An interesting publication in the latest issue of Journal of Cheminformatics in which Melvin Yu uses Monte Carlo simulations to look at the Drug Discovery pipeline DOI. With any analysis of this kind it is easy to argue that it is too simplistic however it does raise some useful discussion points. In particular, the idea that simply attempting to improve drug discovery productivity by simply increasing the size of existing working groups may not necessarily be the best solution.

This also sounds familiar

Simulations also predict that the frequency of compounds to successfully pass the candidate selection milestone as a function of time will be irregular, with projects entering preclinical development in clusters marked by periods of low apparent productivity

Perhaps a greater number of independent smaller research units is a more attractive model?