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QSAR
Terms modified by QSAR Selected AbstractsToxicity assessment of mono-substituted benzenes and phenols using a Pseudomonas initial oxygen uptake assayENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 2 2005Ded-Shih Huang Abstract A methodology is presented for assessing the toxicity of chemical substances through their inhibitory action toward the Pseudomonas initial oxygen uptake (PIOU) rate. The current studies reveal that the PIOU assay is rapid, cost-efficient, and easy to perform. The oxygen uptake rate was found to be associated with a putative benzoate transporter and highly dependent on benzoate concentration. The putative benzoate transporter has been shown to follow Michaelis,Menten kinetics. Most phenols were found to be noncompetitive inhibitors of the benzoate transporter. The inhibition constant (Ki) of these noncompetitive inhibitors can be related to the concentration causing 50% oxygen uptake inhibition in Pseudomonas putida. Modeling these data by using the response,surface approach leads to the development of a quantitative structure,activity relationship (QSAR) for the toxicity of phenols ((1/Ki) = ,0.435 (±0.038) lowest-unoccupied-molecular orbital + 0.517 (±0.027)log KOW ,2.340 (±0.068), n = 49, r2 = 0.930, s = 0.107, r2adj = 0.926, F = 303.1). A comparison of QSAR models derived from the Ki data of the PIOU method and the toxicity data of 40-h Tetrahymena pyrifomis growth inhibition assay (Tetratox) indicated that there was a high correlation between the two approaches (r2 = 0.925). [source] Algal toxicity of nitrobenzenes: Combined effect analysis as a pharmacological probe for similar modes of interactionENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 2 2005Rolf Altenburger Abstract An analysis regarding the effects of the mixture toxicity of different nitrobenzenes on the reproduction of the green alga Scenedesmus vacuolatus was undertaken using the concepts of concentration addition and response addition. Using lipophilicity-based quantitative structure-activity relationship (QSAR) modeling for nitrobenzenes, the assumption is held that mononitrobenzenes may exert narcotic effects as a common type of action, whereas dinitrobenzenes show a somewhat greater toxicity. From the literature, QSARs based on quantum chemical parameters suggest that some mononitrobenzenes may be effective through additional other modes of action. The toxicity of a mixture of 14 nitrobenzenes clearly exceeds the predicted combined effects, as expected for the sum of toxic units from a uniform narcotic mode of action. Moreover, the observed combined effect is smaller than that predicted from similarly acting compounds calculated on the basis of the parameterized dose-response functions using concentration addition. Further modeling of the combined effect, joining the models of concentration addition for components with anticipated similar modes of action and of response addition for those with independent action, led us to propose that not all nitrobenzenes follow the same mode of action. This idea is in line with the hypothesis derived from quantum chemical QSAR considerations. Most interestingly, the methodology introduced here uses combined effect analysis as a pharmacological probe to test for similarity in the mode of action of mixture components. [source] Quantitative structure-activity relationship methods: Perspectives on drug discovery and toxicologyENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 8 2003Roger Perkins Abstract Quantitative structure,activity relationships (QSARs) attempt to correlate chemical structure with activity using statistical approaches. The QSAR models are useful for various purposes including the prediction of activities of untested chemicals. Quantitative structure,activity relationships and other related approaches have attracted broad scientific interest, particularly in the pharmaceutical industry for drug discovery and in toxicology and environmental science for risk assessment. An assortment of new QSAR methods have been developed during the past decade, most of them focused on drug discovery. Besides advancing our fundamental knowledge of QSARs, these scientific efforts have stimulated their application in a wider range of disciplines, such as toxicology, where QSARs have not yet gained full appreciation. In this review, we attempt to summarize the status of QSAR with emphasis on illuminating the utility and limitations of QSAR technology. We will first review two-dimensional (2D) QSAR with a discussion of the availability and appropriate selection of molecular descriptors. We will then proceed to describe three-dimensional (3D) QSAR and key issues associated with this technology, then compare the relative suitability of 2D and 3D QSAR for different applications. Given the recent technological advances in biological research for rapid identification of drug targets, we mention several examples in which QSAR approaches are employed in conjunction with improved knowledge of the structure and function of the target receptor. The review will conclude by discussing statistical validation of QSAR models, a topic that has received sparse attention in recent years despite its critical importance. [source] Effects of eight polycyclic aromatic compounds on the survival and reproduction of the springtail Folsomia fimetaria