From targets to leads: the importance of advanced data analysis for decision support in drug discovery

Lead discovery is a complex process that is intimately linked to chemistry, but which is also increasingly driven by biological sciences. In an industrial pharmaceutical research environment the process is defined by highly automated technologies for target identification and validation, compound library screening, and compound efficacy assessment. The huge volumes and complex dependencies of data produced by such large-scale experiments have led to a reassessment of data analysis processes, resulting in the development of novel data analysis strategies tailored to drug discovery. In this review, recent progress in data-driven research applications is reported, focusing on the use and processing of transcriptomics, proteomics and high-throughput screening data. The successful application of specialized data analysis procedures in many companies is discussed, which has resulted in significant improvements in decision-making processes for progressing therapeutic targets to promising leads.