High-throughput electronic biology: mining information for drug discovery

In the past 10 years, the life sciences have seen a proliferation of electronic data, emerging from systems such as databases, text-mining technologies, high-throughput techniques and 'omics' platforms (for example, DNA microarray).In this article, we review some of the recent developments in the field of electronic biology (eBiology), which uses these resources as a substrate for new drug discovery. As extensive reviews on data sets and tools already exist, we highlight how these resources can be applied directly to solve bottlenecks in the industry.A number of approaches to the application of eBiology in drug discovery are identified, ranging from deep 'systems biology' to 'project-specific' analyses and focus on high-throughput techniques.A set of examples are given, which look at the power of applying multiple resources simultaneously to build layers of evidence and end with the identification of novel drug targets. In these scenarios, the expert in that disease area is a key partner, without which the exercise is unlikely to succeed.Although there are an increasing number of examples of target mining in the literature, there is also a need to consider how one then translates these biological hypotheses into drug discovery programmes. To this end, we consider the data sources and techniques that can be used to apply a business focus to the results of this mining.As hypotheses turn into real programmes, we consider further workflows to support these later stages of discovery, looking at issues such as druggability, selectivity and understanding the action of a compound both in vitro and in vivo. Although these areas are traditionally addressed by computational chemists, there is much to be gained by the use of techniques and resources familiar to eBiologists.Last, we discuss the future needs in eBiology and how the area must be primarily led through scientific creativity, rather than technical considerations.