Sunghwan Kim - PubChem structure–activity relationship (SAR) clusters

Document created by Sunghwan Kim on Jul 9, 2015Last modified by Sunghwan Kim on Jul 9, 2015
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  Publication Details (including relevant citation   information):

  S. Kim, L. Han,   B. Yu, V.D. Hähnke, E.E. Bolton, and S.H. Bryant;

  Journal of Cheminformatics, 2015, 7,   33.




  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.


  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.


  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.


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