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Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

Overview of attention for article published in Frontiers in Chemistry, July 2018
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  • High Attention Score compared to outputs of the same age and source (90th percentile)

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262 Mendeley
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Title
Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery
Published in
Frontiers in Chemistry, July 2018
DOI 10.3389/fchem.2018.00315
Pubmed ID
Authors

Ashutosh Kumar, Kam Y. J. Zhang

Abstract

Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 262 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 262 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 44 17%
Student > Ph. D. Student 43 16%
Student > Master 30 11%
Student > Bachelor 27 10%
Student > Doctoral Student 13 5%
Other 31 12%
Unknown 74 28%
Readers by discipline Count As %
Chemistry 48 18%
Biochemistry, Genetics and Molecular Biology 40 15%
Pharmacology, Toxicology and Pharmaceutical Science 24 9%
Agricultural and Biological Sciences 13 5%
Computer Science 12 5%
Other 34 13%
Unknown 91 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 20 January 2024.
All research outputs
#6,119,844
of 23,577,654 outputs
Outputs from Frontiers in Chemistry
#411
of 6,196 outputs
Outputs of similar age
#103,639
of 331,288 outputs
Outputs of similar age from Frontiers in Chemistry
#16
of 181 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 6,196 research outputs from this source. They receive a mean Attention Score of 2.1. This one has done particularly well, scoring higher than 93% 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 331,288 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 181 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.