L. (collembola, isotomidae)ENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 6 2001Line E. Sverdrup Abstract The effects of eight polycyclic aromatic compounds on the survival and reproduction of the collembolan Folsomia fimetaria L. were investigated in a well-characterized Danish agricultural soil. With the exception of acridine, polycyclic aromatic hydrocarbons (PAHs) and neutral N-, S-, and O-monosubstituted analogues showed similar toxicities to soil collembolans when the results were expressed in relation to total soil concentrations (mg/kg). The estimated concentrations resulting in a 10% reduction of reproductive output (EC10 values) were based on measured initial concentrations and were for acridine 290 mg/kg, carbazole 10 mg/kg, dibenzofuran 19 mg/kg, dibenzothiophene 7.8 mg/kg, fluoranthene 37 mg/kg, fluorene 7.7 mg/kg, phenantrene 23 mg/kg, and pyrene 10 mg/kg. When the EC10 values were converted to soil pore-water concentrations, they showed a highly significant correlation (r2 = 0.71, p < 0.01) to no-observed-effect concentrations for the freshwater crustacean Daphnia magna, as estimated by a quantitative structure activity relation (QSAR) for baseline toxicity (nonpolar narcosis). Only carbazole and acridine were more than two times more toxic (4.9 and 3.1, respectively) than expected from the Daphnia QSAR data. The latter result indicates that the toxicity of the tested substances is close to that expected for compounds with nonpolar narcosis as the mode of action. However, the relatively large uncertainties in the extrapolation method prevent final conclusions from being drawn. [source] A QSAR analysis of toxicity of Aconitum alkaloidsFUNDAMENTAL & CLINICAL PHARMACOLOGY, Issue 6 2004Angélica M. Bello-Ramírez Abstract Biological activity of Aconitum alkaloids may be related to their toxicity rather than to a specific pharmacological action. A Quantitative structure-activity relationships (QSAR) analysis was performed on the following two groups of alkaloids: compounds with an aroyl/aroyloxy group at R14 position (yunaconitine, bulleyaconitine, aconitine, beiwutine, nagarine, 3-acetyl aconitine, and penduline), and compounds with the aroyloxy group at R4 position (N -deacetyllappaconitine, lappaconitine, ranaconitine, N -deacetylfinaconitine, N -deacetylranaconitine). The LD50 (,mol/kg) of the 12 alkaloids were obtained from the literature. LD50 was significantly lower in group 1 than in group 2. The steric and core,core repulsion energies were significantly higher in group 1. The total energy and heat of formation and electronic energies were significantly lower in group 1. The reactivity index of N, C1,, C4, and C6, were similar between groups. The reactivity index of C2, was significantly higher and the reactivity index of C3, and C5, were significantly lower in group 1. Log P and pKa were similar between groups. Molecular weight was significantly higher in group 1. A significant linear relationship was observed between log LD50 and either analgesic log ED50 or local anesthetic log ED50. The LD50/analgesic ED50 obtained from average values was 5.9 for group 1 and 5.0 for group 2. However, the LD50/local anesthetic ED50 was 40.4 and 318, respectively. The study supports that the analgesic effects of these alkaloids are secondary to their toxic effects whereas alkaloids from group 2 are susceptible to be further studied as local anesthetic agents. [source] Theoretical studies of some sulphonamides as corrosion inhibitors for mild steel in acidic mediumINTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, Issue 14 2010Eno E. Ebenso Abstract Density functional theory (DFT) at the B3LYP/6-31G (d,p) and BP86/CEP-31G* basis set levels and ab initio calculations using the RHF/6-31G (d,p) methods were performed on four sulfonamides (namely sulfaacetamide (SAM), sulfapyridine (SPY), sulfamerazine (SMR), and sulfathiazole (STI)) used as corrosion inhibitors for mild steel in acidic medium to determine the relationship between molecular structure and their inhibition efficiencies (%IE). The order of inhibition efficiency obtained was SMR > SPY > STI > SAM which corresponded with the order of most of the calculated quantum chemical parameters namely EHOMO (highest occupied molecular orbital energy), ELUMO (lowest unoccupied molecular orbital energy), the energy gap (,E), the Mulliken charges on the C, O, N, S atoms, hardness (,), softness (S), polarizability (,), dipole moment (,), total energy change (,ET), electrophilicity (,), electron affinity (A), ionization potential (I), the absolute electronegativity (,), and the fraction of electrons transferred (,N). Quantitative structure activity relationship (QSAR) approach has been used and a correlation of the composite index of some of the quantum chemical parameters was performed to characterize the inhibition performance of the sulfonamides studied. The results showed that the %IE of the sulfonamides was closely related to some of the quantum chemical parameters but with varying degrees/order. The calculated %IE of the sulfonamides studied was found to be close to their experimental corrosion inhibition efficiencies. The experimental data obtained fits the Langmuir adsorption isotherm. The negative sign of the EHOMO values and other thermodynamic parameters obtained indicates that the data obtained supports physical adsorption mechanism. © 2009 Wiley Periodicals, Inc. Int J Quantum Chem, 2010 [source] Structure-hepatoprotective activity relationship study of sesquiterpene lactones: A QSAR analysisINTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, Issue 1 2009Yuliya Paukku Abstract This study has been carried out using quantitative structure-activity relationship analysis (QSAR) for 22 sesquiterpene lactones to correlate and predict their hepatoprotective activity. Sesquiterpenoids, the largest class of terpenoids, are a widespread group of substances occurring in various plant organisms. QSAR analysis was carried out using methods such as genetic algorithm for variables selection among generated and calculated descriptors and multiple linear regression analysis. Quantum-chemical calculations have been performed by density functional theory at B3LYP/6-311G(d, p) level for evaluation of electronic properties using reference geometries optimized by semi-empirical AM1 approach. Three models describing hepatoprotective activity values for series of sesquiterpene lactones are proposed. The obtained models are useful for description of sesquiterpene lactones hepatoprotective activity and can be used to estimate the hepatoprotective activity of new substituted sesquiterpene lactones. The models obtained in our study show not only statistical significance, but also good predictive ability. The estimated predictive ability (r) of these models lies within 0.942,0.969. © 2008 Wiley Periodicals, Inc. Int J Quantum Chem, 2009 [source] Solvent effect on the reactivity of CIS -platinum (II) complexes: A density functional approachINTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, Issue 8 2008Pubalee Sarmah Abstract The structure and chemical reactivity of some selected cis -platinum(II) complexes, including clinically used drug molecules, cisplatin, carboplatin, and oxaliplatin are investigated using density functional theory (DFT) calculations. Calculated geometries of the complexes are in agreement with their available X-ray data. The global and local reactivity descriptors, such as hardness, chemical potential, electrophilicity index, Fukui function, and local philicity are calculated to investigate the usefulness of these descriptors for understanding the reactive nature and reactive sites of the complexes. Inclusion of solvent effect shows that both global and local descriptors change the trend of reactivity with respect to their trend in the gas phase. The stability of the complexes increases with the inclusion of water molecules. Simple regression analysis is applied to build up a quantitative structure-activity relationship (QSAR) model based on DFT derived electrophilicity index for the Pt(II) complexes against A2780 human ovarian adenocarcinoma cell line to establish the importance of the descriptor in predicting cytotoxicity. © 2008 Wiley Periodicals, Inc. Int J Quantum Chem, 2008 [source] Theory and applications of the integrated molecular transform and the normalized molecular moment structure descriptors: QSAR and QSPR paradigmsINTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, Issue 6 2001Stephen P. Molnar Abstract The chemical and mathematical rationalé in the development of the numerically unitary integrated molecular transform (FTm), its analogous electronic (FTe) and charge (FTc) transforms, and the normalized molecular moment (Mn), and its analogous electronic (Me) and charge (Mc) moment as molecular structure descriptors are presented. The reported application and utilization of these indices for predictive capability correlations of structure with physicochemical, pharmacological, and thermodynamic properties are reviewed. The further use of these descriptors in considerations of chemical similarity is noted, as is their capability for searching compound databases whose contents include the noted calculated indices. Their potential for characterizing peptides, proteins, and oligomers as well as their possible function in mathematical models is discussed. © 2001 John Wiley & Sons, Inc. Int J Quantum Chem, 2001 [source] Quantitative structure,activity relationship study on the inhibitors of fatty acid amide hydrolaseJOURNAL OF CHEMOMETRICS, Issue 9 2010Peng Lu Abstract A quantitative structure activity relationship (QSAR) analysis was performed on the values of a series of fatty acid amide hydrolase (FAAH) inhibitors. Six molecular descriptors selected by CODESSA software were used as inputs to perform heuristic method (HM) and support vector machine (SVM). The results obtained by SVM were compared with those obtained by the HM. The root mean square errors (RMSEs) for the training set given by HM and SVM were 0.555 and 0.404, respectively, which shows that the performance of the SVM model is better than that of the HM model. This paper provides a new and effective method for predicting the activity of FAAH inhibitors. Copyright © 2010 John Wiley & Sons, Ltd. [source] Modeling based on subspace orthogonal projections for QSAR and QSPR researchJOURNAL OF CHEMOMETRICS, Issue 1 2008Yizeng Liang Abstract A novel projection modeling method for quantitative structure activity relationship (QSAR) and quantitative structure property relationship (QSPR) is developed in this paper. Orthogonalization of block variables is introduced to deal with the problem of variable selection. Projections based on least squares are used to construct the modeling space in order to search for the best regression directions for chemical