Journal of Molecular Modeling

, Volume 17, Issue 6, pp 1473–1482 | Cite as

Integrating computational and mixture-based screening of combinatorial libraries

  • Austin B. Yongye
  • Clemencia Pinilla
  • Jose L. Medina-Franco
  • Marc A. Giulianotti
  • Colette T. Dooley
  • Jon R. Appel
  • Adel Nefzi
  • Thomas Scior
  • Richard A. Houghten
  • Karina Martínez-MayorgaEmail author
Original Paper


Mixture-based synthetic combinatorial library (MB-SCL) screening is a well-established experimental approach for rapidly retrieving structure–activity relationships (SAR) and identifying hits. Virtual screening is also a powerful approach that is increasingly being used in drug discovery programs and has a growing number of successful applications. However, limited efforts have been made to integrate both techniques. To this end, we combined experimental data from a MB-SCL of bicyclic guanidines screened against the κ-opioid receptor and molecular similarity methods. The activity data and similarity analyses were integrated in a biometric analysis–similarity map. Such a map allows the molecules to be categorized as actives, activity cliffs, low similarity to the reference compounds, or missed hits. A compound with IC50 = 309 nM was found in the “missed hits” region, showing that active compounds can be retrieved from a MS-SCL via computational approaches. The strategy presented in this work is general and is envisioned as a general-purpose approach that can be applied to other MB-SCLs.

Mixture-based screening activity data and molecular similarity comparisons to known active compounds are integrated via a biometrical analysis-similarity map, to determine the extent to which molecular similarity methods can rescue missed hits from a mixture-based screening synthetic combinatorial library.


Molecular similarity Mixture-based screening Biometric analysis Combinatorial chemistry Virtual screening 



This work was supported by the State of Florida, Executive Officer of the Governor’s Office of Tourism, Trade and Economic Development. We thank OpenEye Scientific Software for providing the OMEGA, ROCS, and VIDA programs, Dr. Tudor Oprea for providing a subset of the Wombat database, and Kyle Kryak for assistance. The work was funded in part by the National Institutes of Health grants 5R21DA019620-02 (RAH), 5P41GM081261-03 (RAH), 3P41GM079590-03S1 (RAH), and 1F03DAO2S850-D1A1 (AN).

Supplementary material

894_2010_850_MOESM1_ESM.doc (147 kb)
Table S1 Substituent groups and activities of the bicyclic guanidines employed for the percent inhibition evaluations (DOC 147 kb)
894_2010_850_MOESM2_ESM.doc (91 kb)
Table S2 Substituent groups and activities of the bicyclic guanidines employed for the IC50 evaluations (DOC 91 kb)
894_2010_850_MOESM3_ESM.doc (459 kb)
Fig. S1 Right: selection of molecules for synthesis based on percent inhibition. Left: scaffold utilized in this study. The R groups of the four internal queries are: R1, R2, R3: S-methyl, S-4-methoxybenzyl, 3-cyclohexylpropyl; S-methyl, R-4-methoxybenzyl, 1-adamantylethyl; S-cyclohexyl, S-cyclohexyl, 4-methyl-cyclohexylmethyl; S-cyclohexyl, R-isobutyl, 4-methyl-cyclohexylmethyl. The full list of substituents is provided in Table S1. Open circles, % inhibition < 27.46; closed circles, % inhibition > 27.46; asterisks, active reference compounds; see [5] (DOC 459 kb)
894_2010_850_MOESM4_ESM.doc (1.1 mb)
Fig. S2 The biometrical analysis–similarity filtering, recovery plots and area under the curves as a function of database screened are shown in the left, middle and right panels, respectively, for percent inhibition-based activities (DOC 1108 kb)
894_2010_850_MOESM5_ESM.doc (54 kb)
Fig. S3 Selection of molecules for synthesis based on IC50 values. Black circles: IC50 < 502 nM; gray circles: 502 nM < IC50 < 1000 nM; white circles: IC50 > 1000 nM. Crosses: IC50 not measured (DOC 54 kb)
894_2010_850_MOESM6_ESM.doc (674 kb)
Fig. S4 Schematic representation of a modified BA–similarity map (top left) and corresponding maps for 3D descriptors (top right) and 2D descriptors (bottom) (DOC 674 kb)


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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Austin B. Yongye
    • 1
  • Clemencia Pinilla
    • 2
  • Jose L. Medina-Franco
    • 1
  • Marc A. Giulianotti
    • 1
  • Colette T. Dooley
    • 1
  • Jon R. Appel
    • 2
  • Adel Nefzi
    • 1
  • Thomas Scior
    • 3
  • Richard A. Houghten
    • 1
    • 2
  • Karina Martínez-Mayorga
    • 1
    Email author
  1. 1.Torrey Pines Institute for Molecular StudiesPort St LucieUSA
  2. 2.Torrey Pines Institute for Molecular StudiesSan DiegoUSA
  3. 3.Departamento de Farmacia, Facultad de Ciencias QuímicasBenemérita Universidad Autónoma de PueblaPueblaMexico

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