AI is Changing the Drug Discovery Paradigm
Drug discovery is a challenging business, despite huge societal and commercial benefits in the discovery of new drugs it is incredibly challenging to develop discover and develop new therapies, with typically around 30 new drugs developed per year from the entire worldwide pharma and biotech R&D budget. The reasons for this are complex, but the bottom line is that the vast majority of started projects do not successfully finish, there is huge attrition from the idea of a scientist through discovery and clinical development stages. We are developing powerful real world evidence-based artificial intelligence solutions to address drug discovery. Key to recent progress is the availability of large quantities of data, high performance computing and developments in deep-learning approaches to mine for hypotheses that can be rationally scored and prioritised for success.
John studied Chemistry at Bath, graduating in 1987. He then studied for a PhD at Birkbeck College, on protein modelling, followed by a postdoc at ICRF (now CRUK). John then joined Pfizer, eventually leading a multidisciplinary group combining rational drug design, informatics and structural biology. In 2000 he moved to a start-up biotech company, Inpharmatica, where he developed the drug discovery database StARLite. In 2008 John moved to the EMBL-EBI, where the successor resource is known as ChEMBL. Most recently John joined Benevolent.ai, where he continues his research as director of bioinformatics. In this role, John is involved in integrating deep learning and other AI approaches to drug target validation and drug optimisation