The UKRI/BBSRC AI for Drug Discovery programme is hosting a Summer Symposium on 28 June, with the aim of introducing attendees to the AI for Drug Discovery Landscape and to facilitate networking between students, supervisors and industry partners.
The event will take place at the Royal Pharmaceutical Society.
The Symposium will open with a keynote talk from Professor Andreas Bender, University of Cambridge and PangeAI Botanica, and will include talks from our current cohort of AI for Drug Discovery Students, followed by a networking lunch. The event is open to our current staff, students, and industry partners as well as those parties interested in joining the programme, and those who are currently working in complementary areas and are interested in AI for Drug Discovery.
Further details, including how to register your attendance can be found here: https://forms.office.com/e/Se5hPwb3mt
Keynote Speaker Bio
Professor Andreas Bender is a Professor for Life Science Informatics, University of Cambridge and Chief Technology & Informatics Officer, PangeAI Botanica. Prof. Bender's research focuses on developing new life science data analysis methods (AI/ML/data science) and their application in drug discovery, chemical biology, and in silico drug safety.
Talk Abstract:
The amount of chemical and biological data available has increased in the public as well as the private domain, and both on the algorithmic and hardware side progress has been tremendous in machine learning. Press releases describe the design of functional proteins and antibodies from scratch, and several ‘first AI-designed drugs’ have already entered clinical phases. However, all is not well when it comes to the marriage of algorithms with drug discovery, in particular when it comes to the in vivo relevance of what we are able to do with chemical and biological data at this point in time. Reasons for this are that the field is still stuck in reductionist thinking, in combination with a lack of relevant data (and our ability to handle it computationally) and the formation of too many, too narrow specialist domains (among other reasons).
This contribution will point out several areas, from data to algorithms to human mindset, that need changing to benefit fully from available compute power when it comes to in vivo relevant decision making in drug discovery in the future.
Further Reading:
Bender A, Cortés-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet. Drug Discov Today. 2021 Feb;26(2):511-524. doi: 10.1016/j.drudis.2020.12.009.
Bender A, Cortes-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data. Drug Discov Today. 2021 Apr;26(4):1040-1052. doi: 10.1016/j.drudis.2020.11.037.
Emma GrantDigital Environment Research Institute (DERI) Manager | Alan Turing University Liaison Manager
Digital Environment Research Institute | Queen Mary University London
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