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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 aryl hydrocarbon receptor (AhR; GI 29337198). CID 15625 (2,3,7,8-Tetrachlorodibenzo-p-dioxin, also known as TCDD) is tested in two different publications. The numbers in the squares correspond to the CIDs. The colors of the squares indicate the publications where data were obtained.
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Fig10: ComboTCT-opt and 2-D clusters for aryl hydrocarbon receptor (AhR; GI 29337198). CID 15625 (2,3,7,8-Tetrachlorodibenzo-p-dioxin, also known as TCDD) is tested in two different publications. The numbers in the squares correspond to the CIDs. The colors of the squares indicate the publications where data were obtained.

Mentions: PubChem SAR clusters arising from these 43 compounds and a summary of their sizes are provided in Additional file 2, and the ComboTCT-opt clusters and 2-D clusters are compared in Figure 10 for illustration purposes. The most noticeable aspect is that the flavones/isoflavones and aurones are grouped into the same cluster, indicating that there may be a structural basis for the similarity in biological activity against AhR between the two groups of chemicals, although they were tested in different published research studies using different experimental methods. It is also noteworthy that while TCDD (CID 15625) is grouped into the same 3-D cluster as flavones/isoflavones and aurones, while it is excluded as a singleton after the 2-D clustering. This illustrates how 3-D clustering can complement 2-D clustering.Figure 10


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 aryl hydrocarbon receptor (AhR; GI 29337198). CID 15625 (2,3,7,8-Tetrachlorodibenzo-p-dioxin, also known as TCDD) is tested in two different publications. The numbers in the squares correspond to the CIDs. The colors of the squares indicate the publications where data were obtained.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig10: ComboTCT-opt and 2-D clusters for aryl hydrocarbon receptor (AhR; GI 29337198). CID 15625 (2,3,7,8-Tetrachlorodibenzo-p-dioxin, also known as TCDD) is tested in two different publications. The numbers in the squares correspond to the CIDs. The colors of the squares indicate the publications where data were obtained.
Mentions: PubChem SAR clusters arising from these 43 compounds and a summary of their sizes are provided in Additional file 2, and the ComboTCT-opt clusters and 2-D clusters are compared in Figure 10 for illustration purposes. The most noticeable aspect is that the flavones/isoflavones and aurones are grouped into the same cluster, indicating that there may be a structural basis for the similarity in biological activity against AhR between the two groups of chemicals, although they were tested in different published research studies using different experimental methods. It is also noteworthy that while TCDD (CID 15625) is grouped into the same 3-D cluster as flavones/isoflavones and aurones, while it is excluded as a singleton after the 2-D clustering. This illustrates how 3-D clustering can complement 2-D clustering.Figure 10

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