In the run up to the 2nd Annual AI in Drug Development Congress; we sat down with some of the speakers to gain their insights into this exciting industry: from their research priorities to the challenges they face.
In the first of this three-piece series, you’ll hear from five of the speakers across academia, major pharma and biotech on the future of AI, as well as case studies on applications of machine learning for oncology. We hope you find them useful and enjoyable!
THE FUTURE OF AI IN DRUG DISCOVERY
AI is set to revolutionise drug discovery, with machine learning approaches able to process data efficiently and select likely-successful targets. With so many potential applications, there is significant discussion about how best to integrate AI into the drug design toolbox and how it could be utilised in the future.
Click below to learn what has most excited senior experts from Merck, e-Therapeutics and the Science & Technology Facilities Council in this burgeoning field.
Read more here: https://bit.ly/2u3qDds
USING MACHINE LEARNING FOR ONCOLOGY PATIENTS
With oncology remaining one of the most substantially researched and funded therapeutic areas in drug development, many leaders in pharma and academia alike are dedicating their machine learning efforts to improve outcomes from oncology patients.
Benefit from both industry and academic case studies through our conversations with Dennis Wang, Lecturer at University of Sheffield and James Black, Data Scientist at Roche, on their own research efforts in this space.
Read more here: https://bit.ly/2KFyvMt
PHARMATEC EVENT SERIES: 3 EVENTS IN 1
This event is part of the Pharmatec Series of events, including our flagship 16th Annual Pharma IT Congress and fast-emerging Digital Health & Digital Technologies Congress. Bringing together over 300 experts, the Series provides a focused platform for knowledge-exchange and discussion.
Click here for further information: https://bit.ly/2L0fKTm
