top of page
69962821_1685450341591481_90159098650291

6th Machine Learning and AI in Bio(Chemical) Engineering Conference

06-07th July 2023
 
In person-only event

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 Conference

Conference Agenda

The event will be held at the Department of Chemical Engineering and Biotechnology,

Philippa Fawcett Dr, Cambridge CB3 0AS

July 06th, day one

10:00 11:00    Welcome and refreshments

11:00 12:00    Michael Brenner (Keynote)  - Scientific uses of automatic differentiation

12:00 12:25    Zsuzsanna Koczor-Benda - High-throughput property-driven generative design of functional organic molecules

12:25 12:50    Kobi Felton - ML-SAFT: A framework for PCP-SAFT parameter prediction 

12:50 14:00    Lunch

14:00 14:35    Pietro Liò - Generative and Graph models in chemistry and medicine

14:35 15:00    Ryan Greenhalgh - Current methods for drug property prediction in the real world

15:00 15:25    Egon Heuson - Enzyme activity prediction using neural networks, docking and high-throughput screening results

15:25 15:35    Break

15:35 16:10    Timur Madzhidov - State-of-the-art on reaction prediction condition

16:10 16:35    Benoît Baillif - Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations

16:35 17:00    Tom Savage - Multi-Fidelity Data-Driven Design and Analysis of Reactor and Tube Simulations (DARTS)

17:00 19:00    Networking and dinner

19:00                 Day 1 end

 

July 07th, day two

09:00 – 09:15    Coffee reception

09:15 – 10:00    Workshop part 1 - Rapid predictive modelling without having to write code

10:00 – 10:15    Break

10:15 – 11:00    Workshop part 2 - Rapid predictive modelling without having to write code

11:00 – 12:00    Poster Session

12:00 – 12:35    Antonio Del Rio Chanona - Building Models with Machine Learning

12:35 – 13:00    Jiaru Bai - From Platform to Knowledge Graph: Distributed Self-Driving Laboratories

13:00 – 14:00    Lunch

14:00 – 14:35    Stefan Born - Machine Learning as an integral part of an automated experimental workflow in protein engineering

14:35 – 15:00    Nishanthi Gangadharan - Data-driven Dynamic Control Scheme for Antibody Producing CHO Cell Cultures in Fed Batch

15:00 – 15:20    Break

15:20 – 15:45    Emma King-Smith - Practical machine learning for synthetic chemistry

15:45 – 16:10    Miruna Cretu - Standardizing chemical compounds using language models

16:10 – 16:45    Closing Remarks

17:00                 End of day 2

Speakers

Speakers

We are proud to bring inspirational speakers from across the globe

Confirmed speakers

Prof. Pietro Liò (University of Cambridge)
  • Generative and Graph models in chemistry and medicine
Dr. Emma King-Smith (University of Cambridge)
  • Practical Machine Learning for Synthetic Chemistry
Dr. Ryan Greenhalgh (DeepMirror)
  • Current Methods for Drug Property Prediction in the Real World 
Egon Heuson (University of Lille)
  • Enzyme activity prediction using neural networks, docking and high-throughput screening results
 
Timur Madzhidov (Elsevier)
  • State-of-the-art in reaction prediction conditions
Benoît Baillif (University of Cambridge)
  • Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations 
 
Tom Savage (Imperial College London)
  • Multi-Fidelity Data-Driven Design and Analysis of Reactor and Tube Simulations (DARTS)
Nishanthi Gangadharan (University of Cambridge)
  • Data-driven Dynamic Control Scheme for Antibody Producing CHO Cell Cultures in Fed Batch 
Dr. Zsuzsanna Koczor-Benda (University of Warwick)
  • High-throughput property-driven generative design of functional organic molecules 
Jiaru Bai (University of Cambridge)
  • From Platform to Knowledge Graph: Distributed Self-Driving Laboratories 
 
Miruna Cretu (IBM Research Zurich)
  • Standardizing chemical compounds using language models
 
Kobi Felton (University of Cambridge)
  • ML-SAFT: A framework for PCP-SAFT parameter prediction
 
Dr. Antonio Del Rio Chanona (Imperial College of London)
  • Building Models with Machine Learning
Dr. Stefan Born (Technische Universitaet Berlin)
  • Machine Learning as an integral part of an automated experimental workflow in protein engineering
Dr. Michael P. Brenner (Harvard University)
  • Keynote talk: Scientific uses of automatic differentiation

Workshop

 
Rapid Predictive Modeling without Having to Write Code

Volker Kraft, JMP Principal Academic Ambassador 

Phil Kay, JMP Learning Manager

Keynote speaker for 2023 edition

Dr. Michael Brenner (Harvard)

Workshop

Rapid Predictive Modeling without Having to Write Code

Volker Kraft, PhD, JMP Principal Academic Ambassador 

Phil Kay, PhD, JMP Learning Manager

JMP Pro is an industry-standard software for predictive modeling. In fields like chemistry & biotech, pharma, manufacturing or environment & sustainability, modern modeling methods and powerful tools help engineers and scientists to make statistical discoveries from data – without the need to write any line of code. 

During this hands-on workshop you will learn how to:

  • build better and more useful models with modern predictive modeling techniques, such as regression, neural networks, decision trees, support vector machines, k nearest neighbors, and more;

  • fit multiple models automatically and select the best-performing model with model screening;

  • apply cross-validation techniques to avoid overfitting; 

  • use the Prediction Profiler to improve model understanding and make predictions;

  • deploy models both inside and outside of JMP (e.g., in Python)

Abstract Submissions

Abstract Submission

The 6th International Conference on Machine Learning and AI in (bio)Chemical Engineering will take place on July 06-07, 2023. We are requesting abstracts for posters and talks related to this theme. Potential topics/submissions include but are not limited to:

  • Software implementations of key methods

  • Experimental case studies using ML

  • Method development, particularly those that simplify user experience

  • 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.

Sponsors

Sponsors and Exhibitors

Learn more about our sponsors, key players in the area

Elsevier.png
AZ.png
Registration

Registration

06-07th July 2023
 
Hosted by the University of Cambridge
 

Standard registration: £75

Fees include access to the event, refreshments for both days and confererence networking dinner in the evening of day 1.

Payments can only be done by credit card.

bottom of page