title: Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR).
abstract: This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research.
authors.affiliation: NIDA Center of Excellence for Computational Chemogenomics Drug Abuse Research, Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, USA, kyawzeyarmyint@gmail.com.
authors.forename: Kyaw Z
authors.initials: KZ
authors.lastname: Myint
authors.forename: Xiang-Qun
authors.initials: XQ
authors.lastname: Xie
mesh_terms: D000465:Algorithms; D002186:Cannabinoids / Q000737:chemistry* / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods; D062126:Databases, Chemical; D008024:Ligands; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship; D043882:Receptors, Cannabinoid / Q000378:metabolism
chemical_list: D002186:Cannabinoids; D008024:Ligands; D043882:Receptors, Cannabinoid