bluuebunny/crossref_metadata_2025_split huggingface dataset:
Molecular Structure Description by Segmentation of Atomic Score Spaces (SASS)
Authors: Alsberg Bjørn , Esbensen Kim 
Published: October 2006

AbstractMolecular structure is represented as segmented volume elements of 3‐D principal component score spaces of molecules. Clonidine conformations were analysed by principal component analysis (PCA) and their corresponding conformational energies were regressed on segmented PCA score pattern for each conformation. Conformational energy is interpreted as the “activity” of these molecular conformations. The resulting model is able to predict the energy also for other conformations not included in the calibration. We suggest that segmented atomic score spaces (SASS) can be a valuable tool in quantitative structure‐activity relationship (QSAR) studies.
DARC Computer Aided Design in Anticholinergic Research
Authors: Mercier Christiane , Trouiller Gérard , Dubois Jacques‐Emile 
Published: October 2006

AbstractA dynamic method for the setting up and computer‐aided handling of an “ANTICHOLINERGIC” data bank has been designed in order to rationalize the planning of pharmacological and chemical experimentation with a view to optimizing series known to be active. The DARC Computer Aided Design method has been employed to specify the pharmacological profile of certain tests, to reduce the cost of experimentation and to discover new structures either with high central anticholinergic activity or with greater peripheral than central anticholinergic activity. The structural and pharmacological data are reported for more than 60 3‐thienyl aminoalcohol glycolates.
MD simulations in Pseudo‐Particle Fluids: Applications to active‐site Protein Complexes
Authors: Kern Petra , Rognan Didier , Folkers Gerd 
Published: October 2006

AbstractTruncated protein‐ligand active‐site complexes were studied by molecular dynamics simulations. Pseudo‐particles were used for the representation of the outer protein parts and the surrounding solvent. This pseudo‐particle approach was applied to the MD simulation of adenylate kinase wild type or mutant enzymes in complex with different ligands. For further validation of the method, the human leucocyte antigen HLA‐B27 was simulated in complex with three different nonapeptides. All molecular dynamics simulations were additionally compared with biochemical data. Despite the crude approximation of the outer protein parts and the solvent environment, differences in binding properties of the ligands could be observed and correlated to experiments.
Comparative Structure‐Affinity Relations by MTD for Binding of Cycloadenosine Monophosphate Derivatives to Protein Kinase Receptors
Authors: Mureşan Sorel , Jastorff Bernd , Kurunczi Ludovic , Simon Zeno 
Published: October 2006

AbstractA “testkit” series of 27 derivatives of cAMP with various substituents in position 1, 2, 6 and 8 and within the purine cycle, thiophosphoric acid derivatives (with equatorial or axial S‐atom) also included, were used to map four receptor sites of the R‐subunit of cAMP dependent phosphokinases I and II, namely labile and stable receptors AI, BI, AII and BII. A QSAR by the MTD method was applied for the four series of activities, together with the relative nitrogen base hydrophobicity (lgKw), electric charge of the position 6‐substituent (qN) and an indicator variable (δ = 1 for equatorial thiophosphoric derivatives). Correlation coefficients between r = 0.836 and 0.948 were obtained and the reliability of QSAR results was tested by a cross validation‐like procedure. Characteristic steric features (concerning the effects of substituents in different nitrogen‐base positions) were separately obtained for each receptor. For AI and BI receptor there is a negatively charged receptor group interacting with substituents in position 6 of cAMP derivatives. BI and BII receptors are of a marked hydrophobic character. Thiophosphoric acid derivatives, especially those with equatorial S‐atom, have a decreased affinity for all four receptors. The results are compared with other QSAR studies of our group, concerning different series of cAMP derivatives.
SAR Models for Metabolic Activation: Stability of Organic Cation Intermediates
Authors: Mekenyan Ovanes G. , Veith Gilman D. , Bradbury Steven P. , Zaharieva Nora 
Published: October 2006

