Two Class Approximation of TLB (Tomato Late Blight) Activity Data

토마토 역병균 항균 활성 데이터의 이분번 근사모델링

  • Hahn, Hoh-Gyu (Life Science Division, Korea Institute of Science and Technology) ;
  • M.D., Ashek Ali (Life Science Division, Korea Institute of Science and Technology) ;
  • Cho, Seung-Joo (Life Science Division, Korea Institute of Science and Technology)
  • Published : 2005.06.30

Abstract

Quantitative Structure Activity Relationship (QSAR) assumes the relatedness between physical property and biological activity. However, activity data measured at single concentration such as percent activity have not been used extensively for modeling purpose. This probably comes from the fact that these values are qualitative instead of quantitative. To utilize percent activity data for molecular modeling, we classified the whole data into two classes. One class represents the active while the other signifies the inactive. The percent activity data of ${\beta}$-Ketoacetoanilides measured for TLB (Tomato Late Blight) were investigated. CoMFA (Comparative Molecular Field Analysis) was used as a discriminant function. Using CoMFA provides 3D (three dimensional) information, which is crucial for chemical insight. It can also serve as a predictive model. The resultant model classified the given data correctly (98%). When LOO (leave-one-out) crossvalidation procedure was applied, the classification accuracy was 69%. Therefore two class approximation of percent activity data with CoMFA can be utilized to understand the relationship between chemical structure and biological activity and design subsequent chemical analogs.

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