Algorithms for ligand based virtual screening in drug discovery

Abstract

Machine learning can play a very important role in various crucial applications like data mining and pattern recognition. Machine learning techniques have been widely used in drug discovery and development, particularly in the areas of chemo-informatics, bioinformatics and other types of pharmaceutical research. It has been demonstrated that they are suitable for large high dimensional data, and the models built with these methods can be used for robust external predictions. This paper discusses on how the machine learning techniques, especially Support Vector Machines, are going to be applied on the data sets with the help of graph kernels. These graph kernels are used to compare substructures of graphs that are computable in polynomial time.