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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

Distribution of 3-D cluster sizes in terms of the number of “conformers” per cluster. Panelsa, b and c are for assay-, protein-, and pathway-centric clusters, respectively. Data for 2-D clusters are not shown because 2-D clustering does not use conformers.
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Fig3: Distribution of 3-D cluster sizes in terms of the number of “conformers” per cluster. Panelsa, b and c are for assay-, protein-, and pathway-centric clusters, respectively. Data for 2-D clusters are not shown because 2-D clustering does not use conformers.

Mentions: The distribution of the cluster sizes in terms of the number of conformers per cluster is displayed in Figure 3. Only the 3-D cluster data are shown because the 2-D clustering does not use conformer models. Figure 3 clearly shows that, for all three bioactivity similarity contexts, the proportion of small clusters (e.g., with two or three conformers) increases in order of  <  <  <  clusters. This trend is reflected in the average number of conformers per cluster (listed in Table 3), which increases in order of  <  <  <  clusters. This order in the cluster size among the four 3-D cluster types remains unchanged when the number of compounds per cluster is used as a measure of the cluster size (as shown in Figure 2; Table 3).Figure 3


PubChem structure-activity relationship (SAR) clusters.

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

Distribution of 3-D cluster sizes in terms of the number of “conformers” per cluster. Panelsa, b and c are for assay-, protein-, and pathway-centric clusters, respectively. Data for 2-D clusters are not shown because 2-D clustering does not use conformers.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig3: Distribution of 3-D cluster sizes in terms of the number of “conformers” per cluster. Panelsa, b and c are for assay-, protein-, and pathway-centric clusters, respectively. Data for 2-D clusters are not shown because 2-D clustering does not use conformers.
Mentions: The distribution of the cluster sizes in terms of the number of conformers per cluster is displayed in Figure 3. Only the 3-D cluster data are shown because the 2-D clustering does not use conformer models. Figure 3 clearly shows that, for all three bioactivity similarity contexts, the proportion of small clusters (e.g., with two or three conformers) increases in order of  <  <  <  clusters. This trend is reflected in the average number of conformers per cluster (listed in Table 3), which increases in order of  <  <  <  clusters. This order in the cluster size among the four 3-D cluster types remains unchanged when the number of compounds per cluster is used as a measure of the cluster size (as shown in Figure 2; Table 3).Figure 3

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