Symposium
Active Learning in Pharma
2nd December 2024
2pm London GMT / 9am Boston EST
Online (Zoom link will appear here)
Closed-loop labs, powered by Active Learning, rapidly accelerate experimentation.
Experimentation in Pharma is essential.
Until recently, Pharma has relied on experimentation methods 100 years old.
Pharma now are rapidly adopting methods powered by machine learning such as Active Learning and Bayesian Optimisation that enable up to 80% cost savings.
This symposium gathers academia and industry to make a first step towards an ecoystem of Active Learning.
“We think Bayesian Optimisation can increase the chance of finding the right [drug and] co-former combinations by around three-folds”
- Principal Scientist (Solid Formulation) at top 10 Pharma Company
Key Topics Covered
Latest case studies showcasing the benefits of Active Learning in data-driven experimentation & closed-loop optimisation
Novo Nordisk on ProcessOptimizer
Active Learning State of the art in Pharma: Algorithmic & Application advancements
Current Implementation Challenges & Limitations
Discuss the rationale for OpenSource: advantages and disadvantages
10+
Pharma Companies have built Bayesian Optimisation teams since 2022
$5m
Estimated average annual savings from Bayesian Optimisation
5-25%
Average BO performance improvement with Transfer Learing Bayesian Optimisation
1 day
Time to implement BO in established applied maths teams in Pharma
Tentative Program
(London, GMT)
14:00 10-minute Keynote: How to build an ecosystem for Active Learning in Pharma
14:10 5-minute Lightning Talks (Opportunity for teams to introduce themselves and their work)
Novo Nordisk: Rune Christensen
Bayer: Giulio Volpin
Merck Germany: Adrian Socic & Alexander Hopp
Exscientia: Jonathan Harrison
Merck USA: Kevin Stone
TriNova: Thomas Casey
Evonik: Johannes Peter on BoFire
Acceleration Consortium: Sterling Baird
SOLVE: Jose Folch
Evotec: Lionel Colliandre
15:00 Discussion Round:
How big should datasets be to (a) be useful on their own, and (b) to be useful in a transfer learning set-up? How long does it usually take to gather these datasets?
OpenSource for Active Learning Software: Where are we going as a community?
TBD
16:00 Break: Meet & Greet of industry and academia
16:20 Talks: Latest Advancements in Algorithms (UCL, ETH)
"Applying Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Considerations", Mohammed Azzouzi, EPFL (Paper)
"Sample-efficient Bayesian Optimisation Using Known Invariances", Theo Brown, UCL (Paper)
16:50 Talks: Latest Advancements in Applications
"Automated Discovery of Pairwise Interactions from Unstructured Data", Jason Hartford, Valence Labs/Recursion Pharmaceuticals (Paper)
"Self Driven Media Optimization Using Multi-Objective Bayesian Optimization With Laboratory Automation", Dania Awad, WSSB (Presentation)
17:10 Conclusion
17:20 Open End in Breakout Rooms
Expected Outcomes and Benefits
Meet with global academic experts and industry innovators in a focused symposium to hear the latest case studies in the use of Active Learning/Bayesian Optimisation in Pharma space (e.g. assay development, process optimisation, transfer learning etc.).
Stay informed about the latest developments. This programme takes a deep dive into best practice approaches and latest technological advancements; bringing together thought leaders from across the globe for focused discussions and networking. Discuss and facilitate knowledge exchange about the latest use-cases.
Understand current challenges and limitations What challenges will I face in the future? What do I have to prepare for? Where are my strengths and weaknesses, compared to other teams and their challenges?
Community Job Board (Active Learning)
If you'd like to post an Active Learning role here (Bayesian Optimisation, Lab Automation etc.), please reach out to us.
Bayer
Two Positions at Crop Science in Frankfurt am Main:
Data Scientist (all genders) - Active Learning and Small Molecules Design: https://lnkd.in/eB3s6DQs
Data Scientist (all genders) - Probabilistic Modelling and Phenotyping https://lnkd.in/ezhFYRi5
Two Positions at Pharma in Berlin (one link/advert for both):
Research Scientist (all genders) - Machine Learning for Small Molecules https://lnkd.in/eST2XEp3
Sponsors
Unified Laboratory Informatics & Management Software made for enterprise R&D teams.
TBC
TBC
TBC
Hosted by Matterhorn Studio
A Global Collective of Machine Learning Researchers
We enable high-performance Machine Learning for high-performance teams of scientists in Pharmaceuticals, Chemistry and Materials.
Based in Oxford, UK, we pride ourselves in Our Values of scientific excellence and social responsibility.