title: Comparative Analysis of QSAR-based vs. Chemical Similarity Based Predictors of GPCRs Binding Affinity.
abstract: Ligand based virtual screening (LBVS) approaches could be broadly divided into those relying on chemical similarity searches and those employing Quantitative Structure-Activity Relationship (QSAR) models. We have compared the predictive power of these approaches using some datasets of compounds tested against several G-Protein Coupled Receptors (GPCRs). The k-Nearest Neighbors (kNN) QSAR models were built for known ligands of each GPCR target independently, with a fraction of tested ligands for each target set aside as a validation set. The prediction accuracies of QSAR models for making active/inactive calls for compounds in both training and validation sets were compared to those achieved by the Prediction of Activity Spectra for Substances' (PASS) and the Similarity Ensemble Approach (SEA) tools both available online. Models developed with the kNN QSAR method showed the highest predictive power for almost all tested GPCR datasets. The PASS software, which incorporates multiple end-point specific QSAR models demonstrated a moderate predictive power, while SEA, a chemical similarity based approach, had the lowest prediction power. Our studies suggest that when sufficient amount of data is available to develop and rigorously validate QSAR models such models should be chosen as the preferred virtual screening tool in ligand-based computational drug discovery as compared to chemical similarity based approaches.
authors: Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, CB #7360, Beard Hall, Chapel Hill, NC 27599-7360 USA phone: 919-966-2955; fax: 919-966-0204.
authors: Man
authors: M
authors: Luo
authors: Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, District of Columbia 20059.
authors: Xiang S
authors: XS
authors: Wang
authors: Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, CB #7360, Beard Hall, Chapel Hill, NC 27599-7360 USA phone: 919-966-2955; fax: 919-966-0204. alex_tropsha@unc.edu.
authors: Alexander
authors: A
authors: Tropsha
mesh_terms: D000465:Algorithms; D001667:Binding, Competitive; D020650:Combinatorial Chemistry Techniques / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D055808:Drug Discovery / Q000379:methods; D008024:Ligands; D021281:Quantitative Structure-Activity Relationship; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry* / Q000378:metabolism; D015203:Reproducibility of Results; D054852:Small Molecule Libraries / Q000737:chemistry / Q000378:metabolism
chemical_list: D008024:Ligands; D043562:Receptors, G-Protein-Coupled; D054852:Small Molecule Libraries
keywords: GPCRs; Model validation; PASS; QSAR modeling; SEA