Cited by 143Computational Drug Discovery MethodsBioinformatics and Genomic NetworksMachine Learning in Bioinformatics

Comprehensive prediction of drug-protein interactions and side effects for the human proteome

Open access
Zhou, Hongyi, Gao, Mu, Skolnick, Jeffrey·2015·Scientific Reports

Abstract

Identifying unexpected drug-protein interactions is crucial for drug repurposing. We develop a comprehensive proteome scale approach that predicts human protein targets and side effects of drugs. For drug-protein interaction prediction, FINDSITE comb, whose average precision is ~30% and recall ~27%, is employed. For side effect prediction, a new method is developed with a precision of ~57% and a recall of ~24%. Our predictions show that drugs are quite promiscuous, with the average (median) number of human targets per drug of 329 (38), while a given protein interacts with 57 drugs. The result implies that drug side effects are inevitable and existing drugs may be useful for repurposing, with only ~1,000 human proteins likely causing serious side effects. A killing index derived from serious side effects has a strong correlation with FDA approved drugs being withdrawn. Therefore, it provides a pre-filter for new drug development. The methodology is free to the academic community on the DR. PRODIS (DRugome, PROteome and DISeasome) webserver at http://cssb.biology.gatech.edu/dr.prodis/. DR. PRODIS provides protein targets of drugs, drugs for a given protein target, associated diseases and side effects of drugs, as well as an interface for the virtual target screening of new compounds.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/srep11090

Cites

58 of 58 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

1 of the 59 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

  1. Arnott S. in Oxford Handbook of Nucleic Acid Structure (ed Neidle S.) 1– 38 (Oxford University Press, 1999).

Cited by

Sorted by citations

Authors
Zhou, H., Gao, M., & Skolnick, J.
Affiliations
Georgia Institute of Technology (US)
Corresponding author
Skolnick, Jeffrey
Venue
Scientific Reports
Published
2015-06-09
Volume
5
Issue
1
Pages
11090-11090
ISSN
2045-2322 (ISSN-L)
OpenAlex
W2416913394
Cited by
138
References
59
Citations by year
2026: 2 · 2025: 8 · 2024: 24 · 2023: 8 · 2022: 9 · 2021: 13 · 2020: 16 · 2019: 17 · 2018: 14 · 2017: 16 · 2016: 9 · 2015: 2
FWCI
14.06
Type
journal article
Language
en
Open access
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health
  • U.S. Bureau of Land Management
  • National Institute of General Medical Sciences (GM 37408, R01GM037408, GM-48835, R01GM048835)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
drug-protein interaction prediction, drug side effect prediction, drug repurposing, human proteome, drug promiscuity, drug withdrawal, disease association
MeSH terms
Pharmaceutical Preparations, Humans, Protein Binding, Proteins, Proteome, Drug-Related Side Effects and Adverse Reactions

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review