SEA statistics

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Citations per year

Number of citations per year for the bioinformatics software tool SEA
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Tool usage distribution map

This map represents all the scientific publications referring to SEA per scientific context
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Protocols

SEA specifications

Information


Unique identifier OMICS_18727
Name SEA
Alternative name Similarity Ensemble Approach
Interface Web user interface
Restrictions to use None
Input format SMILES
Computer skills Basic
Stability Stable
Maintained Yes

Maintainers


  • person_outline Andrej Sali
  • person_outline John Irwin

Publication for Similarity Ensemble Approach

SEA citations

 (70)
library_books

Structural ensemble based docking simulation and biophysical studies discovered new inhibitors of Hsp90 N terminal domain

2018
Sci Rep
PMCID: 5762686
PMID: 29321504
DOI: 10.1038/s41598-017-18332-8

[…] ) for 1–4, respectively (Fig. ). The data with ZINC18130036 and ZINC00421600 came from the PubChem BioAssay database (AID: 712). In order to compare the chemical similarity in detail, we employed the similarity ensemble approach (SEA). In SEA, the pairwise Tc values between two molecules are summed to form ΣTc. By comparing the ΣTc in a test molecule and the distribution of ΣTc in a set of small m […]

library_books

Prediction of enzymatic pathways by integrative pathway mapping

2018
eLife
PMCID: 5788505
PMID: 29377793
DOI: 10.7554/eLife.31097.026

[…] Comparison of ensembles of ligands using the Similarity Ensemble Approach (SEA) version 1.0 can predict functionally-linked proteins from the similarity of their ligands (), irrespective of their sequence or structural similarities (). It is mor […]

library_books

Thiopurine Drugs Repositioned as Tyrosinase Inhibitors

2017
Int J Mol Sci
PMCID: 5796027
PMID: 29283382
DOI: 10.3390/ijms19010077

[…] icated that the thiopurine drugs showed limited chemical similarities to the known inhibitors, which was supported by the apparent differences. To assess the limited similarity in detail, we used the similarity ensemble approach (SEA) [], which involves summing up the Tc values between a test molecule and a set of small molecules to obtain the ∑Tc. The histogram of the ∑Tc values between a set of […]

library_books

Total Coumarins from Hydrangea paniculata Show Renal Protective Effects in Lipopolysaccharide Induced Acute Kidney Injury via Anti inflammatory and Antioxidant Activities

2017
Front Pharmacol
PMCID: 5735979
PMID: 29311915
DOI: 10.3389/fphar.2017.00872

[…] ration; their chemical structures are listed in Supplemental Figure . The 3D molecular structures of these four compounds were downloaded from the “Pubmed compound database,” and were placed into the Similarity ensemble approach (SEA) database (http://sea.bkslab.org/) for simulating molecular docking. The predicted targets were input to Therapeutic Target Database (TTD), PharmGKB, and Drugbank dat […]

library_books

Predicted Biological Activity of Purchasable Chemical Space

2017
J Chem Inf Model
PMCID: 5780839
PMID: 29193970
DOI: 10.1021/acs.jcim.7b00316

[…] cal, so prioritizing compounds through computational predictions is a pragmatic alternative. There are many methods for predicting biological activities by chemical similarity;− here, we use two. The Similarity Ensemble Approach (SEA), predicts biological targets of a compound based on its resemblance to ligands annotated in a reference database, such as ChEMBL. SEA relates proteins by their pharm […]

library_books

System Pharmacology Based Dissection of the Synergistic Mechanism of Huangqi and Huanglian for Diabetes Mellitus

2017
Front Pharmacol
PMCID: 5633780
PMID: 29051733
DOI: 10.3389/fphar.2017.00694

[…] aches combined with chemometric method, information integration, and data-mining were implemented. First of all, the active ingredients were submitted to various servers viz. DRAR-CPI (Luo et al., ), Similarity Ensemble Approach (SEA, http://sea.bkslab.org/) (Keiser et al., ), STITCH (http://stitch.embl.de/) (Kuhn et al., ), and PharmMapper server (Wang et al., ). All active compounds were also se […]


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SEA institution(s)
Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA; Biological and Medical Informatics, University of California San Francisco, San Francisco, CA, USA; Departments of Biochemistry and Nutrition and National Institute of Mental Health Psychoactive Drug Screening Program, Case Western Reserve University Medical School, Cleveland, OH, USA; Department of Pharmacology and Division of Medicinal Chemistry and Natural Products (BLR), The University of North Carolina Chapel Hill Medical School, Chapel Hill, NC, USA
SEA funding source(s)
Supported by GM71896, Training Grant GM67547, a National Science Foundation graduate fellowship, the National Institute of Mental Health Psychoactive Drug Screening Program and F32-GM074554.

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