From big data to complex network: a navigation through the maze of drug–target interaction

Abstract

Investigation of drug–target interaction is important in drug discovery and development. Increasing evidence has shown drug–target interaction emerges from a holistic network of drug molecules and their complete targets, rather than the traditional one-bullet-one-target model. In this chapter, we first reviewed the important data sources for the construction and prediction of drug–target interactions, including drug screening, active ingredient profiling, target information mining. Then, similarity-based and machine learning-based algorithms in the construction, prediction, and analysis of drug–target interactions, as well as some important computational tools and methods in network construction and analysis were introduced. Finally, we concluded this chapter with some perspectives on future directions in both database and network algorithms. In summary, as a paradigm shift, integrating big data and complex network holds the promise to deepen our understanding of the ever-expanding universe of drug molecules, targets, and their interactions.

Min Li

BSc in Bioinformatics

My research interests include Network Analysis, Molecular Docking, Multi-omics Data Analysis and Tool Development.