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

Abstract

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