Simon K. Kearsley

Merck, Whitehouse Station, NJ, United States

Are you Simon K. Kearsley?

Claim your profile

Publications (25)63.6 Total impact

  • [Show abstract] [Hide abstract]
    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.
    No preview · Article · Nov 2010 · ChemInform
  • Kiyean Nam · Vladimir Maiorov · Bradley Feuston · Simon Kearsley
    [Show abstract] [Hide abstract]
    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.
    No preview · Article · Aug 2006 · Proteins Structure Function and Bioinformatics
  • [Show abstract] [Hide abstract]
    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.
    No preview · Article · Jul 2005 · Journal of Chemical Information and Modeling
  • [Show abstract] [Hide abstract]
    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.
    No preview · Article · Feb 2005 · Current Topics in Medicinal Chemistry
  • [Show abstract] [Hide abstract]
    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.
    No preview · Article · Nov 2004 · Journal of Chemical Information and Computer Sciences
  • Source
    Robert P Sheridan · Simon K Kearsley
    [Show abstract] [Hide abstract]
    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.
    Preview · Article · Oct 2002 · Drug Discovery Today
  • Robert P. Sheridan · Suresh B. Singh · E.M. Fluder · Simon K. Kearsley
    [Show abstract] [Hide abstract]
    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.
    No preview · Article · Sep 2001 · Journal of Chemical Information and Modeling
  • [Show abstract] [Hide abstract]
    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.
    No preview · Article · May 2001 · Journal of Chemical Information and Computer Sciences
  • [Show abstract] [Hide abstract]
    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.
    No preview · Article · May 2001 · Journal of Medicinal Chemistry
  • [Show abstract] [Hide abstract]
    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.
    No preview · Article · May 2001 · Journal of Medicinal Chemistry
  • Robert P. Sheridan · Sonia G. SanFeliciano · Simon K. Kearsley
    [Show abstract] [Hide abstract]
    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.
    No preview · Article · Dec 2000 · Journal of Molecular Graphics and Modelling
  • R P Sheridan · S B Singh · E M Fluder · S K Kearsley
    [Show abstract] [Hide abstract]
    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.
    No preview · Article · Nov 2000 · Journal of Chemical Information and Computer Sciences
  • Robert P Sheridan · Sonia G SanFeliciano · Simon K Kearsley

    No preview · Article · Jan 2000 · Journal of Molecular Graphics and Modelling
  • Michael D. Miller · Robert P. Sheridan · Simon K. Kearsley
    [Show abstract] [Hide abstract]
    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.
    No preview · Article · Jun 1999 · Journal of Medicinal Chemistry
  • [Show abstract] [Hide abstract]
    ABSTRACT: One of the most common operations in molecular modeling is to superimpose sets of active molecules in order to determine what features they have in common and what the three-dimensional disposition of these features might be. In this paper we describe MEGA-SQ, a procedure based on our previously described method SQ (Miller et al. J. Med. Chem. 1999, 42, 1505- 1514), which generates and scores superpositions of pairs of rigid molecules. Given a set of explicitly generated conformations for each of several molecules, MEGA-SQ generates pairwise comparisons of the conformations and then builds the pairwise comparisons into high-scoring 'ensembles' with a genetic algorithm. An ensemble contains one conformation of each molecule with the conformations oriented so that they have the best mutual superposition. We demonstrate the utility of MEGA-SQ with two examples: angiotensin-II antagonists and neurokinin-1 antagonists. We can show that, without any user bias as to what groups are common to the molecules, MEGA-SQ can produce superpositions that closely resemble published pharmacophore models.
    No preview · Article · Jan 1999
  • Robert P. Sheridan,*,‡ · M.D. Miller · D.J. Underwood · Simon K. Kearsley†
    [Show abstract] [Hide abstract]
    ABSTRACT: Similarity searches using topological descriptors have proved extremely useful in aiding large-scale screening. In this paper we describe the geometric atom pair, the 3D analog of the topological atom pair descriptor (Carhart et al. J. Chem. Inf. Comput. Sci. 1985, 25, 64−73). We show the results of geometric similarity searches using the CONCORD-build structures of typical small druglike molecules as probes. The database to be searched is a 3D version of the Derwent Standard Drug File that contains an average of 10 explicit conformations per compound. Using objective criteria for determining how good a descriptor is in selecting active compounds from large databases, we compare the results using the geometric versus the topological atom pair. We find that geometric and topological atom pairs are about equally effective in selecting active compounds from large databases. How the two types of descriptors rank active compounds is generally about the same as well, but occasionally active compounds will be seen as very similar to a probe in geometric descriptors, but as fairly dissimilar in topological descriptors. These are of two types: (1) compounds where equivalent groups are in the same spatial arrangement as in the probe but are connected by very different bond paths and (2) compounds that can superimpose onto the probe when they are in a folded conformation.
    No preview · Article · Jan 1996 · Journal of Chemical Information and Modeling
  • [Show abstract] [Hide abstract]
    ABSTRACT: Similarity searches using topological descriptors have proved extremely useful in aiding large-scale screening. We describe alternative forms of the atom pair (Carhart et al. J. Chem. Inf. Comput. Sci. 1985, 25, 64-73.) and topological torsion (Nilakantan et al. J. Chem. Inf. Comput. Sci. 1987, 27, 82-85.) descriptors that use physiochemical atom types. These types are based on binding property class, atomic log P contribution, and partial atomic charges. The new descriptors are meant to be more ''fuzzy'' than the original descriptors. We propose objective criteria for determining how effective one descriptor is versus another in selecting active compounds from large databases. Using these criteria, we run similarity searches over the Derwent Standard Drug File with ten typical druglike probes. The new descriptors are not as good as the original descriptors in selecting actives if one considers the average over all probes, but the new descriptors do better for several individual probes. Generally we find that whether one descriptor does better than another varies from probe to probe in a way almost impossible to predict a priori. Most importantly, we find that different descriptors typically select very different sets of actives. Thus it is advantageous to run similarity probes with several types of descriptors.
    No preview · Article · Jan 1996 · Journal of Chemical Information and Modeling

