https://community.jmp.com/t5/United-Kingdom-JMP-Users-Group/Success-Through-Statistics-in-High-Throughput-Drug-Discovery/m-p/41980#M28 <P>Success Through Statistics in High Throughput Drug Discovery - Graeme Robb - AstraZeneca</P><P>[This talk was presented at the UK JMP Usergroup meeting on the 13th of July 2017]</P><P>&nbsp;</P><P>In 1980s and 90s, drug discovery companies massively invested in High Throughput Screening (HTS) technologies, allowing millions of compounds to be rapidly screened.&nbsp; This has proven to be a naive approach.&nbsp; Bigger, it seems, is not better and the investment in HTS has not delivered a higher rate of drug approvals in the intervening years.&nbsp; What we now realise is that quantity is perhaps less important than quality.&nbsp; The 'predictive validity' of an assay suitable for HTS is necessarily compromised and may not directly relate to the disease phenotype of interest.&nbsp; High false-positive rates and noisy make it difficult to identify the true positives.&nbsp; Understanding our data and using statistical techniques help to improve our ability to recognise false positives.&nbsp; Statistical learning from past HTS campaigns is used to enhance the results and further spot systematic false positives.&nbsp; In the future, we will face further challenges of scale and predictive validity where statistics, modelling and machine learning can all be used to our advantage.</P><P>&nbsp;</P> Fri, 14 Jul 2017 10:08:32 GMT graeme_robb 2017-07-14T10:08:32Z https://community.jmp.com/t5/United-Kingdom-JMP-Users-Group/Success-Through-Statistics-in-High-Throughput-Drug-Discovery/m-p/41980#M28 <P>Success Through Statistics in High Throughput Drug Discovery - Graeme Robb - AstraZeneca</P><P>[This talk was presented at the UK JMP Usergroup meeting on the 13th of July 2017]</P><P>&nbsp;</P><P>In 1980s and 90s, drug discovery companies massively invested in High Throughput Screening (HTS) technologies, allowing millions of compounds to be rapidly screened.&nbsp; This has proven to be a naive approach.&nbsp; Bigger, it seems, is not better and the investment in HTS has not delivered a higher rate of drug approvals in the intervening years.&nbsp; What we now realise is that quantity is perhaps less important than quality.&nbsp; The 'predictive validity' of an assay suitable for HTS is necessarily compromised and may not directly relate to the disease phenotype of interest.&nbsp; High false-positive rates and noisy make it difficult to identify the true positives.&nbsp; Understanding our data and using statistical techniques help to improve our ability to recognise false positives.&nbsp; Statistical learning from past HTS campaigns is used to enhance the results and further spot systematic false positives.&nbsp; In the future, we will face further challenges of scale and predictive validity where statistics, modelling and machine learning can all be used to our advantage.</P><P>&nbsp;</P> Fri, 14 Jul 2017 10:08:32 GMT https://community.jmp.com/t5/United-Kingdom-JMP-Users-Group/Success-Through-Statistics-in-High-Throughput-Drug-Discovery/m-p/41980#M28 graeme_robb 2017-07-14T10:08:32Z