Showing papers in "Sar and Qsar in Environmental Research in 2009"

How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)

Abstract: Although thousands of quantitative structure–activity and structure–property relationships (QSARs/QSPRs) have been published, as well as numerous papers on the correct procedures for QSAR/QSPR analysis, many analyses are still carried out incorrectly, or in a less than satisfactory manner. We have identified 21 types of error that continue to be perpetrated in the QSAR/QSPR literature, and each of these is discussed, with examples (including some of our own). Where appropriate, we make recommendations for avoiding errors and for improving and enhancing QSAR/QSPR analyses.

Using chemical categories to fill data gaps in hazard assessment.

Abstract: Hazard assessments of chemicals have been limited by the availability of test data and the time needed to evaluate the test data. While available data may be inadequate for the majority of industrial chemicals, the body of existing knowledge for most hazards is large enough to permit reliable estimates to be made for untested chemicals without additional animal testing. We provide a summary of the growing use by regulatory agencies of the chemical categories approach, which groups chemicals based on their similar toxicological behaviour and fills in the data gaps in animal test data such as genotoxicity and aquatic toxicity. Although the categories approach may be distinguished from the use of quantitative structure-activity relationships (QSARs) for specific hazard endpoints, robust chemical categories are founded on quantifying the chemical structure with parameters that control chemical behaviour in conventional hazard assessment. The dissemination of the QSAR Application Toolbox by the Organisation for Economic Cooperation and Development (OECD) is an effort to facilitate the use of the categories approach and reduce the need for additional animal testing.

QNA-based ‘Star Track’ QSAR approach

Abstract: In the existing quantitative structure–activity relationship (QSAR) methods any molecule is represented as a single point in a many-dimensional space of molecular descriptors. We propose a new QSAR approach based on Quantitative Neighbourhoods of Atoms (QNA) descriptors, which characterize each atom of a molecule and depend on the whole molecule structure. In the ‘Star Track’ methodology any molecule is represented as a set of points in a two-dimensional space of QNA descriptors. With our new method the estimate of the target property of a chemical compound is calculated as the average value of the function of QNA descriptors in the points of the atoms of a molecule in QNA descriptor space. Substantially, we propose the use of only two descriptors rather than more than 3000 molecular descriptors that apply in the QSAR method. On the basis of this approach we have developed the computer program GUSAR and compared it with several widely used QSAR methods including CoMFA, CoMSIA, Golpe/GRID, HQSAR and others...

Prediction of acute mammalian toxicity from QSARs and interspecies correlations

Abstract: With the ever-growing number of xenobiotics that can potentially contaminate the environment, the determination of their mammalian toxicity is of prime importance. In this context, LD50 tests on rats and mice have been used for a long time to express the relative hazard associated with the acute toxicity of inorganic and organic chemicals. However, these laboratory tests encounter important hurdles. They are costly, time consuming and actively opposed by animal rights activists. Moreover, new legislation policies, such as REACH (Registration, Evaluation, Authorization and Restriction of Chemicals), aim at reducing the use of toxicity tests on vertebrates. Consequently, there is a need to find alternative methods for estimating the acute mammalian toxicity of chemicals. The quantitative structure-activity relationships (QSARs) and interspecies correlations appear particularly suited to reaching this goal. In this context, this paper reviews more than 150 models aiming at predicting rat and mouse LD50 values from molecular descriptors or (and) ecotoxicity data. The interest of these computational tools is discussed.

The acid–base profile of a contemporary set of drugs: implications for drug discovery

Abstract: Acid-base ionization constant (pK(a)) values have considerable influence on the physicochemical and pharmacokinetic properties of therapeutic substances. A set of 907 drugs was examined to determine the proportion of drugs that contain an ionizable group and the distribution of their pK(a) values. Using this contemporary set of compounds it was found that 64% of these compounds contained an ionizable group. Within this group of ionizable compounds, 34% contained a single basic group while only 20% contained a single acidic functional group. The single acid and single base containing substances were investigated further to examine the distributions of their pK(a) values. These data are discussed and analyzed with a focus on the entire set as well as central nervous system, non-central nervous system and oral drugs. The findings from this research will prompt pharmaceutical companies to assess the constitution of their screening libraries, such that focus is placed on the proportion of ionizable substances, the ratio of acids to bases and the distribution of pK(a) values.

The physicochemical basis of QSARs for baseline toxicity.

