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Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR

Overview of attention for article published in Journal of Cheminformatics, July 2017
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  • Good Attention Score compared to outputs of the same age (66th percentile)

Mentioned by

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4 tweeters
googleplus
1 Google+ user

Citations

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4 Dimensions

Readers on

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18 Mendeley
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Title
Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR
Published in
Journal of Cheminformatics, July 2017
DOI 10.1186/s13321-017-0229-8
Pubmed ID
Authors

Dilip Narayanan, Osman A. B. S. M. Gani, Franz X. E. Gruber, Richard A. Engh

Abstract

Drug design of protein kinase inhibitors is now greatly enabled by thousands of publicly available X-ray structures, extensive ligand binding data, and optimized scaffolds coming off patent. The extensive data begin to enable design against a spectrum of targets (polypharmacology); however, the data also reveal heterogeneities of structure, subtleties of chemical interactions, and apparent inconsistencies between diverse data types. As a result, incorporation of all relevant data requires expert choices to combine computational and informatics methods, along with human insight. Here we consider polypharmacological targeting of protein kinases ALK, MET, and EGFR (and its drug resistant mutant T790M) in non small cell lung cancer as an example. Both EGFR and ALK represent sources of primary oncogenic lesions, while drug resistance arises from MET amplification and EGFR mutation. A drug which inhibits these targets will expand relevant patient populations and forestall drug resistance. Crizotinib co-targets ALK and MET. Analysis of the crystal structures reveals few shared interaction types, highlighting proton-arene and key CH-O hydrogen bonding interactions. These are not typically encoded into molecular mechanics force fields. Cheminformatics analyses of binding data show EGFR to be dissimilar to ALK and MET, but its structure shows how it may be co-targeted with the addition of a covalent trap. This suggests a strategy for the design of a focussed chemical library based on a pan-kinome scaffold. Tests of model compounds show these to be compatible with the goal of ALK, MET, and EGFR polypharmacology.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 28%
Student > Ph. D. Student 5 28%
Student > Master 4 22%
Professor 2 11%
Professor > Associate Professor 1 6%
Other 0 0%
Unknown 1 6%
Readers by discipline Count As %
Chemistry 6 33%
Agricultural and Biological Sciences 3 17%
Medicine and Dentistry 3 17%
Biochemistry, Genetics and Molecular Biology 2 11%
Computer Science 1 6%
Other 2 11%
Unknown 1 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 18 December 2017.
All research outputs
#3,808,081
of 13,696,365 outputs
Outputs from Journal of Cheminformatics
#320
of 552 outputs
Outputs of similar age
#86,602
of 263,180 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 1 outputs
Altmetric has tracked 13,696,365 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 552 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one is in the 41st percentile – i.e., 41% 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 263,180 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 66% of its contemporaries.
We're also able to compare this research output to 1 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