Enabling high-throughput discovery.

Abstract

During the past few years, the introduction of ultra-high-throughput screening and new assay design and detection technologies has exponentially increased the amount and complexity of screening data. Effective use of this data implies a process that begins with assay design. An effective data management system should control a range of processes, from the initial selection of compounds and storage and mining of the assay result to more complex tasks, such as extracting patterns from these data. Remarkable advances have been made during the last year to increase efficiency at different phases of the screening, shifting the bottleneck of this process to data analysis. The challenge facing drug discovery today is to extract knowledge from these data. Knowledge discovery is defined as 'the non-trivial extraction of implicit, unknown, and potentially useful information from data'. A large amount of research is being devoted to optimize the extraction of knowledge from screening data. In this review, we discuss the screening process and its progress during the last year. Some of the challenges for the future, such as optimization of the knowledge discovery process and the sharing of data across an organization, will also be presented.