modeling. A suitable prediction space for such a model is further defined to confine the usage range of the model. Three real data sets were analyzed to check the performance of the proposed modeling method. The results obtained from Monte-Carlo cross-validation (MCCV) showed that the proposed modeling method might provide better results for QSAR and QSPR modeling than PCR and PLS with respect to both fitting and prediction abilities. Copyright © 2007 John Wiley & Sons, Ltd. [source] An alignment-free methodology for modelling field-based 3D-structure activity relationships using inductive logic programmingJOURNAL OF CHEMOMETRICS, Issue 12 2007Bĺrd Buttingsrud Abstract Traditional 3D-quantitative structure,activity relationship (QSAR)/structure,activity relationship (SAR) methodologies are sensitive to the quality of an alignment step which is required to make molecular structures comparable. Even though many methods have been proposed to solve this problem, they often result in a loss of model interpretability. The requirement of alignment is a restriction imposed by traditional regression methods due to their failure to represent relations between data objects directly. Inductive logic programming (ILP) is a class of machine-learning methods able to describe relational data directly. We propose a new methodology which is aimed at using the richness in molecular interaction fields (MIFs) without being restricted by any alignment procedure. A set of MIFs is computed and further compressed by finding their minima corresponding to the sites of strongest interaction between a molecule and the applied test probe. ILP uses these minima to build easily interpretable rules about activity expressed as pharmacophore rules in the powerful language of first-order logic. We use a set of previously published inhibitors of factor Xa of the benzamidine family to discuss the problems, requirements and advantages of the new methodology. Copyright © 2007 John Wiley & Sons, Ltd. [source] Impartial graphical comparison of multivariate calibration methods and the harmony/parsimony tradeoffJOURNAL OF CHEMOMETRICS, Issue 11-12 2006Forrest Stout Abstract For multivariate calibration with the relationship y,=,Xb, it is often necessary to determine the degrees of freedom for parsimony consideration and for the error measure root mean square error of calibration (RMSEC). This paper shows that degrees of freedom can be estimated by an effective rank (ER) measure to estimate the model fitting degrees of freedom and the more parsimonious model has the smallest ER. This paper also shows that when such a measure is used on the X-axis, simultaneous graphing of model errors and other regression diagnostics is possible for ridge regression (RR), partial least squares (PLS) and principal component regression (PCR) and thus, a fair comparison between all potential models can be accomplished. The ER approach is general and applicable to other multivariate calibration methods. It is often noted that by selecting variables, more parsimonious models are obtained; typically by multiple linear regression (MLR). By using the ER, the more parsimonious model is graphically shown to not always be the MLR model. Additionally, a harmony measure is proposed that expresses the bias/variance tradeoff for a particular model. By plotting this new measure against the ER, the proper harmony/parsimony tradeoff can be graphically assessed for RR, PCR and PLS. Essentially, pluralistic criteria for fairly valuating and characterizing models are better than a dualistic or a single criterion approach which is the usual tactic. Results are presented using spectral, industrial and quantitative structure activity relationship (QSAR) data. Copyright © 2007 John Wiley & Sons, Ltd. [source] A combined molecular modeling study on gelatinases and their potent inhibitorsJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 1 2010Lili Xi Abstract Zinc-dependent matrix metalloproteinase (MMP) family is considered to be an attractive target because of its important role in many physiological and pathological processes. In the present work, a molecular modeling study combining protein-, ligand- and complex-based computational methods was performed to analyze a new series of ,- N -biaryl ether sulfonamide hydroxamates as potent inhibitors of gelatinase A (MMP-2) and gelatinase B (MMP-9). Firstly, the similarities and differences between the binding sites of MMP-2 and MMP-9 were analyzed through sequence alignment and structural superimposition. Secondly, in order to extract structural features influencing the activities of these inhibitors, quantitative structure-activity relationship (QSAR) models using genetic algorithm-multiple linear regression (GA-MLR), comparative molecular field (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were developed. The proposed QSAR models could give good predictive ability for the studied inhibitors. Thirdly, docking study was employed to further explore the binding mode between the ligand and protein. The results from all the above analyses could provide the information about the similarities and differences of the binding mode between the MMP-2, MMP-9 and their potent inhibitors. The obtained results can provide very useful information for the design of new potential inhibitors. © 2009 Wiley Periodicals, Inc. J Comput Chem 2010 [source] Unified QSAR & network-based