In this article written by Scannell and Bosley, the contrast between increased costs for drug discovery and the decreased success rate is analyzed using a quantitative decision-theoretic model of the R&D model (quite complex :). In a nutshell, results of this analysis indicate that, “when searching for rare positives (e.g., candidates that will successfully complete clinical development), changes in the predictive validity of screening and disease models that many people working in drug discovery would regard as small and/or unknowable (i.e., an 0.1 absolute change in correlation coefficient between model output and clinical outcomes in man) can offset large (e.g., 10 fold, even 100 fold) changes in models’ brute-force efficiency”. The authors note that here has also been too much enthusiasm for reductionist molecular models which have insufficient predictive validity and that the “reproducibility crisis” could reflect the abandonment of models with high predictive value for reasons of exhaustion and/or scientific fashion. These ideas are very close to what we have discussed with our ECNP Preclinical Data Forum colleagues as it may especially be applicable to the field of neuroscience where clinical failures caused companies to abandon many reasonably validated areas of research (including targets and models) in favor much less validated but bearing the label „novel“. (DOI:10.1371/journal.pone.0147215)