, namely blood : air, fat : air, brain : air, liver : air, muscle : air, and kidney : air were developed utilizing experimentally determined partition coefficients for 131 chemicals obtained from the literature and molecular descriptors based solely on chemical structure. The descriptors were partitioned into four hierarchical classes, including topostructural, topochemical, 3-dimensional, and ab initio quantum chemical. Three types of regression methodologies--ridge regression, principal components regression, and partial least squares regression--were used comparatively in the development of the structure-based models. In addition to the structure-based models, ordinary least squares regression was used to develop comparative models based on experimentally determined properties including saline : air and olive oil : air partition coefficients. The results of the study indicate that many of the structure-based models are comparable or superior to their respective property-based models. This is an important result considering that structural descriptors can be calculated quickly and inexpensively for both existing chemicals and those not yet synthesized. It was also found that ridge regression outperformed principal components regression and partial least squares regression, with respect to the structure-based models, and that generally the topochemical descriptors alone produced models of good predictive ability.

On an aspect of calculated molecular descriptors in QSAR studies of quinolone antibacterials

The re-emergence of tuberculosis infections, which are resistant to conventional drug therapy, has steadily risen in the last decade and as a result of that, fluoroquinolone drugs are being used as the second line of action. But there is hardly any study to examine specific structure activity relationships of quinolone antibacterials against mycobacteria. In this paper, an attempt has been made to establish a quantitative structure activity relationship modeling for a series of quinolone compounds against Mycobacterium fortuitum and Mycobacterium smegmatis. Due to lack of sufficient physicochemical data for the anti-mycobacterial compounds, it becomes very difficult to develop predictive methods based on experimental data. The present paper is an effort for the development of QSARs from the standpoint of physicochemical, constitutional, geometrical, electrostatic and topological indices. Molecular descriptors have been calculated solely from the chemical structure of N-1, C-7 and 8 substituted quinolone compounds and ridge regression models have been developed which can explain a better structure-activity relationship. Consideration of an intermolecular similarity analysis approach that led to a successful computer program development in PERL language has been used for comparing the influence of various molecular descriptors in different data subsets. The comparison of relative effectiveness of the calculated descriptors in our ridge regression model gives rise to some interesting results.

Analysis of algorithms predicting blood:air and tissue:blood partition coefficients from solvent partition coefficients for prevalent components of JP-8 jet fuel

Algorithms predicting tissue and blood partition coefficients (PCs) from solvent properties were compared to assess their usefulness in a petroleum mixture physiologically based pharmacokinetic/pharmacodynamic model. Measured blood:air and tissue:blood PCs for rat and human tissues were sought from literature resources for 14 prevalent jet fuel (JP-8) components. Average experimental PCs were compared with predicted PCs calculated using algorithms from 9 published sources. Algorithms chosen used solvent PCs (octanol:water, saline or water:air, oil:air coefficients) due to the relative accessibility of these parameters. Tissue:blood PCs were calculated from ratios of predicted tissue:air and experimental blood:air values (PCEB). Of the 231 calculated values, 27% performed within +/- 20% of the experimental PC values. Physiologically based equations (based on water and lipid components of a tissue type) did not perform as well as empirical equations (derived from linear regression of experimental PC data) and hybrid equations (physiological parameters and empirical factors combined) for the jet fuel components. The major limitation encountered in this analysis was the lack of experimental data for the selected JP-8 constituents. PCEB values were compared with tissue:blood PCs calculated from ratios of predicted tissue:air and predicted blood:air values (PCPB). Overall, 68% of PCEB values had smaller absolute % errors than PCPB values. If calculated PC values must be used in models, a comparison of experimental and predicted PCs for chemically similar compounds would estimate the expected error level in calculated values.

