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Effects of Multiple Conformers Per Compound Upon 3-D Similarity Search and Bioassay Data Analysis

Overview
Journal J Cheminform
Publisher Biomed Central
Specialty Chemistry
Date 2012 Nov 9
PMID 23134593
Citations 15
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Abstract

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Background: To improve the utility of PubChem, a public repository containing biological activities of small molecules, the PubChem3D project adds computationally-derived three-dimensional (3-D) descriptions to the small-molecule records contained in the PubChem Compound database and provides various search and analysis tools that exploit 3-D molecular similarity. Therefore, the efficient use of PubChem3D resources requires an understanding of the statistical and biological meaning of computed 3-D molecular similarity scores between molecules.

Results: The present study investigated effects of employing multiple conformers per compound upon the 3-D similarity scores between ten thousand randomly selected biologically-tested compounds (10-K set) and between non-inactive compounds in a given biological assay (156-K set). When the "best-conformer-pair" approach, in which a 3-D similarity score between two compounds is represented by the greatest similarity score among all possible conformer pairs arising from a compound pair, was employed with ten diverse conformers per compound, the average 3-D similarity scores for the 10-K set increased by 0.11, 0.09, 0.15, 0.16, 0.07, and 0.18 for STST-opt, CTST-opt, ComboTST-opt, STCT-opt, CTCT-opt, and ComboTCT-opt, respectively, relative to the corresponding averages computed using a single conformer per compound. Interestingly, the best-conformer-pair approach also increased the average 3-D similarity scores for the non-inactive-non-inactive (NN) pairs for a given assay, by comparable amounts to those for the random compound pairs, although some assays showed a pronounced increase in the per-assay NN-pair 3-D similarity scores, compared to the average increase for the random compound pairs.

Conclusion: These results suggest that the use of ten diverse conformers per compound in PubChem bioassay data analysis using 3-D molecular similarity is not expected to increase the separation of non-inactive from random and inactive spaces "on average", although some assays show a noticeable separation between the non-inactive and random spaces when multiple conformers are used for each compound. The present study is a critical next step to understand effects of conformational diversity of the molecules upon the 3-D molecular similarity and its application to biological activity data analysis in PubChem. The results of this study may be helpful to build search and analysis tools that exploit 3-D molecular similarity between compounds archived in PubChem and other molecular libraries in a more efficient way.

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References
1.
Kirchmair J, Ristic S, Eder K, Markt P, Wolber G, Laggner C . Fast and efficient in silico 3D screening: toward maximum computational efficiency of pharmacophore-based and shape-based approaches. J Chem Inf Model. 2007; 47(6):2182-96. DOI: 10.1021/ci700024q. View

2.
Kurogi Y, Guner O . Pharmacophore modeling and three-dimensional database searching for drug design using catalyst. Curr Med Chem. 2001; 8(9):1035-55. DOI: 10.2174/0929867013372481. View

3.
Bukau B, Weissman J, Horwich A . Molecular chaperones and protein quality control. Cell. 2006; 125(3):443-51. DOI: 10.1016/j.cell.2006.04.014. View

4.
Wang Y, Bolton E, Dracheva S, Karapetyan K, Shoemaker B, Suzek T . An overview of the PubChem BioAssay resource. Nucleic Acids Res. 2009; 38(Database issue):D255-66. PMC: 2808922. DOI: 10.1093/nar/gkp965. View

5.
Holliday J, Hu C, Willett P . Grouping of coefficients for the calculation of inter-molecular similarity and dissimilarity using 2D fragment bit-strings. Comb Chem High Throughput Screen. 2002; 5(2):155-66. DOI: 10.2174/1386207024607338. View