ChEMBL: a large-scale bioactivity database for drug discovery

Gaulton, Anna; Bellis, Louisa J.; Bento, A. Patrícia; Chambers, Jon; Davies, Mark; Hersey, Anne; Light, Yvonne; McGlinchey, Shaun; Michalovich, David; Al‐Lazikani, Bissan; Overington, John P.

doi:10.1093/nar/gkr777

Cited by 3,493 publications

(3,103 citation statements)

References 33 publications

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“…More than 400 million coordinate sets were downloaded in 2013 from the wwPDB partner sites. Both the utility and the uniformity of PDB data have enabled the development of other databases and datarelated resources, including resources for drug discovery (for a review see [32]); resources focused on small molecules and ligands such as ChEMBL [33], DrugBank [34], BindingDB [35], BindingMOAD [36], and PDBBind [37]; protein structure classification and annotation resources, such as CATH [38,39], SCOP [40][41][42], and PDBsum [43,44]; and focused, specialty annotation resources such as Protein Data Bank of Transmembrane Proteins (PDBTM) [45], ArchDB for functional loops in structures [46], and 3did for protein-protein interaction surfaces [47]. These resources are frequently compiled in the annual Database Issue of Nucleic Acids Research.…”

Section: Current Capabilities and Usagementioning

confidence: 99%

The Protein Data Bank archive as an open data resource

Berman

Kleywegt

Nakamura

et al. 2014

J Comput Aided Mol Des

118

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Section: Current Capabilities and Usagementioning

confidence: 99%

The Protein Data Bank archive as an open data resource

Berman

Kleywegt

Nakamura

et al. 2014

J Comput Aided Mol Des

118

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“…For huge datasets, such as ChEMBL (Gaulton et al, 2012), BindingDB (Liu et al, 2007) and ChemBank (Seiler et al, 2008), the molecules need to be clustered by similarity to reduce the size of the task. An interactive display of millions of leafs is not possible with current technology.…”

Section: Clustering Of Molecules For Very Large Datasetsmentioning

confidence: 99%

“…A molecules' ability to inhibit protein-protein interactions may also be predicted (Kuenemann et al, 2015). In order to discover such interactions, researchers must harness large databases, including PubChem (Wang et al, 2012) and ChEMBL (Gaulton et al, 2012), which contain vast amounts of heterogeneous biological and chemical data. The size and complexity of these datasets necessitate automated tools to explore available chemical space in order to determine chemical relationships and predict potential interactions.…”

Section: Introductionmentioning

confidence: 99%

ChemTreeMap: an interactive map of biochemical similarity in molecular datasets

Lü¹,

Carlson

2016

Bioinformatics

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Motivation: What if you could explain complex chemistry in a simple tree and share that data online with your collaborators? Computational biology often incorporates diverse chemical data to probe a biological question, but the existing tools for chemical data are ill-suited for the very large datasets inherent to bioinformatics. Furthermore, existing visualization methods often require an expert chemist to interpret the patterns. Biologists need an interactive tool for visualizing chemical information in an intuitive, accessible way that facilitates its integration into today's team-based biological research. Results: ChemTreeMap is an interactive, bioinformatics tool designed to explore chemical space and mine the relationships between chemical structure, molecular properties, and biological activity. ChemTreeMap synergistically combines extended connectivity fingerprints and a neighbor-joining algorithm to produce a hierarchical tree with branch lengths proportional to molecular similarity. Compound properties are shown by leaf color, size and outline to yield a user-defined visualization of the tree. Two representative analyses are included to demonstrate ChemTreeMap's capabilities and utility: assessing dataset overlap and mining structure-activity relationships. Availability and Implementation: The examples from this paper may be accessed at http://ajing. github.io/ChemTreeMap/. Code for the server and client are available in the Supplementary Information, at the aforementioned github site, and on Docker Hub (https://hub.docker.com) with the nametag ajing/chemtreemap.

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“…In a recent analysis 14 , 18,208 activity cliffs were extracted from more than 41,000 unique ChEMBL 15 (release 15) compounds with activity against the current spectrum of human targets. Then, differences in LE between lowly and highly potent cliff partners were systematically assessed.…”

Section: Ligand Efficiency and Lipophilic Efficiencymentioning

confidence: 99%

Advancing the activity cliff concept, part II

et al. 2014

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We present a follow up contribution to further complement a previous commentary on the activity cliff concept and recent advances in activity cliff research. Activity cliffs have originally been defined as pairs of structurally similar compounds that display a large difference in potency against a given target. For medicinal chemistry, activity cliffs are of high interest because structure-activity relationship (SAR) determinants can often be deduced from them. Herein, we present up-to-date results of systematic analyses of the ligand efficiency and lipophilic efficiency relationships between activity cliff-forming compounds, which further increase their attractiveness for the practice of medicinal chemistry. In addition, we summarize the results of a new analysis of coordinated activity cliffs and clusters they form. Taken together, these findings considerably add to our evaluation and current understanding of the activity cliff concept. The results should be viewed in light of the previous commentary article.

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ChEMBL: a large-scale bioactivity database for drug discovery

Cited by 3,493 publications

References 33 publications

The Protein Data Bank archive as an open data resource

The Protein Data Bank archive as an open data resource

ChemTreeMap: an interactive map of biochemical similarity in molecular datasets

Advancing the activity cliff concept, part II

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