The hydrogen perturbation in molecular connectivity computations

A new algorithm for the delta(v) number, the basic parameter of molecular connectivity indices, is proposed. The new algorithm, which is centered on graph concepts like complete graphs and general graphs, encodes the information of the bonded hydrogen on different atoms through a perturbation parameter that makes use of no new graph concepts. The model quality of the new algorithm is tested with 13 properties of seven different classes of compounds, as well as with composite classes of compounds with the same property and with composite properties of the same class of compounds. Chosen properties and classes of compounds display different percentage of bonded hydrogen atoms, which allow a checking of the importance of this parameter. A comparison is drawn with previous results with zero contribution for the hydrogen perturbation as well as among results obtained by changing the number of compounds of a property but keeping constant the percentage of hydrogen atoms. Results underline the importance of the property as well as the importance of the number of compounds in determining the level of the hydrogen perturbation. Molecular connectivity terms are in some cases more critical than the combination of indices in detecting the perturbation introduced by the hydrogen atoms.

Quantitative structure-activity relationship modeling of insect juvenile hormone activity of 2,4-dienoates using computed molecular descriptors

Juvenile hormone (JH) activity of one hundred and eighty 2,4-dienoates reported for the larvae/pupae of six insect species was modeled using 915 atom pairs and 258 global molecular descriptors (topological and geometrical). Ridge regression, principal component regression and partial least square regression methods were used to model each of the JH activities. The use of all of the available parameters did not yield any good models, and extensive predictor trimming was necessary to improve the models. Ridge regression was found to give the best results among the three statistical tools used. The top ten molecular descriptors selected based on the t-statistic for each of the six models were found to be mostly atom pairs containing heteroatoms and topochemical descriptors. This suggests the importance of the chemical nature of the ligand rather than mere space-filling as the basis of the JH bioactivity. The residual plots indicate the existence of some non-linear relations, and recursive partitioning was used to capture any nonlinear relation between the bioassays and the molecular descriptors.

QSAR modeling of blood:air and tissue:air partition coefficients using theoretical descriptors

Human blood:air, human and rat tissue (fat, brain, liver, muscle, and kidney):air partition coefficients of a diverse set of organic compounds were correlated and predicted using structural descriptors by employing CODESSA-PRO and ISIDA programs. Four and five descriptor regression models developed using CODESSA-PRO were validated on three different test sets. Overall, these models have reasonable values of correlation coefficients (R(2)) and leave-one-out correlation coefficients (R(cv)(2)): R(2) = 0.881-0.983; R(cv)(2) = 0.826-0.962. Calculations with ISIDA resulted in models based on atom/bond sequences involving two to three atoms with statistical parameters that were similar to those of models obtained with CODESSA-PRO (R(2) = 0.911-0.974; R(cv)(2) = 0.831-0.936). A mixed pool of molecular and fragment descriptors did not lead to significant improvement of the models.

QSPR treatment of rat blood:air, saline:air and olive oil:air partition coefficients using theoretical molecular descriptors

A QSPR treatment has been applied to a data set that consists of 100 diverse organic compounds to relate the logarithmic function of rat blood:air, saline:air and olive oil:air partition coefficients (denoted by log K(b:a), log K(s:a), and log K(o:a), respectively), with theoretical molecular and fragment descriptors. Three QSPR models with squared correlation coefficients of 0.881, 0.926, and 0.922, respectively, were obtained. The verification of the predictive power of these models on a test set of 33 organic chemicals that were not included in the training set gave satisfactory squared correlation coefficients: 0.791 for rat blood:air, 0.794 for saline:air and 0.846 for olive oil:air.

Predicting blood:air partition coefficients using theoretical molecular descriptors

Three regression methods, namely ridge regression (RR), partial least squares (PLS), and principal components regression (PCR), were used to develop models for the prediction of rat blood:air partition coefficient for increasingly diverse data sets. Initially, modeling was performed for a set of 13 chlorocarbons. To this set, 10 additional hydrophobic compounds were added, including aromatic and non-aromatic hydrocarbons. A set of 16 hydrophilic compounds was also modeled separately. Finally, all 39 compounds were combined into one data set for which comprehensive models were developed. A large set of diverse, theoretical molecular descriptors was calculated for use in the current study. The topostructural (TS), topochemical (TC), and geometrical or 3-dimensional (3D) indices were used hierarchically in model development. In addition, single-class models were developed using the TS, TC, and 3D descriptors. In most cases, RR outperformed PLS and PCR, and the models developed using TC indices were superior to those developed using other classes of descriptors.