5th Machine Learning and AI in Bio(Chemical) Engineering Conference
Hosted by the University of Cambridge
27-28th June 2022
Event booklet available!
Machine Learning and AI in Bio(Chemical) Engineering Conference series is co-organised by research groups of the Universities of Cambridge, Leeds, Glasgow, Southampton, and University College London. This series of conferences grew from two collaborative research projects on robotics and automation in chemical development. The conference will feature keynote and invited lectures from global champions of research in ML/AI in chemistry/(bio)chemical engineering, and regular lectures selected from the community submissions.
The 2022 edition of the conference will be run as a hybrid event. The in-person attendance will be limited to 85 participants. The online event is unlimited; recordings of the sessions will be made available to the registered participants. A specific online poster and networking session will be run for the virtual participants.
Conference Agenda
June 27th, day one
10:00 - 11:00 Welcome and refreshments
11:00 - 12:00 Keynote 1 – Connor Coley - AI for chemical space navigation and synthesis
12:00 - 12:25 Adarsh Arun – Reaction impurity prediction using a data mining approach
12:25 - 12:50 A. Kondinski – Automated rational design via knowledge engineering
12:50 - 14:00 Lunch
14:00 - 14:35 Ruben Sanchez-Garcia - Compound availability and the numbers we care about in computationally-driven drug discovery
14:35 - 15:00 Harry Kay - Developing a novel soft-sensing framework for industrial data analysis and batch process monitoring
15:00 - 15:25 Calvin Tsay - SnAKe: Bayesian Optimization with Pathwise Exploration
15:25 - 15:35 Break
15:35 - 16:10 François-Xavier Felpin - Autonomous Flow reactors Associating In-line/Online Analyses and Feedback Algorithms
16:10 - 16:35 Ioana Gherman - Accelerating whole cell modelling with machine learning
16:35 - 17:00 Haiting Wang – A Hybrid Modelling Framework for Bioprocess
17:00 - 17:25 Pierre-Aurelien Gillot - Systemic comparison of neural network architectures for protein expression prediction in bacteria
17:25 - 19:15 Networking and dinner
19:15 Day 1 end
June 28th, day two
09:00 - 09:15 Coffee reception
09:15 - 10:00 Workshop part 1 - Scaling up & scaling out compound generation and simulation with Grid.ai
10:00 - 10:15 Break
10:15 - 11:00 Workshop part 2
11:00 – 12:00 Poster Session
12:00 - 12:35 Carl Poelking - Strategies for bias compensation and synthetic control in AI-driven structure-based drug design
12:35 - 13:00 Miguel Angel de Carvalho Servia - Automated Kinetic rate equation discovery- A methodological framework
13:00 – 14:00 Lunch
14:00 - 15:00 Keynote 2 – Kerry Gilmore - Towards understanding and controlling mechanistically ambiguous transformations
15:00 - 15:25 Felix Strieth-Kalthoff - Closing the Loop in Materials Discovery: The Quest for Organic Lasers
15:25 – 15:45 Break
15:45 - 16:10 Venkat Kapil - The first-principles diagram of monolayer nanoconfined water
16:10 – 16:35 Abhishek Sharma - AI-EDISON: Autonomous Intelligent Exploration, DIScovery and Optimisation of Nanomaterials
16:35 - 17:00 Closing Remarks
17:00 End of day 2
​June 29th, day three
The iDMT event; separate registration is required.
Information on the iDMT event is on the web page: https://www.idmt.online/digital-labs
Speakers
We are proud to bring inspirational speakers from across the globe
Confirmed speakers
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Prof. Dr. Connor W. Coley (Massachusetts Institute of Technology - MIT)
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"AI for chemical space navigation and synthesis"
Prof. Dr. Kerry Gilmore (University of Connecticut)
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"Towards understanding and controlling mechanistically ambiguous transformations"
Prof. Dr. François-Xavier Felpin (University of Nantes)
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"Autonomous flow reactors associating In-line/Online analyses and feedback algorithms"
Dr. Carl Poelking (Astex Pharmaceuticals)
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"Strategies for bias compensation and synthetic control in AI-driven structure-based drug design"
Dr. Ruben Sanchez-Garcia (University of Oxford)
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"Compound availability and the numbers we care about in computationally-driven drug discovery"
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Dr. Felix Strieth-Kalthoff (University of Toronto)
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"Closing the Loop in Materials Discovery: The Quest for Organic Lasers"
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Dr. Ross Johnstone (Research Engineer, SyntheticGestalt)
Robert Lee (Head of Solutions Architecture, Lightning AI)
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Workshop on "Scaling up & scaling out compound generation and simulation with Grid.ai"
Sponsored by
Early Career research talks
Poster session
Registration
27-28th June 2022
Hosted by the University of Cambridge
Abstract Submission
The 5th International Conference on Machine Learning and AI in (bio)Chemical Engineering will take place on June 27-28, 2022 in Cambridge as a hybrid event. We are requesting abstracts for posters and talks related to this theme. Potential topics/submissions include but are not limited to:
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Software implementations of key methods
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Experimental case studies using ML
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Method development, particularly those that simplify user experience
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Benchmarking of ML methods
Abstracts may be up to 400 words and optionally include explanatory figures. We emphasise that abstracts should include sufficient introduction for newcomers.