Scaffold Searching: Automated Identification of Similar Ring Systems for the Design of Combinatorial Libraries

Bohl, Martin; Dunbar, James B.; Gifford, Eric M.; Wild, David; Willett, Peter; Wilton, David J.

doi:10.1002/qsar.200290001

Cited by 22 publications

(29 citation statements)

References 25 publications

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“…A database such as this may be used in lead discovery programs where bioisosteric ring analogues are sought. Bohl et al 8 discussed the use of shape-similarity methods to identify ring systems that are structurally similar to, and aligned with, a user-defined target ring system. This system can be used to identify alternative scaffolds for the construction of combinatorial libraries.…”

Section: Introductionmentioning

confidence: 99%

Quest for the Rings. In Silico Exploration of Ring Universe To Identify Novel Bioactive Heteroaromatic Scaffolds

et al. 2006

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Bioactive molecules only contain a relatively limited number of unique ring types. To identify those ring properties and structural characteristics that are necessary for biological activity, a large virtual library of nearly 600 000 heteroaromatic scaffolds was created and characterized by calculated properties, including structural features, bioavailability descriptors, and quantum chemical parameters. A self-organizing neural network was used to cluster these scaffolds and to identify properties that best characterize bioactive ring systems. The analysis shows that bioactivity is very sparsely distributed within the scaffold property and structural space, forming only several relatively small, well-defined "bioactivity islands". Various possible applications of a large database of rings with calculated properties and bioactivity scores in the drug design and discovery process are discussed, including virtual screening, support for the design of combinatorial libraries, bioisosteric design, and scaffold hopping.

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Section: Introductionmentioning

confidence: 99%

Quest for the Rings. In Silico Exploration of Ring Universe To Identify Novel Bioactive Heteroaromatic Scaffolds

et al. 2006

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“…1,[3][4][5][6][7][8][9][10][11][12][13] The ability to move to new scaffolds can be of interest in situations where the natural ligands or substrates of protein targets are known but synthetic inhibitors are not and structural information about the target protein is not available. An ideal similarity search method could use endogenous ligand structures to discover drug-like mimetics in large databases in an automated manner.…”

Section: Introductionmentioning

confidence: 99%

A 3D Similarity Method for Scaffold Hopping from Known Drugs or Natural Ligands to New Chemotypes

2004

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A primary goal of 3D similarity searching is to find compounds with similar bioactivity to a reference ligand but with different chemotypes, i.e., "scaffold hopping". However, an adequate description of chemical structures in 3D conformational space is difficult due to the high-dimensionality of the problem. We present an automated method that simplifies flexible 3D chemical descriptions in which clustering techniques traditionally used in data mining are exploited to create "fuzzy" molecular representations called FEPOPS (feature point pharmacophores). The representations can be used for flexible 3D similarity searching given one or more active compounds without a priori knowledge of bioactive conformations or pharmacophores. We demonstrate that similarity searching with FEPOPS significantly enriches for actives taken from in-house high-throughput screening datasets and from MDDR activity classes COX-2, 5-HT3A, and HIV-RT, while also scaffold or ring-system hopping to new chemical frameworks. Further, inhibitors of target proteins (dopamine 2 and retinoic acid receptor) are recalled by FEPOPS by scaffold hopping from their associated endogenous ligands (dopamine and retinoic acid). Importantly, the method excels in comparison to commonly used 2D similarity methods (DAYLIGHT, MACCS, Pipeline Pilot fingerprints) and a commercial 3D method (Pharmacophore Distance Triplets) at finding novel scaffold classes given a single query molecule.

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“…Exact MCS search is one of the most frequently used approaches to identify pharmacophores [14,15], scaffolds [16] and bioisosters [13,17]. Beside these standard approaches there also exists a large number of algorithms for inexact graph matching; an overview can be found in the work of Bengoetxea [18].…”

Section: Theory -Maximum Common Substructurementioning

confidence: 99%

Data and Graph Mining in Chemical Space for ADME and Activity Data Sets

et al. 2006

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We present a classification method, which is based on a coordinate-free chemical space. Thus, it does not depend on descriptor values commonly used by coordinate-based chemical space methods. In our method the molecular similarity of chemical structures is evaluated by a generalized maximum common graph isomorphism, which supports the usage of numerical physicochemical atom property labels in addition to discrete-atomtype labels. The Maximum Common Substructure (MCS) algorithm applies the Highest Scoring Common Substructure (HSCS) ranking of Sheridan and co-workers, which penalizes discontinuous fragments. For all compared classification algorithms used in this work we analyze their usefulness based on two objectives. First, we are interested in highly accurate and general hypotheses and second, the interpretation ability is highly important to increase our structural knowledge for the ADME data sets and the activity data set investigated in this work.

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Scaffold Searching: Automated Identification of Similar Ring Systems for the Design of Combinatorial Libraries

Cited by 22 publications

References 25 publications

Quest for the Rings. In Silico Exploration of Ring Universe To Identify Novel Bioactive Heteroaromatic Scaffolds

Quest for the Rings. In Silico Exploration of Ring Universe To Identify Novel Bioactive Heteroaromatic Scaffolds

A 3D Similarity Method for Scaffold Hopping from Known Drugs or Natural Ligands to New Chemotypes

Data and Graph Mining in Chemical Space for ADME and Activity Data Sets

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