Peter Mulready is an independent consultant, who worked previously as a system architect at Boehringer Ingelheim, one of the world’s largest pharmaceutical companies. Peter got his degree in biology, but his focus shifted to managing and optimizing the use of data in drug discovery research. Specifically, he lead the information technology team responsible for managing and analyzing the data generated during the high-throughput compound testing process. This process required testing up to one million compounds against drug targets (e.g. soluble enzymes, membrane-bound receptors) involved in immunological, inflammatory, or viral disease states. The follow up step to this testing was the identification of core chemical structures that could potentially mitigate the specific disease state.
Although there are many software tools utilized in the drug discovery process, most have some limitations or gaps in their capability. Peter was attracted to Statistics.com because of the deep curriculum in the R language. R seemed like an ideal tool to cover the limitations of these tools because of its breadth and depth of statistical and graphic capability.