Cited by 27Computational Drug Discovery MethodsMachine Learning in Bioinformaticsvaccines and immunoinformatics approaches

Elucidating the druggability of the human proteome with eFindSite

Open access
Kana, Omar, Brylinski, Michal·2019·Journal of Computer-Aided Molecular Design

Abstract

Identifying the viability of protein targets is one of the preliminary steps of drug discovery. Determining the ability of a protein to bind drugs in order to modulate its function, termed the druggability, requires a non-trivial amount of time and resources. Inability to properly measure druggability has accounted for a significant portion of failures in drug discovery. This problem is only further exacerbated by the large sample space of proteins involved in human diseases. With these barriers, the druggability space within the human proteome remains unexplored and has made it difficult to develop drugs for numerous diseases. Hence, we present a new feature developed in eFindSite that employs supervised machine learning to predict the druggability of a given protein. Benchmarking calculations against the Non-Redundant data set of Druggable and Less Druggable binding sites demonstrate that an AUC for druggability prediction with eFindSite is as high as 0.88. With eFindSite, we elucidated the human druggability space to be 10,191 proteins. Considering the disease space from the Open Targets Platform and excluding already known targets from the predicted data set reveal 2731 potentially novel therapeutic targets. eFindSite is freely available as a stand-alone software at https://github.com/michal-brylinski/efindsite .

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 · 10.1007/s10822-019-00197-w

Cites

50 of 50 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?

  • High-confidence proteome-wide structural predictions open the door for a large expansion of predicted protein pockets.

    “High-confidence proteome-wide structural predictions open the door for a large expansion of predicted protein pockets35,36.”

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

Research works printed but not linked: 1

  1. [32]Böhning D (1992) Ann Inst Statist Math 44(1): 197

Cited by

Sorted by citations

Authors
Kana, O., & Brylinski, M.
Affiliations
Louisiana State University (US)
Venue
Journal of Computer-Aided Molecular Design
Published
2019-03-19
Volume
33
Issue
5
Pages
509-519
ISSN
  • 0920-654X (ISSN-L)
  • 1573-4951
OpenAlex
W2924545821
Cited by
27
References
50
Citations by year
2026: 1 · 2025: 4 · 2024: 6 · 2023: 4 · 2022: 5 · 2021: 5 · 2020: 1 · 2019: 1
FWCI
8.48
Type
book chapter
Language
en
Open access
Locations
  • Journal of Computer-Aided Molecular Design (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • Civil War Book Review (submittedVersion)
Funders
National Institute of General Medical Sciences (R35GM119524)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords
druggability prediction, human proteome, protein binding sites, drug target identification, novel therapeutic targets, binding site prediction, supervised learning, Open Targets Platform
MeSH terms
Supervised Machine Learning, 5-Aminolevulinate Synthetase, Binding Sites, Humans, Protein Binding, Proteins, Software, Drug Design, Proteome, Drug Discovery, Serine Proteases

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