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Combinatorial QSAR Modeling of Specificity and Subtype Selectivity of Ligands Binding to Serotonin Receptors 5HT1E and 5HT1F
journal contribution
posted on 2008-05-27, 00:00 authored by Xiang
S. Wang, Hao Tang, Alexander Golbraikh, Alexander TropshaThe Quantitative Structure−Activity Relationship (QSAR)
approach has been applied to model binding affinity and receptor subtype
selectivity of human 5HT1E and 5HT1F receptor−ligands. The
experimental data were obtained from the PDSP Ki Database. Several
descriptor types and data-mining approaches have been used in the
context of combinatorial QSAR modeling. Data mining approaches included k Nearest Neighbor, Automated Lazy Learning (ALL), and PLS;
descriptor types included MolConnZ, MOE, DRAGON, Frequent Subgraphs
(FSG), and Molecular Hologram Fingerprints (MHFs). Highly predictive
QSAR models were generated for all three data sets (i.e., for ligands
of both receptor subtypes and for subtype selectivity), and different
individual techniques were proved best in each case. For real value
activity data available for 5HT1E and 5HT1F ligand binding, models
were characterized by leave-one-out cross-validated R2 (q2) for the training sets
and predictive R2 values for the test
sets. The best models for 5HT1E ligands were obtained with the kNN approach combined with MolConnZ descriptors (q2 = 0.69, R2 = 0.92);
for 5HT1F ligands ALL QSAR method using MolConnZ descriptors gave
the best results (R2 = 0.92). Rigorously
validated classification models were also developed for the 5HT1E/5HT1F
subtype selectivity data set with high correct classification accuracy
for both training (CCRtrain= 0.88) and test (CCRtest = 1.00) sets using kNN with MolConnZ descriptors.
The external predictive power of QSAR models was further validated
by virtual screening of The Scripps Research Institute (TSRI) screening
library to recover 5HT1E agonists and antagonists (not present in
the original PDSP data set) with high enrichment factors. The successful
development of externally predictive and interpretative QSAR models
affords further design and discovery of novel subtype specific GPCR
agents.
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Keywords
data mining approachesCombinatorial QSAR Modeling5 HT 1E ligandsMolConnZ descriptors5 HT 1F ligandsAutomated Lazy LearningsetR 2 valuesSeveral descriptor typesPDSP Ki Databasereceptor subtype selectivityMHFvalue activity dataQSAR modelsMOEFSG5 HT 1E agonistsScripps Research Institute5 HT 1E subtype selectivity datacombinatorial QSAR modelingPLSmodel binding affinity5 HT 1F ligand bindingk Nearest Neighbor5 HT 1EMolecular Hologram FingerprintsTSRIGPCRSerotonin Receptors 5 HT 1E
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