QSAR and classification models of a novel series of COX-2 selective inhibitors: 1,5-diarylimidazoles based on support vector machines

J Comput Aided Mol Des. 2004 Jun;18(6):389-99. doi: 10.1007/s10822-004-2722-1.


The support vector machine, which is a novel algorithm from the machine learning community, was used to develop quantitation and classification models which can be used as a potential screening mechanism for a novel series of COX-2 selective inhibitors. Each compound was represented by calculated structural descriptors that encode constitutional, topological, geometrical, electrostatic, and quantum-chemical features. The heuristic method was then used to search the descriptor space and select the descriptors responsible for activity. Quantitative modelling results in a nonlinear, seven-descriptor model based on SVMs with root mean-square errors of 0.107 and 0.136 for training and prediction sets, respectively. The best classification results are found using SVMs: the accuracy for training and test sets is 91.2% and 88.2%, respectively. This paper proposes a new and effective method for drug design and screening.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Artificial Intelligence
  • Cell Line
  • Computer Simulation
  • Cyclooxygenase 2
  • Cyclooxygenase 2 Inhibitors
  • Cyclooxygenase Inhibitors / chemistry*
  • Cyclooxygenase Inhibitors / classification
  • Cyclooxygenase Inhibitors / pharmacology*
  • Drug Design
  • Humans
  • Membrane Proteins
  • Nonlinear Dynamics
  • Prostaglandin-Endoperoxide Synthases / drug effects*
  • Quantitative Structure-Activity Relationship


  • Cyclooxygenase 2 Inhibitors
  • Cyclooxygenase Inhibitors
  • Membrane Proteins
  • Cyclooxygenase 2
  • PTGS2 protein, human
  • Prostaglandin-Endoperoxide Synthases