Bioinformatic analysis can not only accelerate drug target identification and drug candidate
screening and refinement, but also facilitate characterization of side effects and predict drug resistance.
High-throughput data such as genomic, epigenetic, genome architecture, cistromic, transcriptomic,
proteomic, and ribosome profiling data have all made significant contribution to mechanismbased
drug discovery and drug repurposing. Accumulation of protein and RNA structures, as well as
development of homology modeling and protein structure simulation, coupled with large structure databases
of small molecules and metabolites, paved the way for more realistic protein-ligand docking
experiments and more informative virtual screening. I present the conceptual framework that drives
the collection of these high-throughput data, summarize the utility and potential of mining these data
in drug discovery, outline a few inherent limitations in data and software mining these data, point out
news ways to refine analysis of these diverse types of data, and highlight commonly used software
and databases relevant to drug discovery.