AUGMENTED INTELLIGENCE IN DRUG DISCOVERY XCHANGE
EUROPE 2023
Zurich, November 23

Welcome to hubXchange’s Europe Augmented Intelligence (AI) in Drug Discovery Xchange 2023, bringing together executives from pharma and biotech to address and find solutions to the key issues faced in AI-led drug discovery.
Discussion topics will cover Data Quality, Target Identification, Lead Generation & Optimization and Drug Response Prediction.
Take advantage of this unique highly interactive meeting format designed for maximum engagement, collaboration and networking with your peers.

Please note this is an In-Person meeting.

VENUE DETAILS: Hilton Zurich Airport Hotel, Hohenbuehlstrasse 10 – 8152 Opfikon, Switzerland

SNAPSHOTS OF DISCUSSION TOPICS

Track 1: Data Quality

Improving data robustness and utility to drive biological hypothesis
Integrating multiple data sources for target identification in augmented intelligence for drug discovery

Track 2: Target Identification

Building augmented intelligence for therapeutic target identification: foundations, best practices, challenges
Exploring key issues in drug target identification

Track 3: Lead Generation & Optimization

Advances in augmented drug design: AI-driven insights and challenges for small and ‘new modalities’ molecules
Transformation and technical challenges in augmenting lead optimisation with generative models

Track 4: Drug Response Prediction

Machine Learning-Based Approaches for Predicting Drug Response in Patients
Augmented intelligence-driven strategies for personalized medicine in drug response prediction

Data Quality

Time

Titles and Bullets

Facilitator

8:00 – 8:30

Registration

8:30 – 9:00

Opening Address & Keynote Presentation

9:05 – 10:05

Improving data robustness and utility to drive biological hypothesis

How do we differentiate between AI and ML and does it matter?
ML models are open ended, when do you know the solution is optimised?
Orthogonal checks in wet systems are axiomatic. How do you stop them being self-fulfilling?
Given the complexity of data inputs, data outputs are complex. Are visualisation techniques comprehensive enough for explanation and interpretation?

Trevor Howe

Janssen Fellow, Global Head Cardiovascular and Metabolism Translational Medicine, Johnson & Johnson

Trevor is a Janssen Fellow and Head of Translational Genomics for Johnson & Johnson Innovation. He is responsible for identifying and validating drug targets or pathways from human genetic signatures though internal and external collaborations and has industrial experience in computational chemistry, medicinal chemistry, bioinformatics, genomics and structural biology. He serves on several UK Research Council scientific advisory bodies and boards and was the J&J scientific co-lead for UK Biobank WGS project. He also led J&J into Our Future Health aiming to collect a cohort of 5 million individuals

10:10 – 10:40

1-2-1 Meetings / Networking Break

10:40 – 11:10

1-2-1 Meetings / Networking Break

11:10 – 11:20

Morning Refreshments

11:20 – 12:20

Sponsor-led Roundtable by Exclera

12:20 – 13:20

Networking Lunch

13:20 – 13:50

Spotlight Presentation

13:55 – 14:25

1-2-1 Meetings / Networking Break

14:25 – 14:55

1-2-1 Meetings / Networking Break

15:00 – 15:30

Poster Session by Nostrum Biodiscovery

15:35 – 16:35

Integrating multiple data sources for target identification in augmented intelligence for drug discovery

How do we best leverage different data modalities for target identification and biomarker discovery?
What data modalities are the most informative, what are we missing?
Share experiences in data acquisition and quality control.
What are the challenges in integrating data coming from different sources and how to overcome them.

