Ligand-, Structure-and Pharmacophore-based Molecular Fingerprints: A Case Study on Adenosine A1, A2A, A2B, and A3 Receptor Antagonists

F. Sirci, L. Goracci, Jacqueline E. van Muijlwijk-Koezen, H. Gutiérrez-de-Terán, D. Rodriguez, R. Mannhold

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)1247-1266
Number of pages20
JournalJournal of Computer-aided Molecular Design
Volume26
Issue number11
Early online date12 Oct 2012
DOIs
Publication statusPublished - Nov 2012

Fingerprint

adenosines
Dermatoglyphics
Adenosine
Purinergic P1 Receptors
Ligands
ligands
Purinergic P1 Receptor Antagonists
Area Under Curve
curves
retrieval
selectivity

Cite this

@article{d5393ebbf93f4aa597e49817ea68eb4d,
title = "Ligand-, Structure-and Pharmacophore-based Molecular Fingerprints: A Case Study on Adenosine A1, A2A, A2B, and A3 Receptor Antagonists",
abstract = "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.",
author = "F. Sirci and L. Goracci and {van Muijlwijk-Koezen}, {Jacqueline E.} and H. Guti{\'e}rrez-de-Ter{\'a}n and D. Rodriguez and R. Mannhold",
year = "2012",
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Ligand-, Structure-and Pharmacophore-based Molecular Fingerprints: A Case Study on Adenosine A1, A2A, A2B, and A3 Receptor Antagonists. / Sirci, F.; Goracci, L.; van Muijlwijk-Koezen, Jacqueline E.; Gutiérrez-de-Terán, H.; Rodriguez, D.; Mannhold, R.

In: Journal of Computer-aided Molecular Design, Vol. 26, No. 11, 11.2012, p. 1247-1266.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - Ligand-, Structure-and Pharmacophore-based Molecular Fingerprints: A Case Study on Adenosine A1, A2A, A2B, and A3 Receptor Antagonists

AU - Sirci, F.

AU - Goracci, L.

AU - van Muijlwijk-Koezen, Jacqueline E.

AU - Gutiérrez-de-Terán, H.

AU - Rodriguez, D.

AU - Mannhold, R.

PY - 2012/11

Y1 - 2012/11

N2 - 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.

AB - 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.

U2 - 10.1007/s10822-012-9612-8

DO - 10.1007/s10822-012-9612-8

M3 - Article

VL - 26

SP - 1247

EP - 1266

JO - Journal of Computer-aided Molecular Design

JF - Journal of Computer-aided Molecular Design

SN - 0920-654X

IS - 11

ER -