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Target prediction utilising negative bioactivity data covering large chemical space

Overview of attention for article published in Journal of Cheminformatics, October 2015
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  • Average Attention Score compared to outputs of the same age and source

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Title
Target prediction utilising negative bioactivity data covering large chemical space
Published in
Journal of Cheminformatics, October 2015
DOI 10.1186/s13321-015-0098-y
Pubmed ID
Authors

Lewis H. Mervin, Avid M. Afzal, Georgios Drakakis, Richard Lewis, Ola Engkvist, Andreas Bender

Abstract

In silico analyses are increasingly being used to support mode-of-action investigations; however many such approaches do not utilise the large amounts of inactive data held in chemogenomic repositories. The objective of this work is concerned with the integration of such bioactivity data in the target prediction of orphan compounds to produce the probability of activity and inactivity for a range of targets. To this end, a novel human bioactivity data set was constructed through the assimilation of over 195 million bioactivity data points deposited in the ChEMBL and PubChem repositories, and the subsequent application of a sphere-exclusion selection algorithm to oversample presumed inactive compounds. A Bernoulli Naïve Bayes algorithm was trained using the data and evaluated using fivefold cross-validation, achieving a mean recall and precision of 67.7 and 63.8 % for active compounds and 99.6 and 99.7 % for inactive compounds, respectively. We show the performances of the models are considerably influenced by the underlying intraclass training similarity, the size of a given class of compounds, and the degree of additional oversampling. The method was also validated using compounds extracted from WOMBAT producing average precision-recall AUC and BEDROC scores of 0.56 and 0.85, respectively. Inactive data points used for this test are based on presumed inactivity, producing an approximated indication of the true extrapolative ability of the models. A distance-based applicability domain analysis was also conducted; indicating an average Tanimoto Coefficient distance of 0.3 or greater between a test and training set can be used to give a global measure of confidence in model predictions. A final comparison to a method trained solely on active data from ChEMBL performed with precision-recall AUC and BEDROC scores of 0.45 and 0.76. The inclusion of inactive data for model training produces models with superior AUC and improved early recognition capabilities, although the results from internal and external validation of the models show differing performance between the breadth of models. The realised target prediction protocol is available at https://github.com/lhm30/PIDGIN.Graphical abstractThe inclusion of large scale negative training data for in silico target prediction improves the precision and recall AUC and BEDROC scores for target models.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 183 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 1%
United Kingdom 1 <1%
Netherlands 1 <1%
Spain 1 <1%
Unknown 178 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 43 23%
Student > Ph. D. Student 31 17%
Student > Master 26 14%
Student > Bachelor 12 7%
Student > Doctoral Student 8 4%
Other 33 18%
Unknown 30 16%
Readers by discipline Count As %
Chemistry 42 23%
Computer Science 21 11%
Agricultural and Biological Sciences 19 10%
Pharmacology, Toxicology and Pharmaceutical Science 19 10%
Biochemistry, Genetics and Molecular Biology 18 10%
Other 23 13%
Unknown 41 22%
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 May 2021.
All research outputs
#6,281,635
of 23,577,761 outputs
Outputs from Journal of Cheminformatics
#531
of 874 outputs
Outputs of similar age
#76,455
of 285,389 outputs
Outputs of similar age from Journal of Cheminformatics
#8
of 14 outputs
Altmetric has tracked 23,577,761 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 874 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 39th percentile – i.e., 39% 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 285,389 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 73% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.