02051nas a2200301   4500000000100000000000100001008004100002260001200043653002100055653003500076653003000111653002500141653002200166100002100188700001700209700002000226700002100246700001900267700002600286700002000312700002100332245009200353856005500445300001200500490000700512520121600519022001401735       2022                            d  c2022-1210aDrug development10aExperimental models of disease10aHigh-throughput screening10aPreclinical research10aTarget validation1 aJack W. Scannell1 aJames Bosley1 aJohn A. Hickman1 aGerard R. Dawson1 aHubert Truebel1 aGuilherme S. Ferreira1 aDuncan Richards1 aJ. Mark Treherne00aPredictive validity in drug discovery: what it is, why it matters and how to improve it  uhttps://www.nature.com/articles/s41573-022-00552-x  a915-9310 v213 aSuccessful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which ‘predictive validity’ is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation.  a1474-1784