Identification of the Dual Action Antihypertensive Drugs Using TFS-Based Support Vector Machines

• DOI: 10.1273/cbij.9.41
• Publication Date: 2009-07-31T05:57:30Z
• Authors: Kentaro Kawai; Yoshimasa Takahashi
• Keywords: Identification

Recently, many concerns are paid for dual action drugs such as ACE/NEP inhibitors which have two different biological activities. To identify multiple active by supervised learning approach, a multi-label classification technique is required. In the present work, we investigated of antihypertensive including using support vector machines (SVMs). Biological activity data were taken from MDDR database and they employed computational trial training SVM classifiers. Structural feature representation each drug molecule was based on topological fragment spectra (TFS) method. The obtained classifiers tested finding inhibitors. result suggests that TFS-based useful