Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics

Chapter 23

Protein-Related Drug Activity Comparison Using Support Vector Machines

WEI ZHONG and JIEYUE HE

23.1 Introduction

At present, combinatorial chemistry can produce millions of new molecules at a time. However, this high level of production cannot exhaust the trillions of potential combinations within a few thousand years. The quantitative structure–activity relationship (QSAR) analysis is required to restrict the search space to avoid produce and test every possible molecular combination. QSAR analysis is very important for understanding the correlation between the molecule's activities and structure. Intelligent machine learning techniques are important tools for QSAR analysis. As a result, these techniques are integrated into the drug production process. The effective intelligent computational model can reduce the cost of drug design significantly by producing the sublibrary of molecular combination derived from a much larger library. This survey compares the performance of several popular machine learning technique for drug activity. The machine learning techniques introduced in this chapter are used to predict activity of pyrimidines and triazines based on the structure–activity relationship of these compounds. Pyrimidines and triazines are two important inhibitors of Escherichia coli dihydrofolate reductase (DHFR). Analysis and prediction of activities of these two inhibitors is very important for finding potential treatment agents for malaria, bacterial infection, ...