Optimization of the SVM screening kernel-application to hit definition in compound screening

This study applies the Support Vector Machine (SVM) algorithm to the problem of chemical compound screening with a desired activity and hit definition. The problem of automatically tuning multiple parameters for pattern recognition SVMs using our new introduced kernel for chemical compounds is considered. This is done by a simple eigen analysis method which is applied to the matrix of the same dimension as the kernel matrix to find the structure of feature data, and to find the kernel parameter accordingly. We characterize distribution of data by the principle component analysis method.