Abstract: The physico-chemical properties relevant to the equilibrium partitioning (bioconcentration) of chemicals between organisms and their respired media of water and air are reviewed and illustrated for chemicals that range in hydrophobicity. Relationships are then explored between freely dissolved external concentrations such as LC50s and chemical properties for one important toxicity mechanism, namely baseline toxicity or narcosis. The ‘activity hypothesis’ proposed by Ferguson in 1939 provides a coherent and compelling explanation for baseline toxicity of chemicals in both water- and air-respiring organisms, as well as a reference point for identifying more specific toxicity pathways. From inhalation studies with fish and rodents, narcosis is shown to occur at a chemical activity exceeding approximately 0.01 and there is no evidence of narcosis at activities less than 0.001. The activity hypothesis provides a framework for directly comparing the toxic potency of chemicals in both air- and water-breathing an...

Estimation of molecular diffusivity of pure chemicals in water: a quantitative structure-property relationship study.

Abstract: 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.

A QCAR model for predicting antioxidant activity of wild mushrooms

Abstract: Wild mushrooms have been described as sources of natural antioxidants, particularly phenolic compounds. However, many other compounds present in wild mushrooms can also act as antioxidants (reducers), so whole extracts from a wide range of species need to be examined. To gain further knowledge in this area, the relationship between the antioxidant potential (scavenging effect and reducing power) and chemical composition of twenty three samples from seventeen Portuguese wild mushroom species was investigated. A wide range of analytical parameters reported by our research group (including ash, carbohydrates, proteins, fat, monounsaturated fatty acids, polyunsaturated fatty acids, saturated fatty acids, phenolics, flavonoids, ascorbic acid and beta-carotene) were studied and the data were analysed by partial least squares (PLS) regression analysis to allow correlation of all the parameters. Antioxidant activity correlated well with phenolic and flavonoid contents. A QCAR (Quantitative Composition-Activity Relationships) model was constructed, using the PLS method, and its robustness and predictability were verified by internal and external cross-validation methods. Finally, this model proved to be a useful tool in the prediction of mushrooms' reducing power.

Spectral representation of reduced protein models

Abstract: We consider a spectrum-like two-dimensional graphical representation of proteins based on a reduced protein model in which 20 amino acids are grouped into five classes. This particular grouping of amino acids was suggested by Riddle and co-workers in 1997. The graphical representation is based on depicting sequentially the amino acids on five horizontal lines at equal separations. One-letter codes, B, O, U, X and Y, to which numerical values 1 to 5 have been assigned, are suggested as labels for the fictional amino acids that represent all the amino acids within each group. The approach is illustrated on ND6 proteins of eight species having from 168 to 175 amino acids. While visual inspection of the novel spectral graphical representations of proteins may reveal local similarities and dissimilarities of protein sequences, arithmetic manipulations of spectra offer an elegant route to graphic visualization of the degree of similarity for selected pairs of proteins.

Prediction of chemical carcinogenicity by machine learning approaches

Abstract: In this paper we report a successful application of machine learning approaches to the prediction of chemical carcinogenicity. Two different approaches, namely a support vector machine (SVM) and artificial neural network (ANN), were evaluated for predicting chemical carcinogenicity from molecular structure descriptors. A diverse set of 844 compounds, including 600 carcinogenic (CG+) and 244 noncarcinogenic (CG-) molecules, was used to estimate the accuracies of these approaches. The database was divided into two sets: the model construction set and the independent test set. Relevant molecular descriptors were selected by a hybrid feature selection method combining Fischer's score and Monte Carlo simulated annealing from a wide set of molecular descriptors, including physiochemical properties, constitutional, topological, and geometrical descriptors. The first model validation method was based a five-fold cross-validation method, in which the model construction set is split into five subsets. The five-fold cross-validation was used to select descriptors and optimise the model parameters by maximising the averaged overall accuracy. The final SVM model gave an averaged prediction accuracy of 90.7% for CG+ compounds, 81.6% for CG- compounds and 88.1% for the overall accuracy, while the corresponding ANN model provided an averaged prediction accuracy of 86.1% for CG+ compounds, 79.3% for CG- compounds and 84.2% for the overall accuracy. These results indicate that the hybrid feature selection method is very efficient and the selected descriptors are truly relevant to the carcinogenicity of compounds. Another model validation method, i.e. a hold-out method, was used to build the classification model using the selected descriptors and the optimised model parameters, in which the whole model construction set was used to build the classification model and the independent test set was used to test the predictive ability of the model. The SVM model gave a prediction accuracy of 87.6% for CG+ compounds, 79.1% for CG- compounds and 85.0% for the overall accuracy. The ANN model gave a prediction accuracy of 85.6% for CG+ compounds, 79.1% for CG- compounds and 83.6% for the overall accuracy. The results indicate that the built models are potentially useful for facilitating the prediction of chemical carcinogenicity of untested compounds.

