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Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization

Overview of attention for article published in Frontiers in Chemistry, May 2018
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  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

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3 X users

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Title
Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization
Published in
Frontiers in Chemistry, May 2018
DOI 10.3389/fchem.2018.00188
Pubmed ID
Authors

Claudio N. Cavasotto, Natalia S. Adler, Maria G. Aucar

Abstract

Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 96 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 19%
Student > Ph. D. Student 15 16%
Student > Master 11 11%
Student > Bachelor 7 7%
Professor > Associate Professor 4 4%
Other 14 15%
Unknown 27 28%
Readers by discipline Count As %
Chemistry 31 32%
Biochemistry, Genetics and Molecular Biology 8 8%
Physics and Astronomy 6 6%
Pharmacology, Toxicology and Pharmaceutical Science 5 5%
Agricultural and Biological Sciences 4 4%
Other 13 14%
Unknown 29 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 26 May 2021.
All research outputs
#14,409,968
of 23,081,466 outputs
Outputs from Frontiers in Chemistry
#1,062
of 6,034 outputs
Outputs of similar age
#187,595
of 331,240 outputs
Outputs of similar age from Frontiers in Chemistry
#41
of 163 outputs
Altmetric has tracked 23,081,466 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,034 research outputs from this source. They receive a mean Attention Score of 2.0. This one has done well, scoring higher than 80% 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 331,240 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 163 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 73% of its contemporaries.