Identification of Dopamine D1 Receptor Agonists and Antagonists under Existing Noise Compounds by TFS-based ANN and SVM

Yoshimasa TAKAHASHIa*, Satoshi FUJISHIMAa, Katsumi NISHIKOORIa, Hiroaki KATOa and Takashi OKADAb

aDepartment of Knowledge-based Information Engineering, Toyohashi University of Technology
1-1 Hibarigaoka, Tempaku-cho, Toyohashi 441-8580, Japan
bDepartment of Informatics, School of Science and Technology, Kwansei Gakuin University,
2-1 Gakuen Sanda 669-1337, Japan

(Received: November 15, 2004; Accepted for publication: February 10, 2005; Published on Web: April 1, 2005)

This paper describes classification and prediction for pharmacologically active classes of drugs under the presence of noise chemical compounds. Dopamine D1 receptor agonists (63 compounds), antagonists (169 compounds) and other drugs (696 compounds) were used for the work. Each drug molecule was characterized with Topological Fragment Spectra (TFS) reported by the authors. TFS-based artificial neural network (TFS/ANN) and support vector machine (TFS/SVM) were employed and evaluated for their classification and prediction abilities. It was concluded that the TFS/SVM works better than TFS/ANN in both the training and the prediction.

Keywords: Pattern classification, Topological fragment spectra, ANN, SVM, Dopamine agonists, Dopamine antagonists

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