A virtual screen of a subsection of the AstraZeneca compound collection was performed for checkpoint kinase-1 (Chk-1 kinase) using a knowledge-based strategy. This involved initial filtering of the compound collection by application of generic physical properties followed by removal of compounds with undesirable chemical functionality. Subsequently, a 3-D pharmacophore screen for compounds with kinase binding motifs was applied. A database of approximately 200K compounds remained for docking into the active site of Chk-1 kinase, using the FlexX-Pharm program. For each compound that docked successfully into the binding site, up to 100 poses were saved. These poses were then postfiltered using a customized consensus scoring scheme for a kinase, followed by visual inspection of a selection of the docked compounds. This resulted in 103 compounds being ordered for testing in the project assay, and 36 of these (corresponding to four chemical classes) were found to inhibit the enzyme in a dose-response fashion with IC(50) values ranging from 110 nM to 68 microM.

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Methods for the calculation of two properties of interest in drug design, namely free energy of aqueous solvation and lipophilicity (log P), using fragmental methods are reviewed here. Though aqueous solvation free energies are commonly estimated using `whole molecule' methods such as GB/SA and AMSOL, we have recently shown that fragmental approaches can offer high quality predictions as well (for molecules of the size of 20 atoms or less). In the case of log P predictions, the more commonly used ALOGP and CLOGP approaches represent the two extremes of the fragmental constant approach: ALOGP uses atom-sized fragments and no correction factors; CLOGP uses larger fragments and correction factors, which are typically obtained for each series of molecules separately. Anew approach (HLOGP) that uses both smaller (atom-sized) and larger fragments is shown to offer better performance than the other two widely used methods for the prediction of lipophilicity. In this approach, an automated `inventory' of fragments (bonded atom combinations) within a molecule, known as molecular hologram, is used as a composite descriptor and it is used in conjunction with partial least squares for the prediction of aqueous solvation or lipophilicity. It is emphasized that these different methods are useful in different types of drug design applications involving small organic molecules.

Solvation free energy is an important molecular characteristic useful in drug discovery because it represents the desolvation cost of a ligand binding to a receptor. Most of the recent developments in the estimation of solvation free energy require the use of molecular mechanics and dynamics calculations. Group contribution methods have been rarely used in the past for calculating salvation free energy because automated prediction methods have not been developed in this regard. As an aid to combinatorial library design, we explored rapid and accurate means of computing salvation free energies from the covalent structures of organic molecules and compared the results on a test set with the GB/SA solvation model.. Two independent additive-constitutive QSPR methods have been developed for the computation of solvation free energy. The first is a QSPR model (HLOGS) derived using a technique that uses the counts of distinct/similar fragments and substructures for each molecule as variables in a PLS regression. The second method (ALOGS) uses an extensive atom classification scheme developed earlier for the calculation of Log P. A database of 265 molecules with experimentally determined salvation free energies is used to derive the HLOGS (r = 0.97; rms = 0.58) and ALOGS (r = 0.98; rms = 0.38) models, which were then tested on 27 molecules not present in the training set. A detailed comparison of the HLOGS, ALOGS, GB/SA (with AMBER* and OPLSA* potentials) on the test set showed that the HLOGS and ALOGS models give better results than the GB/SA model. Among the three methods tested, the ALOGS method gives the best result on the test set (r = 0.96; rms = 0.86), though the parametrization for this method is incomplete as many atom types are undetermined due to their absence in the current training set. The HLOGS method appears to handle intramolecular interactions better than the ALOGS method.

Human erythropoietin is a haematopoietic cytokine required for the differentiation and proliferation of precursor cells into red blood cells 1 . It activates cells by binding and orientating two cell-surface erythropoietin receptors (EPORs) which trigger an intracellular phosphorylation cascade 2 . The half-maximal response in a cellular proliferation assay is evoked at an erythropoietin concentration of 10 pM ( ref. 3 ), 10 −2 of its K d value for erythropoietin–EPOR binding site 1 ( K d ≈ 1 nM), and 10 −5 of the K d for erythropoietin–EPOR binding site 2 ( K d ≈ 1 μM) 4 . Overall half-maximal binding (IC 50 ) of cell-surface receptors is produced with ∼0.18 nM erythropoietin, indicating that only ∼6% of the receptors would be bound in the presence of 10 pM erythropoietin. Other effective erythropoietin-mimetic ligands that dimerize receptors can evoke the same cellular responses 5 , 6 but much less efficiently, requiring concentrations close to their K d values (∼0.1 μM). The crystal structure of erythropoietin complexed to the extracellular ligand-binding domains of the erythropoietin receptor, determined at 1.9 Å from two crystal forms, shows that erythropoietin imposes a unique 120° angular relationship and orientation that is responsible for optimal signalling through intracellular kinase pathways.

