- Oral presentation
- Open Access
Development of new in silico methods to identify ligands for orphan GPCR
© Weill and Rognan 2008
- Published: 26 March 2008
- Bayesian Network
- Biogenic Amine
- Protein Couple Receptor
- Shannon Entropy
- Machine Learning Algorithm
G Protein Coupled Receptors (GPCRs) are the principal family of macromolecular targets of pharmaceutic interest. Among human GPCR about 100 are still orphan and awaiting ligands. The primary aim of this study is to predict new ligands for orphan GPCRs. The second aim is to predict for which GPCR a given ligand is interacting (selectivity profile).
In preliminary studies, we have evaluated different machine learning algorithms implemented in Weka  (Bayesian Network, Neronal Network, decision tree, random Forest, SVM…) in terms of their ability to discriminate between true and false biogenic amine receptor-ligand complexes. Receptor-Ligand fingerprints appeared to be superior to ligand fingerprints in discriminating MDDR activity classes. We are currently investigating the possibility to apply above mentioned strategy to all liganded GPCR clusters to define a global model for predicting ligands of new orphan GPCRs.
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