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Prediction of binding constants of protein ligands: A fast method for the prioritization of hits obtained from de novo design or 3D database search programs

Overview of attention for article published in Perspectives in Drug Discovery and Design, July 1998
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3 Wikipedia pages

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166 Mendeley
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7 CiteULike
Title
Prediction of binding constants of protein ligands: A fast method for the prioritization of hits obtained from de novo design or 3D database search programs
Published in
Perspectives in Drug Discovery and Design, July 1998
DOI 10.1023/a:1007999920146
Pubmed ID
Authors

Hans-Joachim Böhm

Abstract

A dataset of 82 protein-ligand complexes of known 3D structure and binding constant Ki was analysed to elucidate the important factors that determine the strength of protein-ligand interactions. The following parameters were investigated: the number and geometry of hydrogen bonds and ionic interactions between the protein and the ligand, the size of the lipophilic contact surface, the flexibility of the ligand, the electrostatic potential in the binding site, water molecules in the binding site, cavities along the protein-ligand interface and specific interactions between aromatic rings. Based on these parameters, a new empirical scoring function is presented that estimates the free energy of binding for a protein-ligand complex of known 3D structure. The function distinguishes between buried and solvent accessible hydrogen bonds. It tolerates deviations in the hydrogen bond geometry of up to 0.25 A in the length and up to 30 degrees in the hydrogen bond angle without penalizing the score. The new energy function reproduces the binding constants (ranging from 3.7 x 10(-2) M to 1 x 10(-14) M, corresponding to binding energies between -8 and -80 kJ/mol) of the dataset with a standard deviation of 7.3 kJ/mol corresponding to 1.3 orders of magnitude in binding affinity. The function can be evaluated very fast and is therefore also suitable for the application in a 3D database search or de novo ligand design program such as LUDI. The physical significance of the individual contributions is discussed.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 166 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 3%
Brazil 2 1%
Israel 1 <1%
France 1 <1%
Russia 1 <1%
United Kingdom 1 <1%
Unknown 155 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 45 27%
Student > Master 22 13%
Researcher 20 12%
Student > Bachelor 17 10%
Professor 10 6%
Other 27 16%
Unknown 25 15%
Readers by discipline Count As %
Chemistry 61 37%
Agricultural and Biological Sciences 31 19%
Pharmacology, Toxicology and Pharmaceutical Science 13 8%
Biochemistry, Genetics and Molecular Biology 12 7%
Computer Science 6 4%
Other 13 8%
Unknown 30 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 05 August 2020.
All research outputs
#8,571,053
of 25,457,858 outputs
Outputs from Perspectives in Drug Discovery and Design
#420
of 949 outputs
Outputs of similar age
#10,358
of 32,560 outputs
Outputs of similar age from Perspectives in Drug Discovery and Design
#1
of 4 outputs
Altmetric has tracked 25,457,858 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 949 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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 32,560 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them