3D-QSAR studies on triazolopiperazine amide inhibitors of dipeptidyl peptidase-IV as anti-diabetic agents.

Abstract: Three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses were carried out on 45 triazolopiperazine amide derivatives as dipeptidyl peptidase IV (DPP-IV) inhibitors in order to elucidate their antidiabetic activities. The studies include Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). Models with good predictive abilities were generated with the cross-validated r(2) (r(2)(cv)) and conventional r(2) values of 0.589 and 0.868 for CoMFA and 0.586 and 0.868 for CoMSIA, respectively. Both models were validated by a test set of nine compounds and gave satisfactory predictive r(2) (r(2)(pred)) values of 0.816 and 0.863, respectively. CoMFA and CoMSIA contour maps were then used to analyse the structural features of the ligands to account for the activity in terms of positively contributing physicochemical properties: steric, electrostatic, hydrophobic and hydrogen bond acceptor fields. The information obtained from CoMFA and CoMSIA three-dimensional contour maps can be used for further design of triazolopiperazine amide-based analogues as anti-diabetic agents.

Quantitative structure–activity relationship (QSAR) for insecticides: development of predictive in vivo insecticide activity models

Abstract: Quantitative structure–activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several t...

Mechanism-based categorization of aromatase inhibitors: a potential discovery and screening tool

Abstract: Cytochrome P450 aromatase is a key steroidogenic enzyme that converts androgens to estrogens in vertebrates. There is much interest in aromatase inhibitors (AIs) both because of their use as pharmaceuticals in the treatment of estrogen-sensitive breast cancers, and because a number of environmental contaminants can act as AIs, thereby disrupting endocrine function in humans and wildlife through suppression of circulating estrogen levels. The goal of the current work was to develop a mechanism-based structure–activity relationship (SAR) categorization framework highlighting the most important chemical structural features responsible for inhibition of aromatase activity. Two main interaction mechanisms were discerned: steroidal and non-steroidal. The steroid scaffold is most prominent when the structure of the target chemical is similar to the natural substrates of aromatase – androstenedione and testosterone. Chemicals acting by non-steroidal mechanism(s) possess a heteroatom (N, O, S) able to coordinate t...

Quantitative structure–property relationship modelling of the degradability rate constant of alkenes by OH radicals in atmosphere

Abstract: In this work, the degradability rate constants of 98 alkenes by OH radicals were predicted from theoretically derived descriptors, which were calculated from the molecular structure alone by applying a quantitative structure–property relationship (QSPR) approach. For the selection of the most relevant descriptors, stepwise multiple linear regression (MLR) and genetic algorithms (GAs) were used. Then some linear and nonlinear techniques were used for the investigation of the relation between selected molecular descriptors and the OH radical degradability rate constant. These methods were MLR, artificial neural networks (ANNs) and support vector machines (SVMs). According to the variable selection method and feature mapping techniques, six QSPR models were constructed which were: stepwise-MLR–MLR, stepwise-MLR–ANN, stepwise-MLR–SVM, GA–MLR, GA–ANN, and GA–SVM. The credibility of these models was evaluated by a leave-24-out cross-validation test. The statistical results are Q 2 = 0.86, SPRESS = 0.16 for GA–A...

A baseline inhalation toxicity model for narcosis in mammals.

Abstract: This paper presents the results of an analysis of the rodent inhalation literature and the development of a quantitative structure–activity relationships (QSAR) model for 4-hour LC50 as baseline toxicity to complement the baseline toxicity model for aquatic animals. We used the same literature review criteria developed for the ECOTOX database which selects only primary references with explicit experimental methods to form a high-quality database. Our literature review focused on the primary references reporting a 4-hour exposure for a single species of rodent in which the chemical had been clearly tested as a vapour and for which the exposure concentrations were not ambiguous. An expert system was used to remove reactive chemicals, receptor-mediated toxicants, and any test that produced symptoms inconsistent with non-polar narcosis. The QSAR model derived for narcosis in rodents was log LC50 = 0.69 × log VP + 1.54 which had an r 2 of 0.91, which is significantly better than the baseline toxicity model for...