AbstractSemi‐empirical methods for estimating the stability of organic cations through hydride abstraction or protonation were evaluated. To simulate hydride abstraction, stability was defined as the difference between the calculated enthalpies of the cations and the parent structures. Cations formed by protonation were compared to proton affinities by computing enthalpies of the conjugate acids and the corresponding bases. Semi‐empirical quantum chemical methods, including AM1, PM3 and MNDO hamiltonians, were used to evaluate their capability to model reaction enthalpies and estimate cation stability. All three methods were acceptable in estimating the stability of cations obtained by hydride abstraction. The correlation coefficients between computed stabilities from the three methods and measured stabilities ranged from 0.83 for MNDO to 0.92 for AMl. The AMl method was the only acceptable hamiltonian for estimating proton affinity for the studied chemicals. The correlation coefficient between measured and computed values was 0.89. The models for stability will be incorporated into larger QSAR activation models for estimating the probability of mutagenicity and tumor formation in risk assessments.
The μ Opioid Receptor Binding Pharmacophore Conformation of Ornithine Containing Cyclic β‐Casomorphin Analogues and Related Peptides
Authors: Brandt Wolfgang , Mrestani‐Klaus Carmen , Schinke Heiko , Neubert Klaus , Barth Alfred , Schmidt Ralf , Schiller Peter W. , Höltje Hans‐Dieter 
Published: October 2006

AbstractLow energy conformers of both active and nearly inactive cyclic β‐casomorphin analogues, such as Tyr‐c[‐Orn‐X‐Pro‐Gly‐], Tyr‐c[‐Orn‐X‐D‐Pro‐Gly‐], Tyr‐c[‐D‐Orn‐X‐Pro‐Gly‐] and Tyr‐c[‐D‐Orn‐X‐D‐Pro‐Gly‐] (X=Phe or 2‐Nal) have been determined in a systematic conformational search using the TRIPOS force field. The obtained conformations were compared with a model based on structure(conformation)‐activity relationships for μ‐selective non‐peptide opiates and taking into consideration both conformational features and molecular electrostatic potentials. On the basis of these findings we were able to propose plausible pharmacophoric structures for the cyclic β‐casomorphins. These conformations were in close agreement with those determined in solution by 1H NMR spectroscopy. The pharmacophoric moieties contained in these structures are in a spatial disposition which allows them to interact with several proposed subsites of the μ‐receptor. These are an anionic site (area A) engaged in an electrostatic interaction with the protonated N‐terminus of the peptides, two hydrophobic sites (areas C and D) that are both occupied by aromatic rings (Tyr and Phe in the case of the peptides) and two hydrophilic receptor binding sites (areas B and F). In all peptides the N‐terminal tyrosine residue assumes a very similar conformation, permitting its interaction with receptor areas A, B and C. Furthermore, the intramolecular distance between the centroids of the Phe3 aromatic ring (interacting with area D) and the Tyr1 ring is about 10 to 11 Å. According to the model, a further receptor subsite designated as F interacts with the carbonyl of the D‐Orn2 residue and in the case of the morphiceptin analogues with the carbonyl group of either cisPro2 or Pro4. The array of proposed receptor subsites also accommodates various opiate alkaloids, several enkephalin analogues and the message domain of dermorphins and deltorphins. Furthermore, the model provides an explanation for the lack of activity observed with cyclic β‐casomorphin analogues containing L‐ornithine in the 2‐position of the peptide sequence. The similarity between the pharmacophore conformations of the opioid peptides and the non‐peptide opiates suggests that their mode of binding to the receptor may be similar.
QSAR Study of the Toxicity of Nitrobenzenes to Tetrahymena pyriformis
Authors: Dearden John C. , Cronin Mark T. D. , Schultz T. Wayne , Lin David T. 
Published: October 2006

AbstractA QSAR analysis has been carried out of the toxicity of 47 mono‐substituted nitrobenzenes to the aquatic ciliate Tetrahymena pyriformis. The single most important parameter was the logarithm of the apparent partition coefficient (logD) at pH 7.35. 2‐ and 3‐derivatives yielded an excellent correlation of toxicity with log D, Swain‐Lupton field parameter and modulus of change of charge on the nitro group oxygen upon substitution. This is consistent with their behaving as pro‐electrophiles. 4‐derivatives, however, clearly had a different mode of action, with the best correlation being found with log D, molar refractivity and the Shannon index.
Structure‐Affinity Binding Relationships by Principal‐Component‐Regression Analysis of Anthraquinone Dyes
Authors: Timofei Simona , Kurunczi Ludovic , Schmidt Walter , Fabian Walter M. F. , Simon Zeno 
Published: October 2006

