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Molecular generative model based on conditional variational autoencoder for de novo molecular design

Overview of attention for article published in Journal of Cheminformatics, July 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
10 X users
patent
6 patents

Citations

dimensions_citation
236 Dimensions

Readers on

mendeley
363 Mendeley
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Title
Molecular generative model based on conditional variational autoencoder for de novo molecular design
Published in
Journal of Cheminformatics, July 2018
DOI 10.1186/s13321-018-0286-7
Pubmed ID
Authors

Jaechang Lim, Seongok Ryu, Jin Woo Kim, Woo Youn Kim

Abstract

We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with five target properties. We were also able to adjust a single property without changing the others and to manipulate it beyond the range of the dataset.

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 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 363 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 363 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 78 21%
Researcher 64 18%
Student > Master 41 11%
Student > Bachelor 33 9%
Other 19 5%
Other 30 8%
Unknown 98 27%
Readers by discipline Count As %
Chemistry 70 19%
Computer Science 54 15%
Biochemistry, Genetics and Molecular Biology 30 8%
Engineering 26 7%
Chemical Engineering 15 4%
Other 54 15%
Unknown 114 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 27. 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 23 November 2023.
All research outputs
#1,443,639
of 25,381,384 outputs
Outputs from Journal of Cheminformatics
#81
of 958 outputs
Outputs of similar age
#29,715
of 333,346 outputs
Outputs of similar age from Journal of Cheminformatics
#2
of 15 outputs
Altmetric has tracked 25,381,384 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 958 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.1. This one has done particularly well, scoring higher than 91% 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 333,346 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 15 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 93% of its contemporaries.