Quantitative structure-activity relationship modelling of oral acute toxicity and cytotoxic activity of fragrance materials in rodents.

Abstract: Fragrance materials are used as ingredients in many consumer and personal care products. The wide and daily use of these substances, as well as their mainly uncontrolled discharge through domestic sewage, make fragrance materials both potential indoor and outdoor air pollutants which are also connected to possible toxic effects on humans (asthma, allergies, headaches). Unfortunately, little is known about the environmental fate and toxicity of these substances. However, the use of alternative, predictive approaches, such as quantitative structure-activity relationships (QSARs), can help in filling the data gap and in the characterization of the environmental and toxicological profile of these substances. In the proposed study, ordinary least squares regression-based QSAR models were developed for three toxicological endpoints: mouse oral LD(50), inhibition of NADH-oxidase (EC(50) NADH-Ox) and the effect on mitochondrial membrane potential (EC(50) DeltaPsim). Theoretical molecular descriptors were calculated by using DRAGON software, and the best QSAR models were developed according to the principles defined by the Organization for Economic Co-operation and Development.

Comparative PBT screening using (Q)SAR tools within REACH legislation

Abstract: Small to medium sized enterprises (SMEs) in the EU are facing challenges due to the introduction of new legislation designed to protect consumers and the environment, REACH (Registration, Evaluation, Authorisation and Restriction of CHemicals). There can be high costs associated with implementing REACH because data on mammalian toxicity, environmental toxicity and environmental fate properties is required and if this data is obtained experimentally the cost is significant. These costs can be reduced if reliable quantitative structure-activity relationships ((Q)SAR) models are instead used to obtain the required information. In this paper we investigate how easily freely available (Q)SAR models can be applied for persistent, bioaccumulative and toxic (PBT) screening of 17 chemicals of interest to SMEs. In this study the PBT predictions obtained from the more user-friendly PBT Profiler and the Danish(Q)SAR database for the chemicals were compared with the results taken directly from the EPI Suite software. It was found that these widely used (Q)SAR databases might have some errors and examples are provided. It was concluded that extra care must be taken when considering the use of these databases for PBT screening. In addition, to increase the likelihood of a correct prediction, data estimates from various (Q)SAR models relevant to the PBT endpoints must be compared.

Assessing the reproductive toxicity of some (con)azole compounds using a structure-activity relationship approach.

Abstract: The present research investigates the study of a set of 27 (con)azoles and their reproductive toxicity. (Con)azoles are used as fungicides and herbicides in agriculture for treatment of fruits, vegetables, cereals, and seeds, or as human antimycotic therapeutics. According to EEC Directive 91/414, active substances used in plant protection products must undergo reproductive toxicity testing. Reproductive toxicity is a complex biological endpoint, which includes many different biological processes and, therefore, it can only to a limited extent be assessed by a single quantitative structure–activity relationship (QSAR) model. The proposed SAR models are built using unsupervised methods, such as hierarchical clustering, principal component analysis and self-organizing maps, with the aim of studying the similarity relationships between structures. The molecular structures are represented with a set of topological and structural descriptors. The models showing clusters, closest neighbours or outliers may supp...

Data-driven model for the prediction of protein transmembrane regions

Abstract: We present a novel approach combining mathematical methods and artificial neural networks to predict the transmembrane regions of transmembrane proteins, considering protein sequence information alone. We have focused on developing a data-driven model based on a non-linear modelling method, the counter-propagation artificial neural network, and on mathematical descriptors defining the sequence information of transmembrane proteins with known three-dimensional structures. The developed model has proven to be promising in predicting protein transmembrane regions, with an error below 10% for the external validation set. In combination with available experimental data the model can give us a better understanding of transmembrane proteins.

In silico method for identification of promising anticancer drug targets.

Abstract: In recent years, the accumulation of the genomics, proteomics, transcriptomics data for topological and functional organization of regulatory networks in a cell has provided the possibility of identifying the potential targets involved in pathological processes and of selecting the most promising targets for future drug development. We propose an approach for anticancer drug target identification, which, using microarray data, allows discrete modelling of regulatory network behaviour. The effect of drugs inhibiting a particular protein or a combination of proteins in a regulatory network is analysed by simulation of a blockade of single nodes or their combinations. The method was applied to the four groups of breast cancer, HER2/neu-positive breast carcinomas, ductal carcinoma, invasive ductal carcinoma and/or a nodal metastasis, and to generalized breast cancer. As a result, some promising specific molecular targets and their combinations were identified. Inhibitors of some identified targets are known a...