AbstractDye‐fibre binding was modelled by principal‐component‐regression analysis (PCRA) method for two series of 46, respectively 49 anthraquinone vat dyes. Robust equations with predictive modelling power were proposed for the correlations with structural parameters (r2 = 0.92 and s = 0.48, respectively r2 = 0.94 and s = 0.47). They indicated the presence of steric, electronic and hydrophobic effects in the dyeing process. PCRA allows to avoid the multicollinearity of the physicochemical descriptors, yielding better results as compared to those obtained from ordinary leastsquares analysis.
Parameter Based Methods for Compound Selection from Chemical Databases
Authors: Hudson Brian D. , Hyde Richard M. , Rahr Elizabeth , Wood John , Osman Julian 
Published: October 2006

AbstractTwo algorithms for the selection of subsets of compounds from chemical databases are presented and discussed. The first is designed to select representative subsets whilst the second is intended to select compounds which cover the available property space. Both make use of calculated physicochemical parameters in contrast to more common methods based on molecular fingerprints. This is an approach to molecular similarity which has proved successful in the past. The methods are illustrated with examples and discussed.
Exhaustive Conformational Searches for Superimposition of Acaricidal Compounds
Authors: Akagi Toshio , Takahashi Yoshimasa , Sasaki Shin‐Ichi 
Published: October 2006

AbstractThree‐dimensional common structural features among three acaricidal compounds which inhibit respiratory complex I were investigated, assuming that they have a similar binding mode. The techniques of COMPASS (COMmon geometrical Pattern Search System) and “molecular field fitting” were employed and exhaustive conformational space searches were performed without any presumption. The results suggested that the “active conformations” of these compounds are non‐planar. Within each type a lone‐pair containing functional group on the heterocyclic ring as well as a hydrophobic region with a t‐butyl group overlapped well, despite the difference in size and chemical nature of the connecting bridge. Structure‐activity relationships of the compounds are explained successfully by the energy of the “active conformation”. The present study demonstrates the usefulness of the exhaustive conformational search to derive simultaneously the “active conformation” and the mode of superimposition.
Evolutionary Neural Networks in Quantitative Structure‐Activity Relationships of Dihydrofolate Reductase Inhibitors
Authors: Kyngäs Jari , Valjakka Jarkko 
Published: October 2006

AbstractThe evolutionary neural network (ENN) is a new system for modelling multifactor data. The strength of ENN's are that they can extract insignificant predictors, choose the size of the hidden layer and fine tune the parameters needed in training the network. We have used an ENN to predict the biological activities of Dihydrofolate Reductase Inhibitors. As a result, we found that evolutionary neural networks give more accurate predictions than statistical methods and feedforward neural networks.
Recent Success Stories Leading to Commercializable Bioactive Compounds with the Aid of Traditional QSAR Procedures
Authors: Fujita Toshio 
Published: October 2006

AbstractNew successful applications of the Hansch‐type traditional QSAR to key steps in designing novel bioactive compounds are reviewed. Studies for a benzhydrylbenzylpiperazine antimigraine agent (lomerizine), azole‐type agricultural fungicides (metconazole and ipconazole), and a biphenylyloxobutanoic acid antiinflammatory agent (flobufen) were taken as the examples. Structural optimizations were nicely made by using the QSAR information sometimes along with findings obtained from 3D molecular modelling studies and/or hypotheses proposed for metabolic fates. The two azole fungicides were launched in 1994. The antimigraine and antiinflammatory agents are expected to be commercialized soon.
Chemometric Study and Validation Strategies in the Structure‐Activity Relationships of New Cardiotonic Agents
Authors: Boggia Raffaella , Forina Michele , Fossa Paola , Mosti Luisa 
Published: October 2006