computational chemistry approach to antimicrobials.JOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 1 2010Abstract In the previous work, we reported a multitarget Quantitative Structure-Activity Relationship (mt-QSAR) model to predict drug activity against different fungal species. This mt-QSAR allowed us to construct a drug,drug multispecies Complex Network (msCN) to investigate drug,drug similarity (González-Díaz and Prado-Prado, J Comput Chem 2008, 29, 656). However, important methodological points remained unclear, such as follows: (1) the accuracy of the methods when applied to other problems; (2) the effect of the distance type used to construct the msCN; (3) how to perform the inverse procedure to study species,species similarity with multidrug resistance CNs (mdrCN); and (4) the implications and necessary steps to perform a substructural Triadic Census Analysis (TCA) of the msCN. To continue the present series with other important problem, we developed here a mt-QSAR model for more than 700 drugs tested in the literature against different parasites (predicting antiparasitic drugs). The data were processed by Linear Discriminate Analysis (LDA) and the model classifies correctly 93.62% (1160 out of 1239 cases) in training. The model validation was carried out by means of external predicting series; the model classified 573 out of 607, that is, 94.4% of cases. Next, we carried out the first comparative study of the topology of six different drug,drug msCNs based on six different distances such as Euclidean, Chebychev, Manhattan, etc. Furthermore, we compared the selected drug,drug msCN and species,species mdsCN with random networks. We also introduced here the inverse methodology to construct species,species msCN based on a mt-QSAR model. Last, we reported the first substructural analysis of drug,drug msCN using Triadic Census Analysis (TCA) algorithm. © 2009 Wiley Periodicals, Inc. J Comput Chem 2010 [source] Conceptual DFT properties-based 3D QSAR: Analysis of inhibitors of the nicotine metabolizing CYP2A6 enzymeJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 12 2009Sofie Van Damme Abstract Structure-activity relationships of 46 P450 2A6 inhibitors were analyzed using the 3D-QSAR methodology. The analysis was carried out to confront the use of traditional steric and electrostatic fields with that of a number of fields reflecting conceptual DFT properties: electron density, HOMO, LUMO, and Fukui f, function as 3D fields. The most predictive models were obtained by combining the information of the electron density with the Fukui f, function (r2 = 0.82, q2 = 0.72), yielding a statistically significant and predictive model. The generated model was able to predict the inhibition potencies of an external test set of five chemicals. The result of the analysis indicates that conceptual DFT-based molecular fields can be useful as 3D QSAR molecular interaction fields. © 2008 Wiley Periodicals, Inc. J Comput Chem 2009 [source] QSAR model for alignment-free prediction of human breast cancer biomarkers based on electrostatic potentials of protein pseudofolding HP-lattice networksJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 16 2008Santiago Vilar Abstract Network theory allows relationships to be established between numerical parameters that describe the molecular structure of genes and proteins and their biological properties. These models can be considered as quantitative structure,activity relationships (QSAR) for biopolymers. The work described here concerns the first QSAR model for 122 proteins that are associated with human breast cancer (HBC), as identified experimentally by Sjöblom et al. (Science 2006, 314, 268) from over 10,000 human proteins. In this study, the 122 proteins related to HBC (HBCp) and a control group of 200 proteins that are not related to HBC (non-HBCp) were forced to fold in an HP lattice network. From these networks a series of electrostatic potential parameters (,k) was calculated to describe each protein numerically. The use of ,k as an entry point to linear discriminant analysis led to a QSAR model to discriminate between HBCp and non-HBCp, and this model could help to predict the involvement of a certain gene and/or protein in HBC. In addition, validation procedures were carried out on the model and these included an external prediction series and evaluation of an additional series of 1000 non-HBCp. In all cases good levels of classification were obtained with values above 80%. This study represents the first example of a QSAR model for the computational chemistry inspired search of potential HBC protein biomarkers. © 2008 Wiley Periodicals, Inc. J Comput Chem 2008 [source] Computational modeling of tetrahydroimidazo-[4,5,1-jk][1,4]-benzodiazepinone derivatives: An atomistic drug design approach using Kier-Hall electrotopological state (E-state) indicesJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 11 2008Nitin S. Sapre Abstract Quantitative structure-activity relationships (QSAR), based on E-state indices have been developed for a series of tetrahydroimidazo-[4,5,1-jk]-benzodiazepinone derivatives against HIV-1 reverse transcriptase (HIV-1 RT). Statistical modeling using multiple linear regression technique in predicting the anti-HIV activity yielded a good correlation for the training set (R2 = 0.913, R = 0.897, Q2 = 0.849, MSE = 0.190, F -ratio = 59.97, PRESS = 18.05, SSE = 0.926, and p value = 0.00). Leave-one-out cross-validation also reaffirmed the predictions (R2 = 0.850, R = 0.824, Q2 = 0.849, MSE = 0.328, and PRESS = 18.05). The predictive ability of the training set was also cross-validated by a test set (R2 = 0.812, R = 0.799, Q2 = 0.765, MSE = 0.347, F -ratio = 64.69, PRESS = 7.37, SSE = 0.975, and p value = 0.00), which ascertained a satisfactory quality of fit. The results reflect the substitution pattern and suggest that the presence of a bulky and electropositive group in the five-member ring and electron withdrawing groups in the seven-member ring will have a positive impact on the antiviral activity of the derivatives. Bulky groups in the six-member ring do not show an activity-enhancing impact. Outlier analysis too reconfirms our findings. The E-state descriptors indicate their importance in quantifying the electronic characteristics of a molecule and thus can be used in chemical interpretation of electronic and steric factors affecting the biological activity of compounds. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2008 [source] Development of a general quantum-chemical descriptor for steric effects: Density functional theory based QSAR study of herbicidal sulfonylurea analoguesJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 13 2006Zhen Xi Abstract Quantitative structure-activity relationship (QSAR) analysis has become one of the most effective approaches for optimizing lead compounds and designing new drugs. Although large number of quantum-chemical descriptors were defined and applied successfully, it is still a big challenge to develop a general quantum-chemical descriptor describing the bulk effects more directly and effectively. In this article, we defined a general quantum-chemical descriptor by characterizing the volume of electron cloud for specific substituent using the method of density functional theory. The application of our defined steric descriptors in the QSAR analysis of sulfonylurea analogues resulted in four QSAR models with high quality (the best model: q2 = 0.881, r2 = 0.901, n = 35, s = 0.401, F = 68.44), which indicated that this descriptor may provide an effective way for solving the problem how to directly describe steric effect in quantum chemistry-based QSAR studies. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 1571,1576, 2006 [source] Antibiotic activity of pentadecapeptides modelled from amino acid descriptorsJOURNAL OF PEPTIDE SCIENCE, Issue 2 2001Tore Lejon Abstract Pentadecapeptides based on modified murine lactoferricin (LFM) sequences show varying degrees of antibacterial activity against Escherichia coli and Staphylococcus aureus. By means of projections to latent structures (PLS), a good correlation is obtained if the biological activity is modelled as a function of variables describing peptide properties, e.g. ,-helicity, hydrophobicity/hydrophilicity and charge. Using variables derived from a principal component analysis (PCA) of all naturally occurring amino acids, it is possible to describe the amino acid content of the peptides using three variables per amino acid position. The resulting descriptor matrix is then used to develop quantitative structure,activity relationships (QSAR). It is shown that the theoretically derived descriptors model the activity of the peptides better than the earlier model, and that properties of the peptides other than antibacterial activity can be predicted. Copyright © 2001 European Peptide Society and John Wiley & Sons, Ltd. [source] Predicting P-glycoprotein substrates by a quantitative structure,activity relationship modelJOURNAL OF PHARMACEUTICAL SCIENCES, Issue 4 2004Vijay K. Gombar Abstract A quantitative structure,activity relationship (QSAR) model has been developed to predict whether a given compound is a P-glycoprotein (Pgp) substrate or not. The training set consisted of 95 compounds classified as substrates or non-substrates based on the results from in vitro monolayer efflux assays. The two-group linear discriminant model uses 27 statistically significant, information-rich structure quantifiers to compute the probability of a given structure to be a Pgp substrate. Analysis of the descriptors revealed that the ability to partition into membranes, molecular bulk, and the counts and electrotopological values of certain isolated and bonded hydrides are important structural attributes of substrates. The model fits the data with sensitivity of 100% and specificity of 90.6% in the jackknifed cross-validation test. A prediction accuracy of 86.2% was obtained on a test set of 58 compounds. Examination of the eight "mispredicted" compounds revealed two distinct categories. Five mispredictions were explained by experimental limitations of the efflux assay; these compounds had high permeability and/or were inhibitors of calcein-AM transport. Three mispredictions were due to limitations of the chemical space covered by the current model. The Pgp QSAR model provides an in silico screen to aid in compound selection and in vitro efflux assay prioritization. © 2004 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 93: 957,968, 2004 [source] QSAR analysis of interstudy variable skin permeability based on the "latent membrane permeability" conceptJOURNAL OF PHARMACEUTICAL SCIENCES, Issue 10 2003Shin-Ichi Fujiwara Abstract A number of QSAR models for skin permeability have been proposed, but these models lack consistency