Evidence map›Paper›PMID 23525139›Full record

ArticleThe open medicinal chemistry journal2012

ChemVassa: A New Method for Identifying Small Molecule Hits in Drug Discovery.

Brian Moldover, Ada Solidar, Christa Montgomery, Henry Miziorko, Jeff Murphy, Gerald J Wyckoff

Open access · diamondAbstract read
In one paragraph

Article in The open medicinal chemistry journal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 22% of its field
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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
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

6 authors at 2 institutions in 2 countries.

Brian MoldoverVassa Informatics, Kansas City, MO.
Ada Solidar
Christa Montgomery
Henry Miziorko
Jeff Murphy
Gerald J Wyckoff
University of Missouri–Kansas City · USNickel Institute · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ChemVassa, a new chemical structure search technology, was developed to allow rapid in silico screening of compounds for hit and hit-to-lead identification in drug development. It functions by using a novel type of molecular descriptor that examines, in part, the structure of the small molecule undergoing analysis, yielding its "information signature." This descriptor takes into account the atoms, bonds, and their positions in 3-dimensional space. For the present study, a database of ChemVassa molecular descriptors was generated for nearly 16 million compounds (from the ZINC database and other compound sources), then an algorithm was developed that allows rapid similarity searching of the database using a query molecular descriptor (e.g., the signature of atorvastatin, below). A scoring metric then allowed ranking of the search results. We used these tools to search a subset of drug-like molecules using the signature of a commercially successful statin, atorvastatin (Lipitor™). The search identified ten novel compounds, two of which have been demonstrated to interact with HMG-CoA reductase, the macromolecular target of atorvastatin. In particular, one compound discussed in the results section tested successfully with an IC50 of less than 100uM and a completely novel structure relative to known inhibitors. Interactions were validated using computational molecular docking and an Hmg-CoA reductase activity assay. The rapidity and low cost of the methodology, and the novel structure of the interactors, suggests this is a highly favorable new method for hit generation.

Indexed as

cheminformaticsdrug discoveryDrug discoverysmall molecule

Identifiers

PMID23525139
PMCPMC3601345
OpenAlexW2056840645

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.