AbstractForty‐two molecules, thirty‐eight milrinone analogues, two lead compounds, amrinone and milrinone, and two commercial products have been studied using chemometrical techniques applied to thirty theoretical descriptors and two biological activities (each one at three different concentrations).PLS Regression was applied both in the usual form PLS‐1, with one response variable, and as PLS‐2, with the contemporary study of more activities in the block of response variables.Regression models (both with the original activities and with log and arctan transforms) were refined by progressive elimination of conformers and of non‐relevant predictors, one‐at‐a‐time, on the basis of the relevance in the regression equation. Different sorts of model refinement gave origin to four chemometrical strategies.Special attention was deserved to the development of validation procedures for the regression analysis, in order to evaluate the true predictive ability of the refined models. The predictive optimization was based on cross‐validation. Complete validation using three sets (training, optimization, external) was applied in one of the strategies. Both optimization and validation were performed in different conditions in order to eliminate the possibility of chance correlation.The severe validation procedures applied prevent underestimate of prediction error, frequently encountered when partial validation procedures are applied.Only one biological activity at the highest concentration can be predicted from the theoretical descriptors with a reasonable prediction error, measured by cross‐validated explained variance. Only volume descriptors have a sure importance in the final regression model.
Nonlinear CoMFA using QPLS as a Novel 3D‐QSAR Approach
Authors: Hasegawa Kiyoshi , Kimura Toshiro , Funatsu Kimito 
Published: October 2006

AbstractComparative molecular field analysis (CoMFA) using partial least squares (PLS) is a popular method in 3D‐QSAR studies. Although CoMFA has been of general use, it is suboptimal or even useless when nonlinear relationships are observed between field variables and biological activity. Quadratic PLS (QPLS) developed by Wold et al. is an extension of PLS to deal with nonlinear chemical data. In this paper, it is demonstrated that QPLS can be applicable to the 3D field variables in CoMFA. The structure‐activity data of dihydrofolate reductase (DHFR) inhibitors were used as a test example. The resulting QPLS model gave high predictivity with only one component to explain the nonlinear relationships between the electrostatic field variables and inhibitory activity. The loading values of the QPLS model were plotted in 3D space and chemically reasonable contour maps were obtained in accordance with the previous multiple and non‐linear regression (MLR) model.
Calculation of Octanol/Water Partition Coefficients (logP) using Artificial Neural Networks and Connection Matrices
Authors: Schaper Klaus‐Jürgen , Samitier Maria Luisa Rosado 
Published: October 2006

AbstractArtificial neural networks of the backpropagation type with three layers (input, hidden, output) are able to recognize structural molecular features determining the lipophilicity of unionized organic molecules and to directly use the chemical structure for the calculation of the partition coefficient [logP(octanol/water)]. Using a network with three neurons in the hidden layer and binary variables to indicate the presence or absence of atom types and bond types a standard deviation of s = 0.248 was obtained in the correlation between logPobs. and logPcalc. for a small training set of 268 simple organic molecules containing C, H, N, O, S, halogens. For a test set of 50 similar molecules the predicted logP values were satisfactory (s = 0.659).
Development of Methods to Ascertain the Predictivity and Consistency of SAR Models: Application to the U.S. National Toxicology Program Rodent Carcinogenicity Bioassays
Authors: Zhang Ying Ping , Sussman Nancy , Rosenkranz Herbert S. , Klopman Gilles 
Published: October 2006

AbstractModels investigating relationships between chemical structures and biological activities are receiving increased recognition for the identification of chemicals with the potential for inducing adverse health effects. The relationships can be either qualitative (noted as SAR) or quantitative (noted as QSAR). The objective of the present study was to define an effective process for evaluating such models. The predictivity of SAR/QSAR models derived from the U.S. National Toxicology Program Rodent Carcinogenicity Bioassay endeavor by CASE/MultiCASE was evaluated by several different approaches: leave‐one‐out tests, 10‐fold cross‐validations and by the use of an independent test set. The goodness‐of‐fit for the data used in the model building, the predictivity for the chemicals not contained in the model, and the consistency of the predictions for a group of chemicals by different SAR/QSAR sub‐models were examined systematically. Individual prediction indices generated by CASE/MultiCASE, arbitrary combinations thereof, as well as weighted combinations using Bayes' theorem, were utilized to derive predictions of Carcinogenicity. Combinations derived using Bayes' theorem provided the most predictive model. The closeness between sub‐models based on the leave‐one‐out procedure and the full model (all chemicals used for model building) makes it the most reliable process for the estimation of a model's predictivity. However, the similarity between the predictions of the leave‐one‐out models and the 10‐fold cross‐validation models indicates that the latter process provides an acceptable approach.
Analysis of a Large Structure‐Activity Data Set Using Recursive Partitioning
Authors: Hawkins Douglas M. , Young S. Stanley , Rusinko Andrew 
Published: October 2006