due to interspecies and interlaboratory differences. This study was initiated to extract an essential QSAR from the multiplicity of data sets of skin permeability by using a novel statistical approach. Ten data sets were collected from the literature, which include a total of 111 permeability coefficients in human, hairless mouse, or hairless rat skin for 94 structurally diverse compounds. Following a Potts and Guy's approach, the octanol/water partition coefficient and molecular weight were chosen as molecular descriptors. All of the data sets were analyzed simultaneously, assuming that all of the sets share a latent, common factor as far as the structure/permeability relationship is concerned. Despite the fact that the degree-of-freedom for the present analysis was limited compared with that for individual regression analyses, the determination coefficients (R2) were high enough for all the 10 data sets, with an average R2 of 0.815 (average R2,=,0.825 for individual analyses). Thus, skin permeability of compounds can be well explained from the log P and M.W., where the ratio of the contribution to skin permeability was approximately 1:1. © 2003 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 92:1939,1946, 2003 [source] Single-mode compound retrieval for QSAR, QSPR data sets, and batch mode exact structure searchingJOURNAL OF PHARMACEUTICAL SCIENCES, Issue 12 2002Christopher A. Lipinski No abstract is available for this article. [source] The Use of Atomic Charges and Orbital Energies as Hydrogen-bonding-donor Parameters for QSAR Studies: Comparison of MNDO, AM1 and PM3 MethodsJOURNAL OF PHARMACY AND PHARMACOLOGY: AN INTERNATI ONAL JOURNAL OF PHARMACEUTICAL SCIENCE, Issue 6 2000TARAVAT GHAFOURIAN Hydrogen-bonding, important in drug-receptor interactions, also determines the solubility and partitioning of drugs between phases. It is, therefore, important to incorporate the effects of hydrogen-bonding in studies of quantitative structure-activity relationships (QSAR). In this study the atomic charge on the most positively charged hydrogen atom in a molecule and the energy of the lowest unoccupied molecular orbital (LUMO) have been used as a measure of hydrogen-bond-donor capacity. For several hydrogen-bonding acids the Mulliken atomic charges and the energy of the LUMO produced by use of three semi-empirical methods, AM1, PM3 and MNDO, and MNDO electrostatic-potential-derived atomic charges, have been compared in correlations with solvatochromic hydrogen-bonding acidity (,,2H). Atomic charges and LUMO energies, particularly those calculated by use of the AM1 and MNDO methods, were found to correlate well with ,,2H. They were also found to be good models of hydrogen-bonding in QSAR correlations. [source] Liposome/water lipophilicity: Methods, information content, and pharmaceutical applicationsMEDICINAL RESEARCH REVIEWS, Issue 3 2004Georgette Plemper van Balen Abstract This review discusses liposome/water lipophilicity in terms of the structure of liposomes, experimental methods, and information content. In a first part, the structural properties of the hydrophobic core and polar surface of liposomes are examined in the light of potential interactions with solute molecules. Particular emphasis is placed on the physicochemical properties of polar headgroups of lipids in liposomes. A second part is dedicated to three useful methods to study liposome/water partitioning, namely potentiometry, equilibrium dialysis, and 1H-NMR relaxation rates. In each case, the principle and limitations of the method are discussed. The next part presents the structural information encoded in liposome/water lipophilicity, in other words the solutes' structural and physicochemical properties that determine their behavior and hence their partitioning in such systems. This presentation is based on a comparison between isotropic (i.e., solvent/water) and anisotropic (e.g., liposome/water) systems. An important factor to be considered is whether the anisotropic lipid phase is ionized or not. Three examples taken from the authors' laboratories are discussed to illustrate the factors or combinations thereof that govern liposome/water lipophilicity, namely (a) hydrophobic interactions alone, (b) hydrophobic and polar interactions, and (c) conformational effects plus hydrophobic and ionic interactions. The next part presents two studies taken from the field of QSAR to exemplify the use of liposome/water lipophilicity in structure,disposition and structure,activity relationships. In the conclusion, we summarize the interests and limitations of this technology and point to promising developments. © 2004 Wiley Periodicals, Inc. Med Res Rev, 24, No. 3, 299,324, 2004 [source] Current trends in QSAR on NO donors and inhibitors of nitric oxide synthase (NOS),,MEDICINAL RESEARCH REVIEWS, Issue 4 2002Christos A. Kontogiorgis Abstract This article evaluates the quantitative structure-activity relationships (QSAR) of nitric oxide (NO) radical donors and nitric oxide synthases (NOS) inhibitors, using the C-QSAR program of Biobyte. Furoxans, triazines, amidoximes, tetrazoles, imidazoles and N,,2-nitroarylamino acid analogues were included in this survey. In nine out of seventeen cases, the clog P plays a significant part in the QSAR of the NO radical donors and of the NOS inhibition. Many of the compounds must be interacting with a hydrophobic space in a non-specific way. In some cases