We have used the recently developed tensor decomposition 3D-QSAR method to predict the conformation and alignment of cocaine derivatives bound to the monoamine transporters, NET, DAT and SERT. The analysis revealed that the ligands bind to the receptors in a conformation with the 3β-aryl group orthogonal or approximately orthogonal to the tropane ring. Semi rigid ligands have been prepared with a 3β-aryl group having strong conformational preferences for this orientation. Two compounds had affinities for the DAT and SERT in the low nanomolar range. The solution conformation of one compound has been determined and the results support the results of the 3D-QSAR analysis. Comparisons of affinities and selectivities of these ligands are discussed.

A class of opioid receptor active derivatives of naltrexone has been synthesized using a common enaminone intermediate. The intermediate used in the synthesis is prepared from N,N-dimethylformamide dimethyl acetal and naltrexone and can be isolated and characterized. The derivatives have heterocyclic groups fused to the 6,7-positions of the morphinan system and all were synthesized in high yield. All compounds were very high affinity, nonselective antagonists for the opioid receptors.

Tandem mass spectrometry has been used to differentiate positional isomers of some monoalkylated naphthalenes. The basis of the distinction is the cluster of peaks from m/z 150 to 155. Principal components analysis can extract isomer-specific information that is not obvious from simple visual inspection of the spectra. The analysis led to the observation of similar trends in single-stage electron impact mass spectra.

Molecular recognition is the basis of rational drug design, and for this reason it has been extensively studied. However, the process by which a ligand recognizes and binds to its receptor is complex and not well understood. For the case in which the geometries (conformation and alignment) of the ligand and receptor are known from X-ray crystal structure data, the problem is simplified. The receptor-bound conformation and alignment of the ligand is assumed, and those of additional ligands are inferred. For the general case in which the geometries of the ligand(s) and receptor are unknown, no general treatment or solution is available and receptor-ligand geometries must be obtained indirectly from structure-activity studies or synthesis and evaluation of rigid analogs. A general treatment for solving for the receptor-bound geometry of a series of ligands is presented here. Using molecular shape analysis, for ligand description, tensor analysis of N-way arrays by partial least-squares (PLS) regression, and 3-way factor analysis, the receptor-bound geometries of trimethoprim and a series of trimethoprim-like dihydrofolate reductase inhibitors are correctly predicted.

The distribution and subsequent concentration in specific compartments of the environment of organic pollutants is determined by the pollutants' physicochemical properties. Since the atmospheric, aqueous and biological compartments are of major concern from the public health standpoint, the vapor/water and water/nonpolar compartment properties are of interest. Currently, such properties are estimated from structure using additive structure—property models. Such models are of limited accuracy when properties for new and interesting compounds are needed. Computer simulation methods based on more fundamental principles provide better estimates and mechanistic information. Some of these methods are discussed here.