AbstractConventional parametric methods such as linear regression have not been entirely successful in analyzing structure‐activity data sets. This is because the underlying relationships may involve nonlinearities, thresholds and interactions, all of which considerably impede linear additive modelling approaches. Recursive partitioning, RP, is able to accommodate all these modelling difficulties seamlessly and therefore invites investigation as a general approach for study of structure activity relationships.In this paper we apply a recursive partitioning procedure, FIRM, to a large monoamine oxidase structure‐activity data set. The methodology is successful in uncovering nonlinearities in the response. Coupling RP with the use of correspondence analysis provides further insights into the distinction between compounds that are inactive, moderately active and active.
Studies on Propafenone‐type Modulators of Multidrug Resistance III: Variations on the Nitrogen
Authors: Chiba Peter , Hitzler Manuela , Richter Elisabeth , Huber Mario , Tmej Claudia , Giovagnoni Emiliano , Ecker Gerhard 
Published: October 2006

AbstractA series of piperazine‐ and piperidine‐analogous propafenone derivatives was synthesized and tested for their ability to modulate PGP‐mediated multidrug resistance. A good correlation between lipophilicity and activity was obtained for a set of 13 compounds. Nevertheless, 4‐hydroxy‐4‐phenylpiperidines 4a–d generally showed higher activity than predicted. A QSAR equation for the complete set of compounds was obtained when using both lipophilicity and an indicator variable for compounds 4a–d (I=1; else I = 0) or H‐bond donor strength of the 4‐hydroxy group (rcv2 = 0.90; n=17). Synthesis of aniline derivatives demonstrated that the propanolamine nitrogen interacts in protonated form. Studies on a series of diphenylalkylamines indicate, that steric factors also seem to play a role for the interaction of the nitrogen with PGP.
Comparative Molecular Field Analysis (CoMFA) on [6] + [6] Fused Pyrazines with Nematocide Properties
Authors: Martínez Ana , Ochoa Carmen , Rodríguez Juan , Rodríguez Mercedes , Castro Ana , González Mercedes , Martínez María Mercedes 
Published: October 2006

AbstractQuantitative structure‐activity relationship (QSAR) studies, using the Comparative Molecular Field Analysis (CoMFA), on a series of 6,7‐diarylpteridine derivatives showing nematocide properties have been carried out. The CoMFA model generated in the study has been used to estimate the namatocide activity (MIC50) of seven 6,7‐diarylpteridines related to those previously studied. The model is highly predictive for all pteridine derivatives in the test set. Moreover, this model also predicts satisfactorily the nematocide activity of other [6] + [6] fused pyrazines (quinoxalines and pyridopyrazines) which were not represented in the training set. The analysis revealed the importance of steric factors (64.8%) and then the electrostatic ones (35.8%). Most pteridines under study were previously synthesized and tested as nematocide agents. Now, the synthesis and biological evaluation of 6,7‐di‐(2′‐thienyl)‐4(3H)‐thioxo‐pteridine 36 are reported.
Enzymes In Vitro as indicators for pesticides: An examination
Authors: Wiegand‐Rosinus Maria , Wfm Ursula Obst , Haberer Klaus , Wild Aloysius 
Published: October 2006

AbstractPesticides—a serious problem especially for drinking water quality—frequently are potent inhibitors of enzymes in their target organisms. As the established chemical analyses of pesticides are time‐consuming, complicated, and expensive, so‐called screening methods are urgently needed. For this purpose the sensitivity of 13 different enzymes was tested in vitro by inhibiting their kinetic rates and/or substrate conversions by 16 pesticides (herbicides and fungicides). Because of the stabilization and resulting low sensitivity of enzymes commercially available—especially in test kits—it was necessary to vary the test conditions and to develop special methods for detecting enzymatic inhibitions. It was shown that the sensitivity to pesticides of the different enzymes varies in a wide range, partly in irrelevant high concentrations. Four enzymes seem to be suitable for inhibition tests, but need to be checked before practical application. The enzyme aldehyde dehydrogenase (AlDh), isolated from yeasts, was very sensitive to ethendithiocarbamates. Therefore an inhibition test could be developed to detect this class of pesticides in a range of some μg/L. The AlDh inhibition test is the first part of an enzymatic screening system for pesticide pollution in water. © 1992 John Wiley & sons, Inc.