S K Kearsley

Merck, Whitehouse Station, New Jersey, United States

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Publications (20)49.79 Total impact

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    ABSTRACT: ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.
    ChemInform 01/2010; 32(48).
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    ABSTRACT: Leukocyte function associated antigen-1 (LFA-1) plays a critical role in T cell migration and has been recognized as a therapeutic target for immune disorders. Several classes of small molecule antagonists have been developed to block LFA-1 interaction with intercellular adhesion molecule-1 (ICAM-1). Recent structural studies show that the antagonists bind to an allosteric site in the I-domain of LFA-1. However, it is not yet clear how these small molecules work as antagonists since no significant conformational change is observed in the I-domain-antagonist complex structures. Here we present a computational study suggesting how these allosteric antagonists affect the dynamics of the I-domain. The lowest frequency vibrational mode calculated from an LFA-1 I-domain structure shows large scale "coil-down" motion of the C-terminal alpha7 helix, which may lead to the open form of the I-domain. The presence of an allosteric antagonist greatly reduces this motion of the alpha7 helix as well as other parts of the I-domain. Thus, our study suggests that allosteric antagonists work by eliminating breathing motion that leads to the open conformation of the I-domain.
    Proteins Structure Function and Bioinformatics 09/2006; 64(2):376-84. · 3.34 Impact Factor
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    ABSTRACT: Reagent Selector is an intranet-based tool that aids in the selection of reagents for use in combinatorial library construction. The user selects an appropriate reagent group as a query, for example, primary amines, and further refines it on the basis of various physicochemical properties, resulting in a list of potential reagents. The results of this selection process are, in turn, converted into synthons: the fragments or R-groups that are to be incorporated into the combinatorial library. The Synthon Analysis interface graphically depicts the chemical properties for each synthon as a function of the topological bond distance from the scaffold attachment point. Displayed in this fashion, the user is able to visualize the property space for the universe of synthons as well as that of the synthons selected. Ultimately, the reagent list that embodies the selected synthons is made available to the user for reagent procurement. Application of the approach to a sample reagent list for a G-protein coupled receptor targeted library is described.
    Journal of Chemical Information and Modeling 07/2005; 45(5):1439-46. · 4.30 Impact Factor
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    ABSTRACT: Motivated by the need to augment Merck's in-house small molecule collection, web-based tools for designing, enumerating, optimizing and tracking compound libraries have been developed. The path leading to the current version of this Virtual Library Tool Kit (VLTK) is discussed in context of the (then) available commercial offerings and the constraints and requirements imposed by the end users. Though the effort was initiated to simplify the tasks of designing novel, drug-like and diverse compound libraries containing between 2K-10K unique entities, it has also evolved into a powerful tool for outsourcing syntheses as well as lead identification and optimization. The web tool includes components that select reagents, analyze synthons, identify backup reagents, enumerate libraries, calculate properties, optimize libraries and finally track the synthesized compounds through biological assays. In addition to accommodating project specific designs and virtual 3D library scanning, the application includes tools for parallel synthesis, laboratory automation and compound registration.
    Current Topics in Medicinal Chemistry 02/2005; 5(8):773-83. · 3.70 Impact Factor
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    ABSTRACT: How well can a QSAR model predict the activity of a molecule not in the training set used to create the model? A set of retrospective cross-validation experiments using 20 diverse in-house activity sets were done to find a good discriminator of prediction accuracy as measured by root-mean-square difference between observed and predicted activity. Among the measures we tested, two seem useful: the similarity of the molecule to be predicted to the nearest molecule in the training set and/or the number of neighbors in the training set, where neighbors are those more similar than a user-chosen cutoff. The molecules with the highest similarity and/or the most neighbors are the best-predicted. This trend holds true for narrow training sets and, to a lesser degree, for many diverse training sets and does not depend on which QSAR method or descriptor is used. One may define the similarity using a different descriptor than that used for the QSAR model. The similarity dependence for diverse training sets is somewhat unexpected. It appears to be greater for those data sets where the association of similar activities vs similar structures (as encoded in the Patterson plot) is stronger. We propose a way to estimate the reliability of the prediction of an arbitrary chemical structure on a given QSAR model, given the training set from which the model was derived.
    Journal of Chemical Information and Computer Sciences 01/2004; 44(6):1912-28.
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    Robert P Sheridan, Simon K Kearsley
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    ABSTRACT: Computational tools to search chemical structure databases are essential to finding leads early in a drug discovery project. Similarity methods are among the most diverse and most useful. We will present some lessons we have gathered over many years experience with in-house methods on several therapeutic problems. The effectiveness of any similarity method can vary greatly from one biological activity to another in a way that is difficult to predict. Also, any two methods tend to select different subsets of actives from a database, so it is advisable to use several search methods where possible.