1. The Concept of the Pharmacophore and its Validity The essential functionalities of a molecule necessary for its pharmacological activity are called pharmacophores. Although the idea of pharmacophores existed for a long time, Ehrlich [1] first introduced this terminology following the term chromophorewhich was used to describe the groups responsible for the color of a compound. The interest in the idea of pharmacophores has grown tremendously in the last few decades due to the availability of computer graphics [2], a number of computational methods to determine the pharmacophoric geometry [3‐7] and various software for 3D database mining using the concept of a pharmacophore pattern match [8]. However, the validity of a pharmacophore hypothesis came more from direct medicinal chemistry structure‐activity relationships (SAR) studies rather than from any theoretical calculations. Such calculations may be possible in the near future with the availability of an ever increasing number of ligand‐protein complex structures, better molecular mechanics force fields and better understanding of solvation factors. In such an approach, we have to show that the pharmacophoric groups provide the major contribution in binding affinity to the protein compared to the rest of the molecule. The main objective here is to discuss the various computational methods for pharmacophore identification and its geometry determination, a number of ways to validate the pharmacophore models and their applications in medicinal chemistry to identify novel active compounds. There are several excellent reviews and papers on pharmacophore modelling [3‐7]. 2. The Medicinal Chemistry Approach for Pharmacophore Determination and its use in Computational Chemistry

A full account of how to calculate the electrostatic binding energy using the finite difference solution to the linearized Poisson-Boltzmann equation (FDPB) for protein-ligand systems is described. The following tests show that the statistical and systematic errors due to discrete grid representation of molecular shape and charges amount to about 1% and 5% of calculated binding energy difference, respectively. The greater accuracy results from a three-stage error cancellation: first in Delta G(S), then Delta Delta G(S), and finally Delta Delta(ele). We conclude in this study that the intrinsic error of FDPB is mostly canceled in computing binding energy differences. Among the parameters examined, the partial charge, dielectric constant, and radius of solvent can influence the calculated results most. (C) 1996 by John Wiley & Sons, Inc.

Zinc endopeptidase thermolysin can be inhibited by a series of phosphorus-containing peptide analogues, Cbz-Gly-psi(PO2)-X-Leu-Y-R (ZG(p)(X)L(Y)R), where X = NH, O, or CH2; Y = NH or O; and R = Leu, Ala, Gly, Phe, H, or CH3. The affinity correlation as well as an X-ray crystallography study suggest that these inhibitors bind to thermolysin in an identical mode. In this work, we calculate the electrostatic binding free energies for a series of 13 phosphorus-containing inhibitors with modifications at X, Y, and R moieties using finite difference solution to the Poisson-Boltzmann equation. A method has been developed to include the solvation entropy changes due to binding different ligands to a macromolecule. We demonstrate that the electrostatic energy and empirically derived solvation entropy can account for most of the binding energy differences in this series. By analyzing the binding contribution from individual residues, we show that the energy of a hydrogen bond is not confined to the donor and acceptor. In particular, the positive charges on Zn and Arg 203, which are not the accepters, contribute significantly to the hydrogen bonds between two amides of ZG(p)LL and the thermolysin.

A program (PROBIT) has been developed that allows the reconstruction of a complete set of three-dimensional protein coordinates from alpha-carbon coordinates. The program generates a statistical measure of polypeptide conformational behavior for substructures in a defined structural context from a library of highly refined protein structures. These statistics provide a prescription for substructure substitution from the database to allow regeneration of the complete protein structure.