Molecular dynamics study of prolyl oligopeptidase with inhibitor in binding cavity.

Abstract: We used the crystal structure of prolyl oligopeptidase (POP) with bound Z-pro-prolinal (ZPP) inhibitor (Protein Data Bank (PDB) structure 1QFS) to perform an intensive molecular dynamics study of the POP-ZPP complex. We performed 100 ns of simulation with the hemiacetal bond, through which the ZPP is bound to the POP, removed in order to better investigate the binding cavity environment. From basic analysis, measuring the radius of gyration, root mean square deviation, solvent accessible surface area and definition of the secondary structure of protein, we determined that the protein structure is highly stable and maintains its structure over the entire simulation time. This demonstrates that such long time simulations can be performed without the protein structure losing stability. We found that water bridges and hydrogen bonds play a negligible role in binding the ZPP thus indicating the importance of the hemiacetal bond. The two domains of the protein are bound by a set of approximately 12 hydrogen bonds, specific to the particular POP protein.

Fragment-Based Prediction of Cytochromes P450 2D6 and 1A2 Inhibition by Recursive Partitioning

Abstract: The evaluation of the ADME (absorption, distribution, metabolism, and excretion) properties of drug candidates is an important stage in drug discovery. To speed up the numerous tests carried out on large databases of compounds, the help of robust and accurate in silico filters is increasingly required. We propose here a method to build predictive and interpretable models for the prediction of cytochrome P450 (CYP) 1A2 and 2D6 inhibition using recursive partitioning (RP), a well-known technique for the construction of decision trees. The originality of the work is the use of several descriptions of the molecules in terms of fragments, i.e. the MACCS keys and five in-house fingerprints based on the electron density properties of fragments, employed to draw easily understandable structure-activity models. The classifiers reached performances of 87.5% and 76.5% of prediction on a validation set for CYP1A2 and CYP2D6, respectively. The analysis of the first nodes of the RP trees permits us to highlight some relations between the structural fragments and the inhibition of CYPs.

Identification of potential influenza virus endonuclease inhibitors through virtual screening based on the 3D-QSAR model

Abstract: Influenza endonucleases have appeared as an attractive target of antiviral therapy for influenza infection. With the purpose of designing a novel antiviral agent with enhanced biological activities against influenza endonuclease, a three-dimensional quantitative structure-activity relationships (3D-QSAR) model was generated based on 34 influenza endonuclease inhibitors. The comparative molecular similarity index analysis (CoMSIA) with a steric, electrostatic and hydrophobic (SEH) model showed the best correlative and predictive capability (q 2 = 0.763, r 2 = 0.969 and F = 174.785), which provided a pharmacophore composed of the electronegative moiety as well as the bulky hydrophobic group. The CoMSIA model was used as a pharmacophore query in the UNITY search of the ChemDiv compound library to give virtual active compounds. The 3D-QSAR model was then used to predict the activity of the selected compounds, which identified three compounds as the most likely inhibitor candidates.

A QSAR investigation of dermal and respiratory chemical sensitizers based on computational chemistry properties.

Abstract: A wide range of physicochemical properties based on molecular topology, size and shape, and semi-empirical molecular orbital theory were calculated for a variety of dermal and respiratory sensitizers, as well as some non-active substances. Compounds were randomly selected to belong to a training set of substances (approximately 90%) for development of quantitative structure–activity relationship (QSAR) models or to a test set (approximately 10%) for testing the models. A choice was made of those descriptors which were related to sensitization using standard statistics. Pattern recognition methods were then utilized to identify the combination of properties that provided the greatest contribution to the observed biological effect. Principal components (PC) analysis was then performed on the most important properties. The models derived were then applied to a test set of known sensitizers to predict their class. For dermal and respiratory sensitizers respectively, the PC model classified five (100%) of the ...

