“Molecular Anatomy”: A New Multi-Dimensional Hierarchical Scaffold Analysis tool
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.
Received 22 Feb, 2021
On 09 Feb, 2021
On 09 Feb, 2021
Invitations sent on 08 Feb, 2021
On 02 Feb, 2021
On 02 Feb, 2021
On 02 Feb, 2021
Posted 07 Jan, 2021
On 02 Jan, 2021
On 31 Dec, 2020
On 31 Dec, 2020
On 31 Dec, 2020
On 29 Dec, 2020
“Molecular Anatomy”: A New Multi-Dimensional Hierarchical Scaffold Analysis tool
Received 22 Feb, 2021
On 09 Feb, 2021
On 09 Feb, 2021
Invitations sent on 08 Feb, 2021
On 02 Feb, 2021
On 02 Feb, 2021
On 02 Feb, 2021
Posted 07 Jan, 2021
On 02 Jan, 2021
On 31 Dec, 2020
On 31 Dec, 2020
On 31 Dec, 2020
On 29 Dec, 2020
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