A Brief Guide To Assay Technology For Efficient Drug Discovery -- Part 1

Those new to screening and lead discovery may struggle to know where to start, while  veterans can always use a refresher course. In this review, which will appear in four installments, I will share my approach to staying current with trends in discovery assay technology. Assay ontology resources geared toward assay information management are the place to start (as Part I of this series).

By far the most broadly recognized compendium is the Assay Guidance Manual, a continuously updated eBook. It came to life as a Lilly effort to compile the tribal knowledge within its therapeutic project team silos, then rapidly evolved to provide guidelines for measurements of new and known molecular entities written by consensus among scientists in academic, non-profit, government and industrial research laboratories. NIH/NCATS has also been a motive force here. The full spectrum of applications to support SAR is covered, whether target or phenotype driven. These include  biochemical, functional and, now more so, all the cell imaging modalities, including microscopy, that integrate into machine learning (AI) assisted discovery aids. The 2017 "what's new" list, for example, handily illustrates the scope and breadth of authorship in this indispensable publication.

For an encyclopedic but, less editorially guided view of assay diversity both the and 's BioAssay databases are public repositories that cover 500,000 plus assay protocols searchable by concept, target, or molecular structure of expected drug-like structures. The features of PubChem BioAssay appear to dominate interest, as (O/A) in the SLAS journal. Both resources do a creditable job of breaking out assays into logical classification trees, although the PubChem BioAssay architecture offers a more succinct information about assay types. This latter task is also accomplished in the and, even more thoroughly, in the (BAO) and  (BARD) portals.