COMPARISON OF CHEMICAL DESCRIPTORS FOR PROTEIN–CHEMICAL INTERACTION PREDICTION

Abstract

Predicting protein–chemical interaction has been an important and challenging task in the bioinformatics community, and there are many related applications in biomedical research, including QSAR modelling and novel lead discovery. A fundamental hypothesis for predicting protein–chemical interaction is that chemical compounds sharing chemical similarity should also share protein target profiles, and the critical question is hence how to measure the distance (or similarity) between two chemicals. An increasing number of chemical descriptors have been invented in the past decades. As chemical descriptors play a critical role in predicting protein– chemical interaction, it is of great importance to compare chemical descriptors and evaluate their performance in such predictions. In this paper, we reported our case study on comparing the performance of DRAGON descriptors, the frequent subgraph-based descriptors (FFSM), and the signature molecular descriptor on predicting protein–chemical interaction using support vector machines over a large number of data sets. Our experiments demonstrated that FFSM and signature descriptors outperformed most DRAGON descriptor classes, and wisely selecting chemical descriptors will be beneficial for predicting protein–chemical interaction.