Bioinformatics is the discipline at the intersection of biology,computer science,and mathematics.It has developed over the last 20 years by accumulation of researchers from those, and other, disciplines. However, in large part, there is no good definition of exactly what a bioinformatician does. If one were to ask 10 people in the field for a definition, one would get at least 10 different answers. It is also well known that there is a critical shortage of trained bioinformatics personnel, but it is less clear what training is needed. This is, in part, because of the rapid rate of change in the field, its underlying analyses, and its data. In this chapter, we will address those aspects of bioinformatics that contribute to drug discovery and drug development in the pharmaceutical and biotechnology industries.
Bioinformatics has developed from a number of academic disciplines, and this has provided a great deal of synergism. However, training in academia does not necessarily provide the background needed for a successful bioinformatics career in industry. This is due to a variety of factors, the chief one being the scale of analyses performed. It is a truism of biology that, until very recently, progress was made “one gene, one protein, one postdoc” at a time. However, in the analysis of human and other genomes for drug targets, there is not enough time or resources to take that approach. Instead, pharmaceutical bioinformaticians must work on hundreds to millions of protein or nucleic acids sequences simultaneously. This bulk-data-processing approach is usually not encountered academically other than in genome sequencing centers. Another difference is that all of the data generated need to be stored and converted into knowledge for experimentalist colleagues, so that they do not have to learn detailed algorithmic interpretation of bioinformatics output.
Bioinformatics can be thought of as consisting of two main interdisciplinary sub-fields: the research and development work required to build the software and database infrastructure and the computation-based research devoted to understanding and solving biological questions. The emphasis of this chapter will be on the application of bioinformatics tools and databases to identify and characterize novel gene targets, which is of interest to pharmaceutical companies. The classical drug discovery paradigm (Fig. 1) depends on the knowledge of functional activity of a protein. This functional knowledge is used either to isolate a target or develop a screen against the target. A pharmaceutical bioinformatician must design a system for converting bulk data and analyses into experimentalist-accessible knowledge. While we describe the current practice of bioinformatics in drug discovery, we also discuss knowledge generation.