QSAR study of selective I1-imidazoline receptor ligands

Abstract: Selective imidazoline1-receptor (I1-R) ligands are used clinically to reduce blood pressure. Thus, there is significant interest in developing new imidazoline analogs with high selectivity and affinity for I1 receptors. A quantitative structure–activity relationship (QSAR) study was carried out on 11 potent I1-R ligands (derivatives of imidazoline, oxazoline and pyrroline) using a multiple linear regression (MLR) procedure. The selected compounds have been studied using B3LYP/3–21G(d, p) and B3LYP/6–31G(d, p) methods. Among the 42 descriptors that were considered in generating the QSAR model, three descriptors (partial atomic charges of nitrogen in the heterocyclic moiety (N-2 charge), log D and the dipole moment of the ligands) resulted in a statistically significant model with r 2 > 0.874 and > 0.802. The QSAR models were validated through cross-validation and external test set prediction. The aim of the developed MLR models for the I1-R ligands was to link the structures to their reported I1-R binding ...

DFT study on the bromination pattern dependence of electronic properties and their validity in quantitative structure–activity relationships of polybrominated diphenyl ethers

Abstract: With quantum chemical computation of density functional theory (DFT), the electronic properties including the polarisabilities, polarisability anisotropies and quadrupole moments of a total of 209 congeners of polybrominated diphenyl ethers (PBDEs) were evaluated. The electronic properties were shown to be highly dependent on the bromination pattern, i.e. their values changed sensitively with the number and sites of bromination. Being similar to the 2,3,7,8-, 1,4,6,9-chlorination of dioxins, respectively, 3,3′,4,4′-, 2,2′,5,5′-bromination of PBDEs can impose relatively greater effects on the electronic properties. Some of electronic properties were found to be potent in explaining the variance of toxicity, and the potency was verified by the development of quantitative structure–activity relationships (QSARs). To further improve the stability and predictability of QSARs for toxicity, two-dimensional topological indices were introduced. In QSARs, polarisability anisotropy was more significant than other po...

QSAR models for P450 (2D6) substrate activity

Abstract: Human Cytochrome P450 (CYP) is a large group of enzymes that possess an essential function in metabolising different exogenous and endogenous compounds. Humans have more than 50 different genes encoding CYP enzymes, among these a gene encoding for the CYP isoenzyme 2D6, a CYP able to metabolise drugs and other chemicals. A training set of 747 chemicals primarily based on in vivo human data for the CYP isoenzyme 2D6 was collected from the literature. QSAR models focusing on substrate/non-substrate activity were constructed by the use of MultiCASE, Leadscope and MDL quantitative structure–activity relationship (QSAR) modelling systems. They cross validated (leave-groups-out) with concordances of 71%, 81% and 82%, respectively. Discrete organic European Inventory of Existing Commercial Chemical Substances (EINECS) chemicals were screened to predict an approximate percentage of CYP 2D6 substrates. These chemicals are potentially present in the environment. The biological importance of the CYP 2D6 and the use ...

Prediction of biomagnification factors for some organochlorine compounds using linear free energy relationship parameters and artificial neural networks.

Abstract: Multiple linear regression and artificial neural networks (ANNs) as feature mapping techniques were used for the prediction of the biomagnification factor (BMF) of some organochlorine pollutants. As independent variables, or compound descriptors, the Abraham descriptors often employed in linear free energy relationships were used. Much better results were obtained from the nonlinear ANN model than from multiple linear regression. The average absolute error, average relative error and root mean square error in the calculation of log (BMF) by the ANN model were 0.055, 0.051 and 0.097 for the training set and 0.11, 0.086 and 0.175 for the internal validation set, respectively. The degree of importance of each descriptor was evaluated by carrying out a sensitivity analysis approach for the nonlinear model. The results obtained reveal that the order of importance is the pollutant volume, the pollutant dipolarity/polarizability and the pollutant excess molar refraction. In order to examine the credibility of th...

A quantum chemical and chemometrical study of indolo[2,1-b]quinazoline and their analogues with cytotoxic activity against breast cancer cells.

Abstract: Some indolo[2,1-b]quinalozine (tryptanthrin) analogues present cytotoxic activity against human breast cancer cells. In this work, chemometric methods were applied in the search for building discriminant models between active and inactive analogues, based on the correlations among their in vitro cytotoxic activities and their electronic and geometric molecular descriptors. From 88 descriptors calculated with density functional theory with the exchange correlation functional B3LYP and the basis set 6-31G* (Gaussian 03), 29 were pre-selected based on their Fisher weights, and finally five descriptors (partial charge on atom 15, bond orders between atoms 12–13, 17–25 and 18–26, and energy difference between frontier molecular orbitals) were selected for principal component analysis. This analysis was able to discriminate 12 inactive from 22 active analogues by using only one principal component, accounting for 49% of the total variance and allowing us to better understand the influence of these electronic de...

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

Abstract: 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 0988, 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