ChemBank: a small-molecule screening and cheminformatics resource database

Seiler, Kathleen Petri; George, Gregory A.; Happ, Mary Pat; Bodycombe, Nicole E.; Carrinski, Hyman A.; Norton, Stephanie; Brudz, Steve; Sullivan, John P.; Muhlich, Jeremy; Serrano, Martin M.; Ferraiolo, Paul; Tolliday, Nicola; Schreiber, Stuart L.; Clemons, Paul A.

doi:10.1093/nar/gkm843

Cited by 245 publications

(217 citation statements)

References 29 publications

(38 reference statements)

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“…ATC codes were available for 59% of the drugs (768 out of 1,309). We retrieved the known target genes for 535 out of 1,309 (41%) drugs from two public repositories, DrugBank (27) and ChemBank (28). We thus assigned a known MoA to 804 drugs out of 1,309 (61%) (Dataset S1).…”

mentioning

confidence: 99%

Discovery of drug mode of action and drug repositioning from transcriptional responses

Iorio

Bosotti²,

Scacheri³

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

755

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A bottleneck in drug discovery is the identification of the molecular targets of a compound (mode of action, MoA) and of its off-target effects. Previous approaches to elucidate drug MoA include analysis of chemical structures, transcriptional responses following treatment, and text mining. Methods based on transcriptional responses require the least amount of information and can be quickly applied to new compounds. Available methods are inefficient and are not able to support network pharmacology. We developed an automatic and robust approach that exploits similarity in gene expression profiles following drug treatment, across multiple cell lines and dosages, to predict similarities in drug effect and MoA. We constructed a "drug network" of 1,302 nodes (drugs) and 41,047 edges (indicating similarities between pair of drugs). We applied network theory, partitioning drugs into groups of densely interconnected nodes (i.e., communities). These communities are significantly enriched for compounds with similar MoA, or acting on the same pathway, and can be used to identify the compoundtargeted biological pathways. New compounds can be integrated into the network to predict their therapeutic and off-target effects. Using this network, we correctly predicted the MoA for nine anticancer compounds, and we were able to discover an unreported effect for a well-known drug. We verified an unexpected similarity between cyclin-dependent kinase 2 inhibitors and Topoisomerase inhibitors. We discovered that Fasudil (a Rho-kinase inhibitor) might be "repositioned" as an enhancer of cellular autophagy, potentially applicable to several neurodegenerative disorders. Our approach was implemented in a tool (Mode of Action by NeTwoRk Analysis, MANTRA, http://mantra.tigem.it).computational drug discovery | drug repurposing | systems biology | chemotherapy

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mentioning

confidence: 99%

Discovery of drug mode of action and drug repositioning from transcriptional responses

Iorio

Bosotti²,

Scacheri³

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

755

728

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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%

“…To demonstrate ChemTreeMap, we have chosen diverse biomolecular datasets ranging from thousands to millions of molecules. To show chem-data overlap, we use some of the largest datasets with bioactivity data: ChEMBL v. 20 (Gaulton et al, 2012), BindingDB (Liu et al, 2007) and ChemBank (Seiler et al, 2008).…”

Section: Datasetsmentioning

confidence: 99%

“…We compared ChEMBL with ChemBank and BindingDB to show datasets that represent collections with more breadth vs more depth, respectively. ChemBank (Seiler et al, 2008) is a set of $1.15 million molecules constructed from chemical HTS studies. Many compounds in ChEMBL are not from HTS studies and will not appear in ChemBank.…”

Section: Comparing Chemical Diversity Of Large Datasetsmentioning

confidence: 99%

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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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“…The C-Map is available on a publicly accessible website (http://www.broad.mit.edu/cmap) that also includes meta-data, such as perturbagen name, concentration, cell line and batch) and analytical tools. It provides links for each compound to a vendor, to ChemBank (a database for structures and synonyms, http://chembank.broad.mit.edu), 62,63 and displays the Anatomical Therapeutic Chemical classification assigned by the World Health Organization to drug substances. Moreover, the raw data are freely available for download.…”

Section: C-map: Guiding Principlesmentioning

confidence: 99%

Genomic approaches to small molecule discovery

Stegmaier

2009

Leukemia

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With the sequencing of the human genome and the development of new genomic technologies, biomedical discovery has been transformed. The applications of these new approaches are ever-expanding from disease classification, to identification of new targets, to outcome prediction. A logical next step is the integration of genomic approaches into small molecule discovery. This review will focus on the application of genomics to compound discovery, with an emphasis on the hematological malignancies. It will focus on the use of genomic tools to discover cancer targets and the development and application of both cell-based and in silico gene expressionbased approaches to small molecule discovery.

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ChemBank: a small-molecule screening and cheminformatics resource database

Cited by 245 publications

References 29 publications

Discovery of drug mode of action and drug repositioning from transcriptional responses

Discovery of drug mode of action and drug repositioning from transcriptional responses

ChemTreeMap: an interactive map of biochemical similarity in molecular datasets

Genomic approaches to small molecule discovery

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