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Research article
Márcia Barros
Universidade de Lisboa Faculdade de Ciências
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9728-9618
License:
This work is licensed under a CC BY 4.0 License. Read Full License
The increasing number of Chemical Compounds is a challenge for the researchers to explore such datasets. In this work, we propose the use of Recommender Systems in the exploration of new Chemical Compounds of interest to scientific researchers. Our approach consists in a Hybrid recommender model suitable for implicit feedback datasets and focused in retrieving a ranked list according to the relevance of the items. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares (ALS) and Bayesian Personalized Ranking(BPR)) and a new content-based algorithm, based on the semantic similarity of the Chemical Compounds in the ChEBI ontology. The algorithms were assessed on an implicit dataset of Chemical Compounds, CheRM-20, with more than 16.000 items (Chemical Compounds). The Hybrid model was able to improve the results of the collaborative-filtering algorithms, with increases of more than 10 percentage points in most of the assessed evaluation metrics.
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CURRENT STATUS: Published
View the published article at Journal of Cheminformatics.
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Editorial decision: Minor revision
On 03 Jan, 2021
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Received 15 Dec, 2020
Review #2 received
Received 15 Dec, 2020
Reviewer #2 agreed
On 10 Dec, 2020
Reviewers invited
Invitations sent on 10 Dec, 2020
Reviewer #1 agreed
On 10 Dec, 2020
Editor assigned
On 09 Dec, 2020
Submission checks complete
On 09 Dec, 2020
Editor invited
On 09 Dec, 2020
No comments provided
Review #2 received
Received 07 Nov, 2020
Editorial decision: Major revision
On 07 Nov, 2020
Review #1 received
Received 06 Nov, 2020
Reviewer #2 agreed
On 30 Oct, 2020
Reviewer #1 agreed
On 30 Oct, 2020
Editor assigned
On 29 Oct, 2020
Reviewers invited
Invitations sent on 29 Oct, 2020
Submission checks complete
On 28 Oct, 2020
Editor invited
On 28 Oct, 2020
Version 1
Posted 08 Sep, 2020
No comments provided
Editorial decision: Major revision
On 29 Sep, 2020
Review #2 received
Received 28 Sep, 2020
Review #1 received
Received 25 Sep, 2020
Reviewer #1 agreed
On 14 Sep, 2020
Reviewer #2 agreed
On 14 Sep, 2020
First submitted
On 03 Sep, 2020
Editor assigned
On 03 Sep, 2020
Reviewers invited
Invitations sent on 03 Sep, 2020
Submission checks complete
On 02 Sep, 2020
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On 02 Sep, 2020
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Subject Areas
Bioinformatics
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A new preprint design is coming!
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See the published version of this preprint at Journal of Cheminformatics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Márcia Barros
Universidade de Lisboa Faculdade de Ciências
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9728-9618
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Journal of Cheminformatics.
No community comments so far
Editorial decision: Minor revision
On 03 Jan, 2021
Review #1 received
Received 15 Dec, 2020
Review #2 received
Received 15 Dec, 2020
Reviewer #2 agreed
On 10 Dec, 2020
Reviewers invited
Invitations sent on 10 Dec, 2020
Reviewer #1 agreed
On 10 Dec, 2020
Editor assigned
On 09 Dec, 2020
Submission checks complete
On 09 Dec, 2020
Editor invited
On 09 Dec, 2020
No comments provided
Review #2 received
Received 07 Nov, 2020
Editorial decision: Major revision
On 07 Nov, 2020
Review #1 received
Received 06 Nov, 2020
Reviewer #2 agreed
On 30 Oct, 2020
Reviewer #1 agreed
On 30 Oct, 2020
Editor assigned
On 29 Oct, 2020
Reviewers invited
Invitations sent on 29 Oct, 2020
Submission checks complete
On 28 Oct, 2020
Editor invited
On 28 Oct, 2020
Version 1
Posted 08 Sep, 2020
No comments provided
Editorial decision: Major revision
On 29 Sep, 2020
Review #2 received
Received 28 Sep, 2020
Review #1 received
Received 25 Sep, 2020
Reviewer #1 agreed
On 14 Sep, 2020
Reviewer #2 agreed
On 14 Sep, 2020
First submitted
On 03 Sep, 2020
Editor assigned
On 03 Sep, 2020
Reviewers invited
Invitations sent on 03 Sep, 2020
Submission checks complete
On 02 Sep, 2020
Editor invited
On 02 Sep, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Journal of Cheminformatics.
The increasing number of Chemical Compounds is a challenge for the researchers to explore such datasets. In this work, we propose the use of Recommender Systems in the exploration of new Chemical Compounds of interest to scientific researchers. Our approach consists in a Hybrid recommender model suitable for implicit feedback datasets and focused in retrieving a ranked list according to the relevance of the items. The model integrates collaborative-filtering algorithms for implicit feedback (Alternating Least Squares (ALS) and Bayesian Personalized Ranking(BPR)) and a new content-based algorithm, based on the semantic similarity of the Chemical Compounds in the ChEBI ontology. The algorithms were assessed on an implicit dataset of Chemical Compounds, CheRM-20, with more than 16.000 items (Chemical Compounds). The Hybrid model was able to improve the results of the collaborative-filtering algorithms, with increases of more than 10 percentage points in most of the assessed evaluation metrics.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
The full text of this article is available to read as a PDF.
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