SAR Trek

Typical therapeutic research projects continually generate and amass data – chemical structures; sequences; formulations; primary, secondary, and high-content assay results; observed and predicted physicochemical properties; DMPK study results; instrument data; sample genealogy and provenance; progress reports, etc. – and researchers are then charged with the responsibility of making sense of all the data, to advance and explore hypotheses, deduce insights, and decide which compounds and entities to pursue, which formulations or growth conditions to optimize, and which to drop or shelve.

A usual first step will be to collect together all the relevant data and get it into a form that is amenable to further searching and refinement: but this poses a potentially challenging set of questions – what data exists, where is it, what format is it in, and how much of it is there? Answering these questions may then be complicated if the data resides in different, possibly disconnected, potentially legacy systems: e.g. chemical structures in an aging corporate registry, sequences in a newer system, assay results in another database, DMPK values buried inside an electronic lab notebook, and instrument data in an unconnected LIMS or LES.

So the researcher is faced with knowing:

• Which systems exist, where they are located, and what they contain,
• How to search each of them to find the required data,
• How to extract the desired information from each source in the correct usable format, and
• How to meld or mash-up these various disparate data sets to generate a project corpus for further analysis and refinement.