Evidence map›Paper›PMID 24875476›Full record

ArticleNucleic acids research2014

DDI-CPI, a server that predicts drug-drug interactions through implementing the chemical-protein interactome.

Heng Luo, Ping Zhang, Hui Huang, Jialiang Huang, Emily Kao, Leming Shi, Lin He, Lun Yang

Abstract read
In one paragraph

Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed.

  1. Article
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  3. Review
  4. Review
  5. Article
  6. Article
  7. Finding Causal Mechanistic Drug-Drug Interactions from Observational Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020
    Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Pattern Discovery from High-Order Drug-Drug Interaction Relations.Journal of healthcare informatics research · 2018
    Article
  17. Article
  18. Article
  19. Article
  20. Mechanisms and the clinical relevance of complex drug-drug interactions.Clinical pharmacology : advances and applications · 2018
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Heng LuoBio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China University of Arkansas at Little Rock/University of Arkansas for Medical Sciences, Little Rock, AR 72204, USA.
Ping ZhangHealthcare Analytics Research Group, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA.
Hui HuangBio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China.
Jialiang HuangDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Heath, Boston, MA 02215, USA.
Emily KaoDepartment of Bioengineering, University of California at Berkeley, Berkeley, CA 94720, USA.
Leming ShiSchool of Pharmacy, Fudan University, Shanghai 201203, China.
Lin HeBio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China.
Lun YangBio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, China lun.yang@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drug-drug interactions (DDIs) may cause serious side-effects that draw great attention from both academia and industry. Since some DDIs are mediated by unexpected drug-human protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles of the drugs to predict their DDIs. Here we introduce the DDI-CPI server, which can make real-time DDI predictions based only on molecular structure. When the user submits a molecule, the server will dock user's molecule across 611 human proteins, generating a CPI profile that can be used as a feature vector for the pre-constructed prediction model. It can suggest potential DDIs between the user's molecule and our library of 2515 drug molecules. In cross-validation and independent validation, the server achieved an AUC greater than 0.85. Additionally, by investigating the CPI profiles of predicted DDI, users can explore the PK/PD proteins that might be involved in a particular DDI. A 3D visualization of the drug-protein interaction will be provided as well. The DDI-CPI is freely accessible at http://cpi.bio-x.cn/ddi/.

Indexed as

Drug DesignSoftwareHumansInternetMolecular Docking SimulationPharmaceutical PreparationsProteinsSertralinePharmaceutical PreparationsProteinsSertraline

Identifiers

PMID24875476
PMCPMC4086096

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.