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Research article
Andrea R. Beccari
Dompé farmaceutici SpA: Dompe farmaceutici SpA
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6830-2695
License:
This work is licensed under a CC BY 4.0 License. Read Full License
The scaffold representation is widely employed to classify bioactive compounds on the basis of common core structures or correlate compound classes with specific biological activities.
In this paper, we present a novel approach called “Molecular Anatomy” as a flexible and unbiased molecular scaffold-based metrics to cluster large set of compounds. We introduce a set of nine molecular representations at different abstraction levels, combined with fragmentation rules, to define a multi-dimensional network of hierarchically interconnected molecular frameworks. We demonstrate that the introduction of a flexible scaffold definition and multiple pruning rules is an effective method to identify relevant chemical moieties. This approach allows to cluster together different molecular species with similar biological activity, capturing most of the structure activity information, in particular when libraries containing a huge number of singletons are analyzed. We also propose a procedure to derive a network visualization that allows a full graphical representation of compounds dataset, permitting an efficient navigation in the scaffold’s space and significantly contributing to perform high quality SAR analysis.
Keywords
scaffold analysis, HTS data analysis, library design, network visualization, clustering
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CURRENT STATUS: Published
View the published article at Journal of Cheminformatics.
Version (no preprint)
Posted 28 Apr, 2021
Editorial decision: Minor revision
On 28 Apr, 2021
Review #1 received
Received 26 Apr, 2021
Reviewer #2 agreed
On 13 Apr, 2021
Review #2 received
Received 13 Apr, 2021
Reviewers invited
Invitations sent on 12 Apr, 2021
Reviewer #1 agreed
On 12 Apr, 2021
Editor assigned
On 09 Apr, 2021
Submission checks complete
On 09 Apr, 2021
Editor invited
On 09 Apr, 2021
This version was not preprinted
No community comments so far
Editorial decision: Major revision
On 09 Mar, 2021
Review #1 received
Received 22 Feb, 2021
Reviewer #2 agreed
On 09 Feb, 2021
Reviewer #1 agreed
On 09 Feb, 2021
Reviewers invited
Invitations sent on 08 Feb, 2021
Editor assigned
On 02 Feb, 2021
Submission checks complete
On 02 Feb, 2021
Editor invited
On 02 Feb, 2021
Version 1
Posted 07 Jan, 2021
No comments provided
Editorial decision: Revise before peer review
On 02 Jan, 2021
Editor assigned
On 31 Dec, 2020
Submission checks complete
On 31 Dec, 2020
Editor invited
On 31 Dec, 2020
First submitted
On 29 Dec, 2020
Metrics
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Subject Areas
Atomic and Molecular Physics
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
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
Andrea R. Beccari
Dompé farmaceutici SpA: Dompe farmaceutici SpA
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6830-2695
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.
Version (no preprint)
Posted 28 Apr, 2021
Editorial decision: Minor revision
On 28 Apr, 2021
Review #1 received
Received 26 Apr, 2021
Reviewer #2 agreed
On 13 Apr, 2021
Review #2 received
Received 13 Apr, 2021
Reviewers invited
Invitations sent on 12 Apr, 2021
Reviewer #1 agreed
On 12 Apr, 2021
Editor assigned
On 09 Apr, 2021
Submission checks complete
On 09 Apr, 2021
Editor invited
On 09 Apr, 2021
This version was not preprinted
No community comments so far
Editorial decision: Major revision
On 09 Mar, 2021
Review #1 received
Received 22 Feb, 2021
Reviewer #2 agreed
On 09 Feb, 2021
Reviewer #1 agreed
On 09 Feb, 2021
Reviewers invited
Invitations sent on 08 Feb, 2021
Editor assigned
On 02 Feb, 2021
Submission checks complete
On 02 Feb, 2021
Editor invited
On 02 Feb, 2021
Version 1
Posted 07 Jan, 2021
No comments provided
Editorial decision: Revise before peer review
On 02 Jan, 2021
Editor assigned
On 31 Dec, 2020
Submission checks complete
On 31 Dec, 2020
Editor invited
On 31 Dec, 2020
First submitted
On 29 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Atomic and Molecular Physics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Journal of Cheminformatics.
The scaffold representation is widely employed to classify bioactive compounds on the basis of common core structures or correlate compound classes with specific biological activities.
In this paper, we present a novel approach called “Molecular Anatomy” as a flexible and unbiased molecular scaffold-based metrics to cluster large set of compounds. We introduce a set of nine molecular representations at different abstraction levels, combined with fragmentation rules, to define a multi-dimensional network of hierarchically interconnected molecular frameworks. We demonstrate that the introduction of a flexible scaffold definition and multiple pruning rules is an effective method to identify relevant chemical moieties. This approach allows to cluster together different molecular species with similar biological activity, capturing most of the structure activity information, in particular when libraries containing a huge number of singletons are analyzed. We also propose a procedure to derive a network visualization that allows a full graphical representation of compounds dataset, permitting an efficient navigation in the scaffold’s space and significantly contributing to perform high quality SAR analysis.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
This is a list of supplementary files associated with this preprint. Click to download.
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