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June 16 @ 12:00 - 13:30
Artificial Intelligence in Drug Discovery
Speaker: Andreas Bender, Professor for Molecular Informatics at Cambridge University – Chief Technology & Informatics Officer at PangeAI, part of Pangea Botanica – London/UK and Berlin/Germany.
Abstract: Artificial Intelligence in Drug Discovery: We Need to Appreciate the Characteristics of Chemical and Biological Data to Really Make Progress
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.
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.