molecular refractivity CMR/MR as well as sterimol parameters (B1 and L) are important. Electronic effects, with the exception of the Hammett's constant , and the Swain,Lupton parameter F, are not found to govern the biological activity. Stereochemical and electronic features are also found to be important. Indicator variables were used after the best model was found to account for the usual structural features. © 2002 Wiley Periodicals, Inc. Med Res Rev, 22, No. 4, 385,418, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/med.10012 [source] Are Mechanistic and Statistical QSAR Approaches Really Different?MOLECULAR INFORMATICS, Issue 6-7 2010MLR Studies on 158 Cycloalkyl-Pyranones Abstract Two parallel approaches for quantitative structure-activity relationships (QSAR) are predominant in literature, one guided by mechanistic methods (including read-across) and another by the use of statistical methods. To bridge the gap between these two approaches and to verify their main differences, a comparative study of mechanistically relevant and statistically relevant QSAR models, developed on a case study of 158 cycloalkyl-pyranones, biologically active on inhibition (Ki) of HIV protease, was performed. Firstly, Multiple Linear Regression (MLR) based models were developed starting from a limited amount of molecular descriptors which were widely proven to have mechanistic interpretation. Then robust and predictive MLR models were developed on the same set using two different statistical approaches unbiased of input descriptors. Development of models based on Statistical I method was guided by stepwise addition of descriptors while Genetic Algorithm based selection of descriptors was used for the Statistical II. Internal validation, the standard error of the estimate, and Fisher's significance test were performed for both the statistical models. In addition, external validation was performed for Statistical II model, and Applicability Domain was verified as normally practiced in this approach. The relationships between the activity and the important descriptors selected in all the models were analyzed and compared. It is concluded that, despite the different type and number of input descriptors, and the applied descriptor selection tools or the algorithms used for developing the final model, the mechanistical and statistical approach are comparable to each other in terms of quality and also for mechanistic interpretability of modelling descriptors. Agreement can be observed between these two approaches and the better result could be a consensus prediction from both the models. [source] Probing Small-Molecule Binding to the Liver-X Receptor: A Mixed-Model QSAR StudyMOLECULAR INFORMATICS, Issue 1-2 2010Morena Spreafico Abstract The LXR model has been added in the VirtualToxLab, a fully automated technology that allows for the identification of the endocrine-disrupting potential of drugs, chemicals and natural products. This protocol has then been applied to screen a series of 161 natural compounds probing their binding to the LXR. The results of the simulation were compared with experimental data (where available) and suggest that the LXR model can be applied to predict the binding affinity of existing or hypothetical compounds for screening purposes. The binding of 52 ligands towards the liver X receptors (LXRs) was identified trough docking to the three-dimensional protein structure and quantified by multidimensional QSAR (mQSAR), an approach referred to as ,mixed-model QSAR'. The model was validated by the prediction of 17 external compounds (oxysterols) present neither in the training nor in the test set. The robustness of the model was verified by consensus scoring using a conceptually different methodology, and chance correlation was ruled out by a series of scramble tests. [source] Exhaustive Structure Generation for Inverse-QSPR/QSARMOLECULAR INFORMATICS, Issue 1-2 2010Tomoyuki Miyao Abstract Chemical structure generation based on quantitative structure property relationship (QSPR) or quantitative structure activity relationship (QSAR) models is one of the central themes in the field of computer-aided molecular design. The objective of structure generation is to find promising molecules, which according to statistical models, are considered to have desired properties. In this paper, a new method is proposed for the exhaustive generation of chemical structures based on inverse-QSPR/QSAR. In this method, QSPR/QSAR models are constructed by multiple linear regression method, and then the conditional distribution of explanatory variables given the desired properties is estimated by inverse analysis of the models using the framework of a linear Gaussian model. Finally, chemical structures are exhaustively generated by a sophisticated algorithm that is based on a canonical construction path method. The usefulness of the proposed method is demonstrated using a dataset of the boiling points of acyclic hydrocarbons containing up to 12 carbon atoms. The QSPR model was constructed with 600 hydrocarbons and their boiling points. Using the proposed method, chemical structures which had boiling points of 100, 150, or 200,°C were exhaustively generated. [source] Editorial: Good-bye QSAR & Combinatorial ScienceMOLECULAR INFORMATICS, Issue 11-12 2009Article first published online: 22 DEC 200 No abstract is available for this article. [source] | ||||||||||||||||