Chapter 4 Multivariate Data Analysis of Chemical and Biological Data Rainer Franke, Rainer FrankeSearch for more papers by this authorAndreas Gruska, Andreas GruskaSearch for more papers by this authorJames Devillers, James DevillersSearch for more papers by this authorDaniel Chessel, Daniel ChesselSearch for more papers by this authorWilliam J. Dunn III, William J. Dunn IIISearch for more papers by this authorSvante Wold, Svante WoldSearch for more papers by this authorPaul J. Lewi, Paul J. LewiSearch for more papers by this authorMartyn Glenn Ford, Martyn Glenn FordSearch for more papers by this authorDavid William Salt, David William SaltSearch for more papers by this authorHan van de Waterbeemd, Han van de WaterbeemdSearch for more papers by this authorJames W. McFarland, James W. McFarlandSearch for more papers by this authorDaniel J. Gans, Daniel J. GansSearch for more papers by this author Rainer Franke, Rainer FrankeSearch for more papers by this authorAndreas Gruska, Andreas GruskaSearch for more papers by this authorJames Devillers, James DevillersSearch for more papers by this authorDaniel Chessel, Daniel ChesselSearch for more papers by this authorWilliam J. Dunn III, William J. Dunn IIISearch for more papers by this authorSvante Wold, Svante WoldSearch for more papers by this authorPaul J. Lewi, Paul J. LewiSearch for more papers by this authorMartyn Glenn Ford, Martyn Glenn FordSearch for more papers by this authorDavid William Salt, David William SaltSearch for more papers by this authorHan van de Waterbeemd, Han van de WaterbeemdSearch for more papers by this authorJames W. McFarland, James W. McFarlandSearch for more papers by this authorDaniel J. Gans, Daniel J. GansSearch for more papers by this author Book Editor(s):Dr. Han van de Waterbeemd, Dr. Han van de Waterbeemd F. Hoffmann - La Roche Ltd., Pharma Research New Technologies, CH-4002 Basel, SwitzerlandSearch for more papers by this author First published: 09 February 1995 https://doi.org/10.1002/9783527615452.ch4Citations: 38Book Series:Methods and Principles in Medicinal Chemistry AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat Summary This chapter contains sections titled: Principal Component and Factor Analysis Graphical Analysis as an Aid in Medicinal Chemistry SIMCA Pattern Recognition and Classification PLS for Multivariate Linear Modeling Spectral Mapping of Drug Test Specificities Display of Multivariate Data Using Non-Linear Mapping The Use of Canonical Correlation Analysis Discriminant Analysis for Activity Prediction Cluster Significance Analysis Citing Literature Chemometric Methods in Molecular Design RelatedInformation

The recent development of tandem mass spectrometry (MS), with much higher information content in the mass spectrum, makes it possible to obtain higher identification accuracy compared to that obtained with one-dimensional mass spectra. We have begun a project to explore the use of computational pattern recognition methods to identify compounds from their tandem mass, or MS-MS, spectra. We have obtained MS-MS spectra of several potentially hazardous polycyclic aromatic hydrocarbons and other compounds of environmental interest and applied pattern recognition to these spectra. Some of our recent findings are discussed below.

Relative log P values of dimethylether to methanol and dimethylamine to methylamine were calculated in the chloroform/water system using Monte Carlo simulations and statistical perturbation theory. Correct ordering of the calculated relative log Ps was obtained for the two pairs although the method leads to an overestimation of these values. In aqueous solution, both dimethyl ether and dimethylamine solutes are proton acceptors forming a single hydrogen bond to water. Dimethylamine forms a stable N-H-Ow hydrogen bond while the water hydrogen is poorly localized in the O-H-Ow bond to the ether. In chloroform, the solvent molecules are less ordered around the solutes than was found around methanol and methylamine.

With use of statistical perturbation theory and Monte Carlo simulation methods, relative chloroform/water partition coefficients for the solutes methylamine, methanol, and acetonitrile were computed. Good agreement was obtained for the experimental and computed relative log P values for methanol and methylamine. The log P values of acetonitrile relative to the other solutes are overestimated. An analysis of the thermodynamics suggests that entropy plays an important role in the partitioning process. an examination of the solution structure reveals that structure ordering of the solvent chloroform around the solutes via dipole-dipole interactions is rather poor even in the first solvation shells of the polar sites of the solutes. No H...Cl hydrogen bond was found in the solutions of methanol and methylamine.

