Biointormatics of Targeted Therapeutics And Applications in Drug Discovery

Abstract

The modern rational drug discovery process is based on the hypothesis that modulation of certain targets may exert therapeutic value and therapeutics directed at those targets are then developed. In this big data era, development of databases and methods to store, retrieve, organize and analyze the ever-increasing targeted therapeutics data are of importance to drug discovery. This work starts with the update of Therapeutic Target Database (TTD), which serves as a comprehensive source of therapeutics data, including targets, drugs, natural products and biomarkers. To facilitate the modern drug discovery, various bioinformatics methods to analyze the targeted therapeutics information from TTD have been developed. In particular, machine learning methods to predict multi-target agents, clustering method to analyze the drug distribution in target-specific chemical space, systematic analysis to learn multi-target mechanisms from the synergistic combinations of natural products and to utilize biomarkers for personalized targeted treatment will be discussed in this work.