Alignment-independent technique for 3D QSAR analysis

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 RTest2 = 0.56 to 0.61. The best model using 2D > 3D (imported directly from ChemSpider) produced RTest2 = 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 RTest2 = 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.