  • No preview · Article · Jan 1996
  • Robert P. Sheridan · Simon K. Kearsley
    [Show abstract] [Hide abstract]
    ABSTRACT: In combinatorial synthesis, molecules are assembled by linking chemically similar fragments. Since the number of available chemical fragments often greatly exceeds the number of distinct fragments that can be used in one synthetic experiment, choosing a subset of fragments becomes problematical. For example, only a few dozen distinct primary and secondary amines have ever been reported to have been used in constructing a library of peptoids (oligomers of N-substituted glycine), while there are several thousand suitable primary and secondary amines that are commercially available. If a combinatorial library is to be constructed with a particular biological activity in mind, computer-based structure-activity methods can be used to rationally select a subset of fragments. In principle one would computationally generate every possible molecule as a combination of fragments, score each molecule by the Likelihood of its being active, and select those fragments that occur in high-scoring molecules. For many cases there are too many combinations to take this exhaustive approach, but genetic algorithms can be used to quickly find high-scoring molecules by sampling a small subset of the total combinatorial space. In this paper we demonstrate how a genetic algorithm is used to select a subset of amines for the construction of a tripeptoid library. We show three examples. In the first example, the scoring is based on the similarity of the tripeptoids to a specific tripeptoid target. Since the target itself can be generated in this example, we have an opportunity to experiment with the protocol of our genetic algorithm. In the second example, scoring is based on the similarity to two tetrapeptide CCK antagonists. In the third, scoring is done by a trend vector derived from activity data on ACE inhibitors. In all cases we show that the genetic algorithm can find, in a modest amount of computer time, high-scoring peptoids that resemble the targets.
    No preview · Article · Mar 1995 · Journal of Chemical Information and Modeling
  • R.P. Sheridan · S.K. Kearsley
    [Show abstract] [Hide abstract]
    ABSTRACT: In combinatorial synthesis, molecules are assembled by linking chemically similar fragments. Since the number of available chemical fragments often greatly exceeds the number of distinct fragments that can be used in one synthetic experiment, choosing a subset of fragments becomes problematical. For example, only a few dozen distinct primary and secondary amines have ever been reported to have been used in constructing a library of peptoids (oligomers of N-substituted glycine), while there are several thousand suitable primary and secondary amines that are commercially available. If a combinatorial library is to be constructed with a particular biological activity in mind, computer-based structure - activity methods can be used to rationally select a subset of fragments. In principle one would computationally generate every possible molecule as a combination of fragments, score each molecule by the likelihood of its being active, and select those fragments that occur in high-scoring molecules. For many cases there are too many combinations to take this exhaustive approach, but genetic algorithms can be used to quickly find high-scoring molecules by sampling a small subset of the total combinatorial space. In this paper we demonstrate how a genetic algorithm is used to select a subset of amines for the construction of a tripeptoid library. We show three examples. In the first example, the scoring is based on the similarity of the tripeptoids to a specific tripeptoid target. Since the target itself can be generated in this example, we have an opportunity to experiment with the protocol of our genetic algorithm. In the second example, scoring is based on the similarity to two tetrapeptide CCK antagonists. In the third, scoring is done by a trend vector derived from activity data on ACE inhibitors. In all cases we show that the genetic algorithm can find, in a modest amount of computer time, high-scoring peptoids that resemble the targets.
    No preview · Article · Jan 1995 · Journal of Chemical Information and Computer Sciences