Limits...
PubChem structure-activity relationship (SAR) clusters.

Kim S, Han L, Yu B, Hähnke VD, Bolton EE, Bryant SH - J Cheminform (2015)

Bottom Line: The resulting 18 million clusters, named "PubChem SAR clusters", were delivered in such a way that each cluster contains a group of small molecules similar to each other in both structure and bioactivity.Each SAR cluster can be a useful resource in developing a meaningful SAR or enable one to design or expand compound libraries from the cluster.It can also help to predict the potential therapeutic effects and pharmacological actions of less-known compounds from those of well-known compounds (i.e., drugs) in the same cluster.

View Article: PubMed Central - PubMed

Affiliation: National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, 8600 Rockville Pike, Bethesda, MD 20894 USA.

ABSTRACT

Background: Developing structure-activity relationships (SARs) of molecules is an important approach in facilitating hit exploration in the early stage of drug discovery. Although information on millions of compounds and their bioactivities is freely available to the public, it is very challenging to infer a meaningful and novel SAR from that information.

Results: Research discussed in the present paper employed a bioactivity-centered clustering approach to group 843,845 non-inactive compounds stored in PubChem according to both structural similarity and bioactivity similarity, with the aim of mining bioactivity data in PubChem for useful SAR information. The compounds were clustered in three bioactivity similarity contexts: (1) non-inactive in a given bioassay, (2) non-inactive against a given protein, and (3) non-inactive against proteins involved in a given pathway. In each context, these small molecules were clustered according to their two-dimensional (2-D) and three-dimensional (3-D) structural similarities. The resulting 18 million clusters, named "PubChem SAR clusters", were delivered in such a way that each cluster contains a group of small molecules similar to each other in both structure and bioactivity.

Conclusions: The PubChem SAR clusters, pre-computed using publicly available bioactivity information, make it possible to quickly navigate and narrow down the compounds of interest. Each SAR cluster can be a useful resource in developing a meaningful SAR or enable one to design or expand compound libraries from the cluster. It can also help to predict the potential therapeutic effects and pharmacological actions of less-known compounds from those of well-known compounds (i.e., drugs) in the same cluster.

No MeSH data available.


Related in: MedlinePlus

ComboTCT-opt and 2-D clusters for AID 47904. Each node represents a non-inactive compound and the edge between two nodes within a cluster indicates that the distance between the two CIDs is closer than the dthresh value used for clustering. The node color represents the value of the inhibition constant (Ki) for the compound against human carbonic anhydrase (CA) isozyme II. All singletons are removed.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4492103&req=5

Fig8: ComboTCT-opt and 2-D clusters for AID 47904. Each node represents a non-inactive compound and the edge between two nodes within a cluster indicates that the distance between the two CIDs is closer than the dthresh value used for clustering. The node color represents the value of the inhibition constant (Ki) for the compound against human carbonic anhydrase (CA) isozyme II. All singletons are removed.

Mentions: In AID 47904, sulfamide (H2NSO2NH2; CID 82267) and its 25 derivatives, as well as six CA inhibitors already in clinical use, were tested against human CA isozyme II. The PubChem SAR clusters (Clusters 1–27) for these 32 compounds are given as Additional file 1. The corresponding ComboTCT-opt clusters and 2-D clusters are visualized in Figure 8, in which each node represents a compound and the edge between two nodes indicates that the distance between the two corresponding CIDs is closer than the dthresh value used for clustering. When two nodes are in different clusters, no edge is added between them. However, even in this case, the two nodes may still be closer than the dthresh value, which is an inevitable consequence of the clustering algorithm employed.Figure 8


PubChem structure-activity relationship (SAR) clusters.

Kim S, Han L, Yu B, Hähnke VD, Bolton EE, Bryant SH - J Cheminform (2015)

ComboTCT-opt and 2-D clusters for AID 47904. Each node represents a non-inactive compound and the edge between two nodes within a cluster indicates that the distance between the two CIDs is closer than the dthresh value used for clustering. The node color represents the value of the inhibition constant (Ki) for the compound against human carbonic anhydrase (CA) isozyme II. All singletons are removed.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4492103&req=5

Fig8: ComboTCT-opt and 2-D clusters for AID 47904. Each node represents a non-inactive compound and the edge between two nodes within a cluster indicates that the distance between the two CIDs is closer than the dthresh value used for clustering. The node color represents the value of the inhibition constant (Ki) for the compound against human carbonic anhydrase (CA) isozyme II. All singletons are removed.
Mentions: In AID 47904, sulfamide (H2NSO2NH2; CID 82267) and its 25 derivatives, as well as six CA inhibitors already in clinical use, were tested against human CA isozyme II. The PubChem SAR clusters (Clusters 1–27) for these 32 compounds are given as Additional file 1. The corresponding ComboTCT-opt clusters and 2-D clusters are visualized in Figure 8, in which each node represents a compound and the edge between two nodes indicates that the distance between the two corresponding CIDs is closer than the dthresh value used for clustering. When two nodes are in different clusters, no edge is added between them. However, even in this case, the two nodes may still be closer than the dthresh value, which is an inevitable consequence of the clustering algorithm employed.Figure 8

Bottom Line: The resulting 18 million clusters, named "PubChem SAR clusters", were delivered in such a way that each cluster contains a group of small molecules similar to each other in both structure and bioactivity.Each SAR cluster can be a useful resource in developing a meaningful SAR or enable one to design or expand compound libraries from the cluster.It can also help to predict the potential therapeutic effects and pharmacological actions of less-known compounds from those of well-known compounds (i.e., drugs) in the same cluster.

View Article: PubMed Central - PubMed

Affiliation: National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, 8600 Rockville Pike, Bethesda, MD 20894 USA.

ABSTRACT

Background: Developing structure-activity relationships (SARs) of molecules is an important approach in facilitating hit exploration in the early stage of drug discovery. Although information on millions of compounds and their bioactivities is freely available to the public, it is very challenging to infer a meaningful and novel SAR from that information.

Results: Research discussed in the present paper employed a bioactivity-centered clustering approach to group 843,845 non-inactive compounds stored in PubChem according to both structural similarity and bioactivity similarity, with the aim of mining bioactivity data in PubChem for useful SAR information. The compounds were clustered in three bioactivity similarity contexts: (1) non-inactive in a given bioassay, (2) non-inactive against a given protein, and (3) non-inactive against proteins involved in a given pathway. In each context, these small molecules were clustered according to their two-dimensional (2-D) and three-dimensional (3-D) structural similarities. The resulting 18 million clusters, named "PubChem SAR clusters", were delivered in such a way that each cluster contains a group of small molecules similar to each other in both structure and bioactivity.

Conclusions: The PubChem SAR clusters, pre-computed using publicly available bioactivity information, make it possible to quickly navigate and narrow down the compounds of interest. Each SAR cluster can be a useful resource in developing a meaningful SAR or enable one to design or expand compound libraries from the cluster. It can also help to predict the potential therapeutic effects and pharmacological actions of less-known compounds from those of well-known compounds (i.e., drugs) in the same cluster.

No MeSH data available.


Related in: MedlinePlus