New 4-Point Pharmacophore Method for Molecular Similarity and Diversity Applications:  Overview of the Method and Applications, Including a Novel Approach to the Design of Combinatorial Libraries Containing Privileged Substructures

Mason, Jonathan S.; Morize, Isabelle; Menard, Paul R.; Cheney, Daniel L.; Hulme, Christopher; Labaudinière, Richard

doi:10.1021/jm9806998

Cited by 380 publications

(361 citation statements)

References 15 publications

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“…Representative state-of-the-art fingerprint designs include hashed connectivity pathways, 5 structural dictionary-based designs, 6 layered-atom environments, 7 and pharmacophore-type fingerprints. 8 Similarities between database and active reference molecules are quantitatively determined by calculating the pairwise overlap of their fingerprint representations. For this purpose, a variety of similarity metrics have been introduced, the most prominent being the Tanimoto coefficient (Tc).…”

Section: Introductionmentioning

confidence: 99%

Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds

Geppert¹,

Horváth²,

Gärtner³

et al. 2008

J. Chem. Inf. Model.

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Similarity searching using molecular fingerprints is computationally efficient and a surprisingly effective virtual screening tool. In this study, we have compared ranking methods for similarity searching using multiple active reference molecules. Different 2D fingerprints were used as search tools and also as descriptors for a support vector machine (SVM) algorithm. In systematic database search calculations, a SVM-based ranking scheme consistently outperformed nearest neighbor and centroid approaches, regardless of the fingerprints that were tested, even if only very small training sets were used for SVM learning. The superiority of SVM-based ranking over conventional fingerprint methods is ascribed to the fact that SVM makes use of information about database molecules, in addition to known active compounds, during the learning phase.

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Section: Introductionmentioning

confidence: 99%

Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds

Geppert¹,

Horváth²,

Gärtner³

et al. 2008

J. Chem. Inf. Model.

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“…The shape of a molecule is often a key factor in determining whether a molecule will be bioactive, and many types of 3D representation have thus been reported in the similarity literature [7]. Examples include fingerprints that encode interatomic distance or angular information [100,101], and descriptions of molecular shape [102,103] and of the distribution of electrostatic charge around a molecule [104,105]. However, these all need to take account of the fact that most molecules are flexible, i.e., they can adopt several or many different 3D shapes (called conformations) (where as a molecule has only a single 2D topology).…”

Section: Resultsmentioning

confidence: 99%

Similarity‐based data mining in files of two‐dimensional chemical structures using fingerprint measures of molecular resemblance

Willett

2011

WIREs Data Min & Knowl

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This paper reviews the use of measures of inter-molecular similarity for processing databases of chemical structures, which play an important role in the discovery of new drugs by the pharmaceutical industry. The similarity measures considered here are based on the use of a fingerprint representation of molecular structure, where a fingerprint is a vector encoding the presence of fragment substructures in a molecule and where the similarity between pairs of such fingerprints is computed using an association coefficient such as the Tanimoto coefficient. The Similar Property Principle provides the basic rationale for the use of similarity methods in three important chemoinformatics applications: similarity searching, database clustering, and molecular diversity analysis. Similarity searching enables the identification of those molecules in a database that are most similar to a userdefined, biologically active query molecule, with data fusion providing an effective way of combining the results of multiple similarity searches. Cluster analysis, typically using the Jarvis-Patrick, Ward or divisive k-means clustering methods, enables the cost-effective selection of molecules for biological testing, for property prediction and for investigating database overlap. Molecular diversity analysis, typically using cluster-based, dissimilarity-based or optimisation-based approaches, enables the identification of structurally diverse sets of molecules, so as to ensure that the full chemical space spanned by a database is tested in the search for novel bioactive molecules.

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“…In this way, targeted libraries can be designed and higher discovery efficiency may be achieved. Subsequent libraries may be used to fill in gaps in the information derived from the first generation results and to probe more finely the most promising portions of chemical space [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…MCSS has been applied previously to guide the design of much smaller libraries targeted to picornavirus [23]. Other groups have shown that pharmacophore approaches incorporating active site interaction information derived from protein structures improve combinatorial library design [24][25][26][27][28]. Indeed, MCSS molecular probe maps can be used to derive active site pharmacophore models [29].…”

Section: Introductionmentioning

confidence: 99%

Ligand design by a combinatorial approach based on modeling and experiment: application to HLA-DR4

Evensen

Joseph‐McCarthy

Weiss

et al. 2007

J Comput Aided Mol Des

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Combinatorial synthesis and large scale screening methods are being used increasingly in drug discovery, particularly for finding novel lead compounds. Although these ''random'' methods sample larger areas of chemical space than traditional synthetic approaches, only a relatively small percentage of all possible compounds are practically accessible. It is therefore helpful to select regions of chemical space that have greater likelihood of yielding useful leads. When three-dimensional structural data are available for the target molecule this can be achieved by applying structure-based computational design methods to focus the combinatorial library. This is advantageous over the standard usage of computational methods to design a small number of specific novel ligands, because here computation is employed as part of the combinatorial design process and so is required only to determine a propensity for binding of certain chemical moieties in regions of the target molecule. This paper describes the application of the Multiple Copy Simultaneous Search (MCSS) method, an active site mapping and de novo structure-based design tool, to design a focused combinatorial library for the class II MHC protein HLA-DR4. Methods for the synthesizing and screening the computationally designed library are presented; evidence is provided to show that binding was achieved. Although the structure of the protein-ligand complex could not be determined, experimental results including cross-exclusion of a known HLA-DR4 peptide ligand (HA) by a compound from the library. Computational model building suggest that at least one of the ligands designed and identified by the methods described binds in a mode similar to that of native peptides.

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New 4-Point Pharmacophore Method for Molecular Similarity and Diversity Applications: Overview of the Method and Applications, Including a Novel Approach to the Design of Combinatorial Libraries Containing Privileged Substructures

Cited by 380 publications

References 15 publications

Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds

Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds

Similarity‐based data mining in files of two‐dimensional chemical structures using fingerprint measures of molecular resemblance

Ligand design by a combinatorial approach based on modeling and experiment: application to HLA-DR4

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