Development of QSAR methods with protein information

Abstract

QSAR (Quantitative Structure Activity Relationships) is a method for building models to predict activity of compounds and a protein by using statistic methods. When we build a QSAR model, we use only information of compounds, not proteins. Recently, new QSAR methods using not only compound information but also protein information have been researched. These methods are considered to have some merits of good prediction power and so on. One of these methods is Support Vector Machine-Target Ligand Kernel (SVR-TLK), which use product of ligand kernel and protein kernel as a kernel of SVR. In this study, we used SVR-TLK to activity data set of Dopamine type 2 and type 3. Descriptors of compounds are constitutional descriptors and topological descriptors and so on, and descriptors of proteins are frequency of a pair of 20 amino acids. The SVR-TLK model shows better Q2 value than normal QSAR models. Therefore, usefulness of SVR-TLK has been proved.