[I] Grosjean, D., and Fung, K., J. Air Pollut. Control Assoc. 34, 537 (1984). [2] Sonnefeld, W. J., Zoller, W. H., May, W. E., Anal. Chem. 55, 275 (1983). [3] Yamasaki, H., Kuwata, K., and Kuge, Y., Nippon Kagaku Kaishi 8, 1324 (1984) [4] Arey, J., Zielinska, B., Atkinson, R., and Winer, A. M., Atmos. Environ. 21, 1437 (1987) [5) Arey, J., Zielinska, B., Atkinson, R., Winer, A. M., Ramdahl, T., and Pits, J. N., Jr., Atmos. Environ. 20, 2339 (1986). [6] Atkinson, R., Arey, J., Zielinska, B., Winer, A. M., and Pitts, J. N., Jr., The Formation of Nitropolycyclic Hydrocarbons and their Contribution to the Mutagenicity of Ambient Air, In: Short-Term Bioassays in the Analysis of Complex Environmental Mixtures V, Sandhu, S. S., DeMarini, D. M., Mass, M. J., Moore, M. M., and Mumford, J. S., Eds., Plenum Press, in press (1987). [7] Zielinska, B., Arey, J., Atkinson, R., and McElroy, P. A., Nitration of Acephenanthrylene Under Simulated Atmospheric Conditions in Solution, and the Presence of Nitroacephenanthrylene(s) in Ambient Particles, Environ. Sci. Technol., submitted for publication (1987). [8] Zielinska, B., Arey, J., Atkinson, R., and Winer, A. M., The Nitroarenes of Molecular Weight 247 in Ambient Particulate Samples, J. Chromatogr., to be submitted (1987).

AbstractThe relationship between chemical structure of a solute and the logarithm of its partition coefficient (log P) between the aqueous and a nonpolar phase is poorly understood. We have recently shown that the variation in log P data for 50 low molecular‐weight organic solutes in 6 aqueous‐non‐polar solvents is a function of two structural features. The main feature accounts for ≈︁60% of the variation in the log P data, and is uniformly weighted in all 6 nonpolar solvent systems. This suggests that it is related to the aqueous solution properties of the solute. The first feature is termed the isotropic surface area, or the surface area associated with the nonpolar portion of the solute when the solute is considered to be a hydrated complex. The hydrated solute complex is termed a supermolecule with waters of hydration occupying hydrogen bonding sites on the functional groups of the solute. Empirical rules for formation of the super molecule are discussed.In this report the analysis is extended to log P data for 72 solutes in the 6 nonpolar solvent systems. The results of the analysis are essentially unchanged for this more extended data set and the second factor is tentatively identified. The second structural feature accounted for ≈︁35% of the variation in the log P data was not equally weighted in all solvents and is difficult to interpret structurally.

The logarithm of the partition coefficient (log P) of low-molecular-weight organic compounds is a physicochemical parameter used extensively in structure-biological activity studies to model interactions of the compounds with nonpolar phases in vitro and in vivo. The partition coefficient can be determined between water and a number of nonpolar solvents. The most common nonpolar solvent is 1-octanol, but solvents such as benzene, carbon tetrachloride, and chloroform are frequently used as models for the nonpolar phases. The functional relationship between chemical structure and partitioning is not well-understood. In this paper, partition coefficient data for 50 solutes in six nonpolar solvent systems are analyzed by using principal components analysis. The objective of the work is to explore the relationship between solute structure and partitioning behavior for simple organic compounds. Two structural factors are found to be important, with the isotropic surface area being the most important. The isotropic surface area can be used to estimate log P in some solvents and as an independent variable in quantitative structure-activity relationships (QSAR). This is illustrated by estimating the rate of epidermal diffusion of steroids.

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTClassification of polychlorinated biphenyl residues: isomers vs. homolog concentrations in modeling Aroclors and polychlorinated biphenyl residuesD. L. Stalling, T. R. Schwartz, W. J. Dunn, and Svante. WoldCite this: Anal. Chem. 1987, 59, 14, 1853–1859Publication Date (Print):July 15, 1987Publication History Published online1 May 2002Published inissue 15 July 1987https://pubs.acs.org/doi/10.1021/ac00141a026https://doi.org/10.1021/ac00141a026research-articleACS PublicationsRequest reuse permissionsArticle Views130Altmetric-Citations21LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose Get e-Alerts