Journal of Computer-Aided Molecular Design

, Volume 26, Issue 11, pp 1247–1266 | Cite as

Ligand-, structure- and pharmacophore-based molecular fingerprints: a case study on adenosine A1, A2A, A2B, and A3 receptor antagonists

  • Francesco SirciEmail author
  • Laura Goracci
  • David Rodríguez
  • Jacqueline van Muijlwijk-Koezen
  • Hugo Gutiérrez-de-Terán
  • Raimund Mannhold


FLAP fingerprints are applied in the ligand-, structure- and pharmacophore-based mode in a case study on antagonists of all four adenosine receptor (AR) subtypes. Structurally diverse antagonist collections with respect to the different ARs were constructed by including binding data to human species only. FLAP models well discriminate “active” (=highly potent) from “inactive” (=weakly potent) AR antagonists, as indicated by enrichment curves, numbers of false positives, and AUC values. For all FLAP modes, model predictivity slightly decreases as follows: A2BR > A2AR > A3R > A1R antagonists. General performance of FLAP modes in this study is: ligand- > structure- > pharmacophore- based mode. We also compared the FLAP performance with other common ligand- and structure-based fingerprints. Concerning the ligand-based mode, FLAP model performance is superior to ECFP4 and ROCS for all AR subtypes. Although focusing on the early first part of the A2A, A2B and A3 enrichment curves, ECFP4 and ROCS still retain a satisfactory retrieval of actives. FLAP is also superior when comparing the structure-based mode with PLANTS and GOLD. In this study we applied for the first time the novel FLAPPharm tool for pharmacophore generation. Pharmacophore hypotheses, generated with this tool, convincingly match with formerly published data. Finally, we could demonstrate the capability of FLAP models to uncover selectivity aspects although single AR subtype models were not trained for this purpose.


Adenosine receptor subtypes FLAP G protein coupled receptors (GPCRs) GOLD GRID PLANTS ECFP4 ROCS Ligand-based virtual screening Structure-based virtual screening Pharmacophore-based virtual screening 



Adenosine receptors


Area under the curve


Extended-connectivity fingeprint


Enrichment factor


Fingerprint of ligands and proteins


False negative(s)


False positive(s)


Genetic optimization of ligand docking H-bonding, hydrogen bonding


Linear discriminant analysis


Molecular interaction fields


Protein-ligand ant colony optimization


Quantitative structure–activity relationships


Receiver operating characteristic


Rapid overlay of chemical structures



We thank Molecular Discovery Ltd. for granting FLAP suite license. We also thank Lydia Siragusa for assisting us in the Protein–Protein study.

Supplementary material

10822_2012_9612_MOESM1_ESM.xls (274 kb)
Supplementary material 1 (XLS 274 kb)
10822_2012_9612_MOESM2_ESM.doc (428 kb)
Supplementary material 2 (DOC 427 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Francesco Sirci
    • 1
    Email author
  • Laura Goracci
    • 1
  • David Rodríguez
    • 2
  • Jacqueline van Muijlwijk-Koezen
    • 3
  • Hugo Gutiérrez-de-Terán
    • 2
  • Raimund Mannhold
    • 4
  1. 1.Laboratory for Chemometrics and Molecular Modeling, Chemistry DepartmentUniversity of PerugiaPerugiaItaly
  2. 2.Fundación Pública Galega de Medicina Xenómica–SERGASComplejo Hospitalario Universitario de SantiagoSantiago de CompostelaSpain
  3. 3.Leiden/Amsterdam Center for Drug Research (LACDR), Division of Medicinal Chemistry, Department of Pharmacochemistry, Faculty of Exact SciencesVU University AmsterdamAmsterdamThe Netherlands
  4. 4.Department of Laser Medicine, Molecular Drug Research GroupHeinrich-Heine-UniversitätDüsseldorfGermany

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