title: Classification of dopamine antagonists using TFS-based artificial neural network.
abstract: In the former work, the authors proposed the Topological Fragment Spectral (TFS) method as a tool for the description of the topological structure profile of a molecule. This paper describes the TFS-based artificial neural network (TFS/ANN) approach for the classification and the prediction of pharmacological active classes of chemicals. Dopamine antagonists of 1227 that interact with different types of receptors (D1, D2, D3, and D4) were used for the training. The TFS/ANN successfully classified 89% of the drugs into their own active classes. Then, the trained model was used for predicting the class of unknown compounds. For the prediction set of 137 drugs that were not included in the training set, the TFS/ANN model predicted 111 (81%) drugs of them into their own active classes correctly.
authors.affiliation: Laboratory for Molecular Information Systems, Department of Knowledge-Based Information Engineering, Toyohashi University of Technology, Hibarigaoka 1-1, Tempaku-cho, Toyohashi 441-8580, Japan.
authors.forename: Satoshi
authors.initials: S
authors.lastname: Fujishima
authors.forename: Yoshimasa
authors.initials: Y
authors.lastname: Takahashi
mesh_terms: D018492:Dopamine Antagonists / Q000145:classification*; D016571:Neural Networks, Computer
chemical_list: D018492:Dopamine Antagonists