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Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

Overview of attention for article published in Frontiers in Chemistry, May 2018
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  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

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Title
Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds
Published in
Frontiers in Chemistry, May 2018
DOI 10.3389/fchem.2018.00138
Pubmed ID
Authors

Hongbin Huang, Guigui Zhang, Yuquan Zhou, Chenru Lin, Suling Chen, Yutong Lin, Shangkang Mai, Zunnan Huang

Abstract

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

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X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 234 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 234 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 15%
Researcher 32 14%
Student > Master 26 11%
Student > Bachelor 26 11%
Student > Doctoral Student 10 4%
Other 37 16%
Unknown 69 29%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 46 20%
Chemistry 31 13%
Pharmacology, Toxicology and Pharmaceutical Science 21 9%
Agricultural and Biological Sciences 18 8%
Computer Science 9 4%
Other 26 11%
Unknown 83 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 February 2022.
All research outputs
#17,929,322
of 26,249,293 outputs
Outputs from Frontiers in Chemistry
#1,853
of 6,861 outputs
Outputs of similar age
#224,390
of 344,863 outputs
Outputs of similar age from Frontiers in Chemistry
#56
of 155 outputs
Altmetric has tracked 26,249,293 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,861 research outputs from this source. They receive a mean Attention Score of 2.4. This one has gotten more attention than average, scoring higher than 70% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 344,863 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 155 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.