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Nonpher: computational method for design of hard-to-synthesize structures

Overview of attention for article published in Journal of Cheminformatics, March 2017
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Title
Nonpher: computational method for design of hard-to-synthesize structures
Published in
Journal of Cheminformatics, March 2017
DOI 10.1186/s13321-017-0206-2
Pubmed ID
Authors

Milan Voršilák, Daniel Svozil

Abstract

In cheminformatics, machine learning methods are typically used to classify chemical compounds into distinctive classes such as active/nonactive or toxic/nontoxic. To train a classifier, a training data set must consist of examples from both positive and negative classes. While a biological activity or toxicity can be experimentally measured, another important molecular property, a synthetic feasibility, is a more abstract feature that can't be easily assessed. In the present paper, we introduce Nonpher, a computational method for the construction of a hard-to-synthesize virtual library. Nonpher is based on a molecular morphing algorithm in which new structures are iteratively generated by simple structural changes, such as the addition or removal of an atom or a bond. In Nonpher, molecular morphing was optimized so that it yields structures not overly complex, but just right hard-to-synthesize. Nonpher results were compared with SAscore and dense region (DR), other two methods for the generation of hard-to-synthesize compounds. Random forest classifier trained on Nonpher data achieves better results than models obtained using SAscore and DR data.

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The data shown below were collected from the profiles of 8 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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 25%
Researcher 5 16%
Student > Ph. D. Student 5 16%
Student > Bachelor 4 13%
Other 2 6%
Other 4 13%
Unknown 4 13%
Readers by discipline Count As %
Chemistry 15 47%
Pharmacology, Toxicology and Pharmaceutical Science 4 13%
Engineering 3 9%
Agricultural and Biological Sciences 2 6%
Biochemistry, Genetics and Molecular Biology 2 6%
Other 1 3%
Unknown 5 16%
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 27 June 2019.
All research outputs
#6,387,445
of 24,261,860 outputs
Outputs from Journal of Cheminformatics
#500
of 893 outputs
Outputs of similar age
#97,755
of 313,290 outputs
Outputs of similar age from Journal of Cheminformatics
#14
of 19 outputs
Altmetric has tracked 24,261,860 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 893 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one is in the 43rd percentile – i.e., 43% 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 313,290 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 19 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.