Improved statistical methods for hit selection in high-throughput screening

High-throughput screening (HTS) plays a central role in modem drug discovery, allowing the rapid screening of large compound collections against a variety of putative drug targets. HTS is an industrial-scale process, relying on sophisticated automation, control, and state-of-the art detection technologies to organize, test, and measure hundreds of thousands to millions of compounds in nano- to microliter volumes. Despite this high technology, hit selection for HTS is still typically done using simple data analysis and basic statistical methods. The authors discuss in this article some shortcomings of these methods and present alternatives based on modem methods of statistical data analysis. Most important, they describe and show numerous real examples from the biologist-friendly StatServer((R)) HTS application (SHS), a custom-developed software tool built on the commercially available S-PLUS(R) and StatServer((R)) statistical analysis and server software. This system remotely processes HTS data using powerful and sophisticated statistical methodology but insulates users from the technical details by outputting results in a variety of readily interpretable graphs and tables.