With the ever increasing volume of data available to scientists in drug discovery and development, the opportunity to leverage an increasing amount of these data in the assessment of drug safety is clear. The challenge in an environment of increasing data volume is in the structuring and the analysis of these data, such that decisions can be made without excluding information or overstating their meaning. Informatics and modelling play a crucial role in addressing this challenge in two basic ways: a) the data are structured and analysed in a transparent and objective way; and b) new experiments are designed with the model as part of the design process, much like modern experimental physics. Enhancing the use and impact of informatics and modelling on drug discovery is not simply a matter of increasing processor speed and memory capacity. The transformation of raw data to usable, and useful, information is a scientific, technical and, perhaps most importantly, cultural challenge within drug discovery. This review will highlight some of the history, current approaches and promising future directions in this rapidly expanding area.
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