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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

Number of structure–activity relationship (SAR) clusters. These numbers do not include clusters with only one compound (i.e., singletons). 3-D clusters that have multiple conformers of only one compound were also regarded as singletons and not included in the statistics. Ntotal indicates the total number of clusters for a given bioactivity similarity context. Numbers in parentheses on the pie charts indicate the percentage of five cluster types (based on structural similarity measures used in clustering) with respect to Ntotal for the corresponding bioactivity similarity context. For all three bioactivity similarity contexts, there are more 3-D clusters than 2-D clusters.
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Fig1: Number of structure–activity relationship (SAR) clusters. These numbers do not include clusters with only one compound (i.e., singletons). 3-D clusters that have multiple conformers of only one compound were also regarded as singletons and not included in the statistics. Ntotal indicates the total number of clusters for a given bioactivity similarity context. Numbers in parentheses on the pie charts indicate the percentage of five cluster types (based on structural similarity measures used in clustering) with respect to Ntotal for the corresponding bioactivity similarity context. For all three bioactivity similarity contexts, there are more 3-D clusters than 2-D clusters.

Mentions: The numbers of SAR clusters generated for the 15 cluster subtypes are compared in Figure 1. There were 9.9 million assay-centric clusters, 2.5 million protein-centric clusters, and 6.2 million pathway-centric clusters. If the five similarity measures employed give similar clustering results, the number of SAR clusters from a given similarity measure is expected to be around 20% of the total number of clusters. However, for all three bioactivity similarity contexts, 2-D clusters corresponded only to 3–4.5% of the total clusters. All remaining clusters were 3-D clusters.Figure 1


PubChem structure-activity relationship (SAR) clusters.

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

Number of structure–activity relationship (SAR) clusters. These numbers do not include clusters with only one compound (i.e., singletons). 3-D clusters that have multiple conformers of only one compound were also regarded as singletons and not included in the statistics. Ntotal indicates the total number of clusters for a given bioactivity similarity context. Numbers in parentheses on the pie charts indicate the percentage of five cluster types (based on structural similarity measures used in clustering) with respect to Ntotal for the corresponding bioactivity similarity context. For all three bioactivity similarity contexts, there are more 3-D clusters than 2-D clusters.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: Number of structure–activity relationship (SAR) clusters. These numbers do not include clusters with only one compound (i.e., singletons). 3-D clusters that have multiple conformers of only one compound were also regarded as singletons and not included in the statistics. Ntotal indicates the total number of clusters for a given bioactivity similarity context. Numbers in parentheses on the pie charts indicate the percentage of five cluster types (based on structural similarity measures used in clustering) with respect to Ntotal for the corresponding bioactivity similarity context. For all three bioactivity similarity contexts, there are more 3-D clusters than 2-D clusters.
Mentions: The numbers of SAR clusters generated for the 15 cluster subtypes are compared in Figure 1. There were 9.9 million assay-centric clusters, 2.5 million protein-centric clusters, and 6.2 million pathway-centric clusters. If the five similarity measures employed give similar clustering results, the number of SAR clusters from a given similarity measure is expected to be around 20% of the total number of clusters. However, for all three bioactivity similarity contexts, 2-D clusters corresponded only to 3–4.5% of the total clusters. All remaining clusters were 3-D clusters.Figure 1

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