    Drug Discovery Today 10/2002; 7(17):903-11. · 6.55 Impact Factor
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    ABSTRACT: A novel method for computing chemical similarity from chemical substructure descriptors is described. This new method, called LaSSI, uses the singular value decomposition (SVD) of a chemical descriptor-molecule matrix to create a low-dimensional representation of the original descriptor space. Ranking molecules by similarity to a probe molecule in the reduced-dimensional space has several advantages over analogous ranking in the original descriptor space: matching latent structures is more robust than matching discrete descriptors, choosing the number of singular values provides a rational way to vary the "fuzziness" of the search, and the reduction in the dimensionality of the chemical space increases searching speed. LaSSI also allows the calculation of the similarity between two descriptors and between a descriptor and a molecule.
    Journal of Medicinal Chemistry 05/2001; 44(8):1177-84. · 5.61 Impact Factor
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    ABSTRACT: Similarity searches based on chemical descriptors have proven extremely useful in aiding large-scale drug screening. Here we present results of similarity searching using Latent Semantic Structure Indexing (LaSSI). LaSSI uses a singular value decomposition on chemical descriptors to project molecules into a k-dimensional descriptor space, where k is the number of retained singular values. The effect of the projection is that certain descriptors are emphasized over others and some descriptors may count as partially equivalent to others. We compare LaSSI searches to searches done with TOPOSIM, our standard in-house method, which uses the Dice similarity definition. Standard descriptor-based methods such as TOPOSIM count all descriptors equally and treat all descriptors as independent. For this work we use atom pairs and topological torsions as examples of chemical descriptors. Using objective criteria to determine how effective one similarity method is versus another in selecting active compounds from a large database, we find for a series of 16 drug-like probes that LaSSI is as good as or better than TOPOSIM in selecting active compounds from the MDDR database, if the user is allowed to treat k as an adjustable parameter. Typically, LaSSI selects very different sets of actives than does TOPOSIM, so it can find classes of actives that TOPOSIM would miss.
    Journal of Medicinal Chemistry 05/2001; 44(8):1185-91. · 5.61 Impact Factor
  • J. Chem. Inf. Comput. Sci. 01/2001; 41:1395-1406.
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    ABSTRACT: A knowledge-based approach for generating conformations of molecules has been developed. The method described here provides a good sampling of the molecule's conformational space by restricting the generated conformations to those consistent with the reference database. The present approach, internally named et for enumerate torsions, differs from previous database-mining approaches by employing a library of much larger substructures while treating open chains, rings, and combinations of chains and rings in the same manner. In addition to knowledge in the form of observed torsion angles, some knowledge from the medicinal chemist is captured in the form of which substructures are identified. The knowledge-based approach is compared to Blaney et al.'s distance geometry (DG) algorithm for sampling the conformational space of molecules. The structures of 113 protein-bound molecules, determined by X-ray crystallography, were used to compare the methods. The present knowledge-based approach (i) generates conformations closer to the experimentally determined conformation, (ii) generates them sooner, and (iii) is significantly faster than the DG method.
    Journal of Chemical Information and Computer Sciences 01/2001; 41(3):754-63.
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    ABSTRACT: In combinatorial synthesis, molecules are assembled by linking chemically similar fragments. Because the number of available chemical fragments often greatly exceeds the number that can be used in one synthetic experiment, one needs a rational method for choosing a subset of desirable fragments. If a combinatorial library is to be targeted against a particular biological activity, virtual screening methods can be used to predict which molecules in a virtual library are most likely to be active. When the number of possible molecules in a virtual library is very large, genetic algorithms (GAs) or simulated annealing can be used to quickly find high-scoring molecules by sampling a small subset of the total combinatorial space. We previously demonstrated how a GA can be used to select a subset of fragments for a combinatorial library, and we used topology-based methods of scoring. Here we extend that earlier work in three ways. (1) We demonstrate use of the GA with 3D scoring methods developed in our laboratory. (2) We show that the approach of assembling libraries from fragments in high-scoring molecules is a reasonable one. (3) We compare results from a library-based GA to those from a molecule-based GA.
    Journal of Molecular Graphics and Modelling 01/2000; 18(4-5):320-34, 525. · 2.33 Impact Factor
  • M D Miller, R P Sheridan, S K Kearsley
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    ABSTRACT: A new method SQ has been developed to provide fast, automatic, and objective pairwise three-dimensional molecular alignments. SQ uses an atom-based clique-matching step followed by an alignment scoring function that has been parametrized to recognize pharmacologically relevant atomic properties. Molecular alignments from SQ are consistent with known drug-receptor interactions. We demonstrate this with six pairs of receptor-ligand complexes from the Brookhaven Protein Data Bank. The SQ-generated alignment of one isolated ligand onto another is shown to approximate the alignment of the ligands when the receptors are superimposed. SQ appears to be better than its predecessor SEAL (Kearsley and Smith, Tetrahedron Comput. Methodol. 1990, 3, 615-633) in this regard. SQ has been tailored so that, given one molecule as a probe, it can be used to routinely scan large chemical databases for which precomputed conformations have been stored. The SQ score, a measure of 3D similarity of each candidate molecule to the probe, can be used to rank compounds for the purposes of chemical screening. We demonstrate this with three probes (a thrombin inhibitor, an HIV protease inhibitor, and a model for angiotensin II). In each case SQ can preferentially select from the MDDR database other compounds with the same activity as the probe. We further show, using the angiotensin example, how SQ can identify topologically diverse compounds with the same activity.