Energy transfer in the "rapid diffusion" limit from electronically excited terbium(III) chelates in three different charge states to horse heart ferricytochrome c was measured as a function of ionic strength. Theoretical rate constants calculated by numerical integration of the Forster integral (containing the Poisson-Boltzmann-generated protein electrostatic potential) were compared with the experimental data to evaluate the accuracy of protein electrostatic field calculations at the protein/solvent interface. Two dielectric formalisms were used: a simple coulombic/Debye-Hückel procedure and a finite difference method [Warwicker, J. & Watson, H. C. (1982) J. Mol. Biol. 157, 671-679] that accounts for the low-dielectric protein interior and the irregular protein/solvent boundary. Good agreement with experiment was obtained and the ionic-strength dependence of the reaction was successfully reproduced. The sensitivity of theoretical rate constants to the choices of effective donor sphere size and the energy transfer distance criterion was analyzed. Electrostatic potential and rate-constant calculations were carried out on sets of structures collected along two molecular dynamics trajectories of cytochrome c. Protein conformational fluctuations were shown to produce large variations in the calculated energy transfer rate constant. We conclude that protein fluctuations and the resulting transient structures can play significant roles in biological or catalytic activities that are not apparent from examination of a static structure. For calculating protein electrostatics, large-scale low-frequency conformational fluctuations, such as charged side-chain reorientation, are established to be as important as the computational method for incorporating dielectric boundary effects.

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTVibrational intensities of cyclopropene. A molecular force field and dipole moment derivativesKenneth B. Wiberg, Richard C. Dempsey, and John J. WendoloskiCite this: J. Phys. Chem. 1984, 88, 23, 5596–5603Publication Date (Print):November 1, 1984Publication History Published online1 May 2002Published inissue 1 November 1984https://doi.org/10.1021/j150667a028Request reuse permissionsArticle Views61Altmetric-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 InReddit PDF (864 KB) Get e-Alerts

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTThe molecular structure of hydrogen disulfide (H2S2) and barriers to internal rotationDavid A. Dixon, Daniel J. Zeroka, John J. Wendoloski, and Zelda R. WassermanCite this: J. Phys. Chem. 1985, 89, 25, 5334–5336Publication Date (Print):December 1, 1985Publication History Published online1 May 2002Published inissue 1 December 1985https://doi.org/10.1021/j100271a005Request reuse permissions Article Views235Altmetric-Citations42LEARN 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 InReddit PDF (411 KB) Get e-Alerts

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTCharge redistribution in the molecular vibrations of acetylene, ethylene, ethane, methane, silane and the ammonium ion. Signs of the M-H bond momentsKenneth B. Wiberg and John J. WendoloskiCite this: J. Phys. Chem. 1984, 88, 3, 586–593Publication Date (Print):February 1, 1984Publication History Published online1 May 2002Published inissue 1 February 1984https://pubs.acs.org/doi/10.1021/j150647a051https://doi.org/10.1021/j150647a051research-articleACS PublicationsRequest reuse permissionsArticle Views312Altmetric-Citations60LEARN 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 options Get e-Alerts

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTTrefoil aromatics: a potentially new class of aromatic moleculesT. Fukunaga, H. E. Simmons, J. J. Wendoloski, and M. D. GordonCite this: J. Am. Chem. Soc. 1983, 105, 9, 2729–2734Publication Date (Print):May 1, 1983Publication History Published online1 May 2002Published inissue 1 May 1983https://doi.org/10.1021/ja00347a035RIGHTS & PERMISSIONSArticle Views68Altmetric-Citations11LEARN 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 InReddit PDF (641 KB) Get e-AlertscloseSupporting Info (1)»Supporting Information Supporting Information Get e-Alerts

Als "trefoil"‐ Aromaten werden Systeme bezeichnet, die eine Dreizentren‐Zweielektrauen‐Bindung im Zentrum eines Annulenperimeters mit [4n + 2l‐π‐Elektronen haben.

Ab initio Hartee--Fock (HF) and multiconfiguration Hartee--Fock (MCHF) calculations have been carried out to characterize the reactants, transition state, and products of the electrophilic addition of O(/sup 3/P) to the ..pi.. bond of ethylene. The results show that the diradical product CH/sub 2/CH/sub 2/O is stable with respect to the reactants. The transition state has C/sub s/ symmetry, not C/sub 2v/, with the oxygen atom localized on one of the two double-bond C atoms.