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Methodology
Predicting Target Profiles with Confidence as a Service using Docking Scores
Laeeq Ahmed
Department of Computational Science and Technology, KTH Royal Institute of Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6877-3702
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Journal of Cheminformatics.
Background: Identifying and assessing ligand-target binding is a core component in early drug discovery as
one or more unwanted interactions may be associated with safety issues.
Contributions: We present an open-source, extendable web service for predicting target profiles with
confidence using machine learning for a panel of 7 targets, where models are trained on molecular docking
scores from a large virtual library. The method uses conformal prediction to produce valid measures of
prediction efficiency for a particular confidence level. The service also offers the possibility to dock chemical
structures to the panel of targets with QuickVina on individual compound basis.
Results: The docking procedure and resulting models were validated by docking well-known inhibitors for each
of the 7 targets using QuickVina. The model predictions showed comparable performance to molecular docking
scores against an external validation set. The implementation as publicly available microservices on Kubernetes
ensures resilience, scalability, and extensibility
Keywords
Predicted Target Profiles, Virtual Screening, Drug Discovery, Conformal Prediction, AutoDock Vina, Apache Spark
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Peer Review Timeline
CURRENT STATUS: Published
Version 2
Posted 25 Sep, 2020
Published
On 15 Oct, 2020
No community comments so far
Editor assigned
On 22 Sep, 2020
Editorial decision: Accept
On 22 Sep, 2020
Submission checks complete
On 21 Sep, 2020
Editor invited
On 21 Sep, 2020
No comments provided
Editorial decision: Minor revision
On 05 Sep, 2020
Review #3 received
Received 11 Aug, 2020
Reviewer #3 agreed
On 05 Aug, 2020
Review #2 received
Received 20 Jun, 2020
Review #1 received
Received 20 Jun, 2020
Reviewer #2 agreed
On 30 May, 2020
Reviewers invited
Invitations sent on 23 May, 2020
Reviewer #1 agreed
On 23 May, 2020
Editor assigned
On 21 May, 2020
First submitted
On 20 May, 2020
Submission checks complete
On 20 May, 2020
Editor invited
On 20 May, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Medical Informatics
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This preprint is under consideration at Journal of Cheminformatics. A preprint is 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
Methodology
Predicting Target Profiles with Confidence as a Service using Docking Scores
Laeeq Ahmed
Department of Computational Science and Technology, KTH Royal Institute of Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6877-3702
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
Version 2
Posted 25 Sep, 2020
Published
On 15 Oct, 2020
No community comments so far
Editor assigned
On 22 Sep, 2020
Editorial decision: Accept
On 22 Sep, 2020
Submission checks complete
On 21 Sep, 2020
Editor invited
On 21 Sep, 2020
No comments provided
Editorial decision: Minor revision
On 05 Sep, 2020
Review #3 received
Received 11 Aug, 2020
Reviewer #3 agreed
On 05 Aug, 2020
Review #2 received
Received 20 Jun, 2020
Review #1 received
Received 20 Jun, 2020
Reviewer #2 agreed
On 30 May, 2020
Reviewers invited
Invitations sent on 23 May, 2020
Reviewer #1 agreed
On 23 May, 2020
Editor assigned
On 21 May, 2020
First submitted
On 20 May, 2020
Submission checks complete
On 20 May, 2020
Editor invited
On 20 May, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Medical Informatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Journal of Cheminformatics.
Background: Identifying and assessing ligand-target binding is a core component in early drug discovery as
one or more unwanted interactions may be associated with safety issues.
Contributions: We present an open-source, extendable web service for predicting target profiles with
confidence using machine learning for a panel of 7 targets, where models are trained on molecular docking
scores from a large virtual library. The method uses conformal prediction to produce valid measures of
prediction efficiency for a particular confidence level. The service also offers the possibility to dock chemical
structures to the panel of targets with QuickVina on individual compound basis.
Results: The docking procedure and resulting models were validated by docking well-known inhibitors for each
of the 7 targets using QuickVina. The model predictions showed comparable performance to molecular docking
scores against an external validation set. The implementation as publicly available microservices on Kubernetes
ensures resilience, scalability, and extensibility
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.