    Journal of Medicinal Chemistry 06/1999; 42(9):1505-14. · 5.61 Impact Factor
  • Journal of Chemical Information and Computer Sciences. 01/1996; 36:128-136.
  • Journal of Chemical Information and Computer Sciences. 01/1996; 36:118-127.
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    ABSTRACT: Structure elucidation for natural product compounds is assisted by making similarity comparisons between the uncharacterized experimental 13C NMR spectrum with relevant databases of estimated spectra. Databases of estimated spectra are deduced from a small set of assigned structures using HOSE codes. Using spectra estimated from structures circumvents problems of inconsistent, incomplete, missing or irrelevant data. It also enables rapid generation of reasonably sized databases that are unavailable from commercial sources. We validate the similarity method theoretically by analyzing what can be best expected from a match of an estimated set of peaks to the experimental spectrum. We also show by example that the method is successful when used in the laboratory.
    Analytica Chimica Acta 01/1995; · 4.39 Impact Factor
  • Robert P. Sheridan, Simon K. Kearsley
    Journal of Chemical Information and Computer Sciences. 01/1995; 35:310-320.
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    ABSTRACT: Specially expanded databases containing three-dimensional structures are created to enhance the utility of docking methods to find new leads, i.e., active compounds of pharmacological interest. The expansion is based on the automatic generation of a set of maximally dissimilar conformations. The ligand receptor system of methotrexate and dihydrofolate reductase is used to demonstrate the feasibility of creating flexibases and their utility in docking studies.
    Journal of Computer-Aided Molecular Design 11/1994; 8(5):565-82. · 3.17 Impact Factor
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    ABSTRACT: We present a system, FLOG (Flexible Ligands Oriented on Grid), that searches a database of 3D coordinates to find molecules complementary to a macromolecular receptor of known 3D structure. The philosophy of FLOG is similar to that reported for DOCK [Shoichet, B.K. et al., J. Comput. Chem., 13 (1992) 380]. In common with that system, we use a match center representation of the volume of the binding cavity and we use a clique-finding algorithm to generate trial orientations of each candidate ligand in the binding site. Also we use a grid representation of the receptor to assess the fit of each orientation. We have introduced a number of novel features within this paradigm. First, we address ligand flexibility by including up to 25 explicit conformations of each structure in our databases. Nonhydrogen atoms in each database entry are assigned one of seven atom types (anion, cation, donor, acceptor, polar, hydrophobic and other) based on their local bonded chemical environments. Second, we have devised a new grid-based scoring function compatible with this 'heavy atom' representation of the ligands. This includes several potentials (electrostatic, hydrogen bonding, hydrophobic and van der Waals) calculated from the location of the receptor atoms. Third, we have improved the fitting stage of the search. Initial dockings are generated with a more efficient clique-finding algorithm. This new algorithm includes the concept of 'essential points', match centers that must be paired with a ligand atom. Also, we introduce the use of a rapid simplex-based rigid-body optimizer to refine the orientations. We demonstrate, using dihydrofolate reductase as a sample receptor, that the FLOG system can select known inhibitors from a large database of drug-like compounds.
    Journal of Computer-Aided Molecular Design 05/1994; 8(2):153-74. · 3.17 Impact Factor
  • Methods in Enzymology 02/1994; 241:354-70. · 2.00 Impact Factor
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    ABSTRACT: Similarity searches based on chemical descriptors have proven extremely useful in aiding large-scale drug screening. Typically an investigator starts with a "probe", a drug-like molecule with an interesting biological activity, and searches a database to find similar compounds. In some projects, however, the only known actives are peptides, and the investigator needs to identify drug-like actives. 3D similarity methods are able to help in this endeavor but suffer from the necessity of having to specify the active conformation of the probe, something that is not always possible at the beginning of a project. Also, 3D methods are slow and are complicated by the need to generate low-energy conformations. In contrast, topological methods are relatively rapid and do not depend on conformation. However, unmodified topological similarity methods, given a peptide probe, will preferentially select other peptides from a database. In this paper we show some simple protocols that, if used with a standard topological similarity search method, are sufficient to select nonpeptide actives given a peptide probe. We demonstrate these protocols by using 10 peptide-like probes to select appropriate nonpeptide actives from the MDDR database.
    Journal of Chemical Information and Computer Sciences 41(5):1395-406.