2D Depiction of Fragment Hierarchies

Clark, Alex M.

doi:10.1021/ci900350h

Cited by 17 publications

(23 citation statements)

References 25 publications

(23 reference statements)

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“…With a clear intuitive advantage over traditional approachs [47], Tree Maps have been widely adopted to demonstrate hierarchical clustering based on the similarity of chemical structures [31] or biological values [49,50]. The biggest advantage of Tree maps is that it can highlight both the distribution of compounds over scaffolds and scaffold structural diversity.…”

Section: Scaffold Countsmentioning

confidence: 99%

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Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds

et al. 2015

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The discovery of novel scaffolds against a specific target has long been one of the most significant but challengeable goals in discovering lead compounds. A scaffold that binds in important regions of the active pocket is more favorable as a starting point because scaffolds generally possess greater optimization possibilities. However, due to the lack of sufficient chemical space diversity of the databases and the ineffectiveness of the screening methods, it still remains a great challenge to discover novel active scaffolds. Since the strengths and weaknesses of both fragment-based drug design and traditional virtual screening (VS), we proposed a fragment VS concept based on Bayesian categorization for the discovery of novel scaffolds. This work investigated the proposal through an application on VEGFR-2 target. Firstly, scaffold and structural diversity of chemical space for 10 compound databases were explicitly evaluated. Simultaneously, a robust Bayesian classification model was constructed for screening not only compound databases but also their corresponding fragment databases. Although analysis of the scaffold diversity demonstrated a very unevenly distribution of scaffolds over molecules, results showed that our Bayesian model behaved better in screening fragments than molecules. Through a literature retrospective research, several generated fragments with relatively high Bayesian scores indeed exhibit VEGFR-2 biological activity, which strongly proved the effectiveness of fragment VS based on Bayesian categorization models. This investigation of Bayesian-based fragment VS can further emphasize the necessity for enrichment of compound databases employed in lead discovery by amplifying the diversity of databases with novel structures.

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Section: Scaffold Countsmentioning

confidence: 99%

“…Therefore, Tree Maps [31,47,50] was used to explicate the structural similarity of the databases. The scaffolds were firstly clustered using the ECFP_4 fingerprints and then visualized using Tree Maps.…”

Section: Scaffold Diversity Analysismentioning

confidence: 99%

Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds

et al. 2015

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“…Depiction algorithms ensuring such a consistent orientation of the structures have been implemented by Clark et al. for the SAR report tool which is part of the MOE software 70,73,74…”

Section: Applications Of Scaffold Classificationsmentioning

confidence: 99%

“…In contrast to the tools described so far, the main purpose of the tool is not to display rule base classifications of structural data, but to visualize structural hierarchy which are interactively built up by user‐defined substructure queries. As pointed out by Clark et al 73,74. the visualization of the scaffolds and their related structures gains intuitiveness, if in the depiction of the structures the individual structures are oriented in the same way as the scaffold they belong to and related scaffold are also oriented in the same way.…”

Section: Applications Of Scaffold Classificationsmentioning

confidence: 99%

Rule‐Based Classification of Chemical Structures by Scaffold

Schuffenhauer¹,

Varin²

2011

Molecular Informatics

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Databases for small organic chemical molecules usually contain millions of structures. The screening decks of pharmaceutical companies contain more than a million of structures. Nevertheless chemical substructure searching in these databases can be performed interactively in seconds. Because of this nobody has really missed structural classification of these databases for the purpose of finding data for individual chemical substructures. However, a full deck high-throughput screen produces also activity data for more than a million of substances. How can this amount of data be analyzed? Which are the active scaffolds identified by an assays? To answer such questions systematic classifications of molecules by scaffolds are needed. In this review it is described how molecules can be hierarchically classified by their scaffolds. It is explained how such classifications can be used to identify active scaffolds in an HTS data set. Once active classes are identified, they need to be visualized in the context of related scaffolds in order to understand SAR. Consequently such visualizations are another topic of this review. In addition scaffold based diversity measures are discussed and an outlook is given about the potential impact of structural classifications on a chemically aware semantic web.

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“…Recently, [91] proposes a visual representation of molecular structural hierarchies alongside with activity information. This approach creates a sequential fragmentation for all the molecules in the dataset using the decomposition rules proposed in [92].…”

Section: Clustering Of Chemical Databasesmentioning

confidence: 99%