title: Alignment-independent technique for 3D QSAR analysis.
abstract: Molecular biochemistry is controlled by 3D phenomena but structure-activity models based on 3D descriptors are infrequently used for large data sets because of the computational overhead for determining molecular conformations. A diverse dataset of 146 androgen receptor binders was used to investigate how different methods for defining molecular conformations affect the performance of 3D-quantitative spectral data activity relationship models. Molecular conformations tested: (1) global minimum of molecules' potential energy surface; (2) alignment-to-templates using equal electronic and steric force field contributions; (3) alignment using contributions "Best-for-Each" template; (4) non-energy optimized, non-aligned (2D > 3D). Aggregate predictions from models were compared. Highest average coefficients of determination ranged from R Test (2) = 0.56 to 0.61. The best model using 2D > 3D (imported directly from ChemSpider) produced R Test (2) = 0.61. It was superior to energy-minimized and conformation-aligned models and was achieved in only 3-7 % of the time required using the other conformation strategies. Predictions averaged from models built on different conformations achieved a consensus R Test (2) = 0.65. The best 2D > 3D model was analyzed for underlying structure-activity relationships. For the compound strongest binding to the androgen receptor, 10 substructural features contributing to binding were flagged. Utility of 2D > 3D was compared for two other activity endpoints, each modeling a medium sized data set. Results suggested that large scale, accurate predictions using 2D > 3D SDAR descriptors may be produced for interactions involving endocrine system nuclear receptors and other data sets in which strongest activities are produced by fairly inflexible substrates.
authors.affiliation: Division of Systems Biology, National Center for Toxicological Research, 3900 NCTR Road, Jefferson, AR, 72079, USA. jon.wilkes@fda.hhs.gov.
authors.forename: Jon G
authors.initials: JG
authors.lastname: Wilkes
authors.affiliation: Division of Systems Biology, National Center for Toxicological Research, 3900 NCTR Road, Jefferson, AR, 72079, USA.
authors.forename: Iva B
authors.initials: IB
authors.lastname: Stoyanova-Slavova
authors.affiliation: Division of Systems Biology, National Center for Toxicological Research, 3900 NCTR Road, Jefferson, AR, 72079, USA.
authors.forename: Dan A
authors.initials: DA
authors.lastname: Buzatu
mesh_terms: D059002:Androgen Receptor Antagonists / Q000737:chemistry*; D003198:Computer Simulation; D004703:Endocrine System / Q000187:drug effects* / Q000473:pathology; D006801:Humans; D008958:Models, Molecular; D011485:Protein Binding; D011487:Protein Conformation; D021281:Quantitative Structure-Activity Relationship; D011944:Receptors, Androgen / Q000737:chemistry* / Q000378:metabolism
chemical_list: D059002:Androgen Receptor Antagonists; D011944:Receptors, Androgen
keywords: 3D modeling; Molecular conformation; Quantitative structure–activity relationship; Spectral data-activity relationship