Drug research and development (R&D) is a comprehensive, expensive, and time-consuming enterprise, full of risk throughout the process [1]. In general, the pipeline for drug discovery is composed of three major steps: drug target identification and validation, lead compound discovery and optimization, and preclinical research (Figure 20.1). In the last decades, a lot of new technologies have been developed and applied in drug R&D to shorten the research cycle and reduce the expenses. Among them, computational approaches have revolutionized the pipeline of the discovery and development [2]. In the past forty years, computational technologies for drug R&D have been evolving very quickly, especially in recent decades with the unprecedented development of biology, biomedicine, and computer capabilities. In the postgenomic era, because of the dramatic increase of small molecule and biomacromolecule information, computational tools have been applied in almost every stage of drug R&D, greatly changing the strategy and pipeline for drug discovery [2]. Computational approaches span almost all stages in the discovery and development pipeline, from target identification to lead discovery and from lead optimization to preclinical or clinical trials (Figure 20.1).

As shown in Figure 20.1, the target identification and validation are the first two key stages in the drug discovery pipeline. By 2000, only about 500 drug targets had been reported [3, 4]. The completion of human genome project and numerous pathogen genomes unveiled that there are thirty thousand to forty thousand genes and at least the same number of proteins, and many of these proteins are potential targets for drug discovery. However, it is still a challenging task to identify and validate those druggable targets from thousands of candidate macromolecules [5].