Slavica Dimitrieva

Associate Director & Senior Principal Scientist, Novartis

Slavica Dimitrieva works as an Associate Director & Senior Principal Scientist at the Oncology Data Science department at the Novartis Institutes for BioMedical Research in Basel. She is passionate about applying AI on multi-modal data for oncology drug discovery and precision medicine. Slavica has a background in Computer Science and a PhD in Computational Biology from the EPFL in Lausanne. During her studies, she received an ETH Medal for her Master thesis at ETH Zurich, and later a Best Graduate Paper Award by the Swiss Institute of Bioinformatics. After a postdoc at the Imperial College London, she worked as a Bioinformatics expert and Lecturer at the Functional Genomics Centre in Zurich at ETH Zurich, before joining Novartis. At Novartis her focus is on data-driven drug discovery and she co-leads the data science efforts in oncology translational research.

16:35 – 17:35

Evening Drinks Reception

Target Identification

Time

Titles and Bullets

Facilitator

8:00 – 8:30

Registration

8:30 – 9:00

Opening Address & Keynote Presentation

9:05 – 10:05

Building augmented intelligence for therapeutic target identification: foundations, best practices, challenges

What are the data requirements for target identification, are some more important than others, in what context?
How to keep up with a rapidly changing data landscape?
Where do knowledge graphs come in, how are they used, by whom?
Which ML/AI methods are useful for data integration & target prioritization?
What are collective thoughts about pre-competitive partnerships?

Florian Nigsch

Director, Data Science, Novartis

Florian Nigsch studied chemistry and biology in Vienna, Austria, and Paris, France, where he graduated with a Master’s degree in Physical and Theoretical Chemistry. He then obtained a PhD in chemistry from Cambridge University, UK. After a postdoc at NIBR at the interface of cheminformatics and bioinformatics, he joined NIBR in Basel, Switzerland in 2012. Florian is currently leading a group of data scientists in the Discovery Sciences department focused on target identification and project progression in early drug discovery.

10:10 – 10:40

1-2-1 Meetings / Networking Break

10:40 – 11:10

1-2-1 Meetings / Networking Break

11:10 – 11:20

Morning Refreshments

11:20 – 12:20

Sponsor-led Roundtable

12:20 – 13:20

Networking Lunch

13:20 – 13:55

Spotlight Presentation

13:55 – 14:25

1-2-1 Meetings / Networking Break

14:25 – 14:55

1-2-1 Meetings / Networking Break

15:00 – 15:30

Poster Session by ImmunoPrecise Antibodies

15:35 – 16:35

Exploring key issues in drug target identification

Lars von Wedel

Head of Advanced Analytics, Grunenthal Pharma GmbH TBC

16:35 – 17:35

Evening Drinks Reception

Lead Generation & Optimization

Time

Titles and Bullets

Facilitator

8:00 – 8:30

Registration

8:30 – 9:00

Opening Address & Keynote Presentation

9:05 – 10:05

Advances in augmented drug design: AI-driven insights and challenges for small and ‘new modalities’ molecules

What’s the current status of chemical representation for small and ‘new modalities’ molecules in augmented drug design?
How is AI aiding in unraveling biological complexity to predict in-vivo readouts with more data (biological, ADME, and PK/PD)?
Does limited data hinder augmented drug design, especially for novel and complex targets?
Which key areas of AI/ML in lead generation are expected to have a significant impact in the coming years?

Alfonso Pozzan

VP Head of Molecular Architects, Evotec

Alfonso Pozzan is computational chemist and drug hunter with over 25 years of experience supporting drug discovery across various research organizations. His expertise spans drug design, molecular modeling, cheminformatics, and data analysis. Alfonso’s multidisciplinary background allows him to approach challenges with analytical prowess and an open mind. Possessing a problem-solving attitude and strong leadership skills, he currently leads the Molecular Architect department at Evotec Verona, where he collaborates with computational chemists and medicinal chemists to drive innovation in projects focused on discovering lead molecules.

10:10 – 10:40

1-2-1 Meetings / Networking Break

10:40 – 11:10

1-2-1 Meetings / Networking Break

11:10 – 11:20

Morning Refreshments

11:20 – 12:20

Sponsor-led Roundtable

12:20 – 13:20

Networking Lunch

13:20 – 13:50

Spotlight Presentation

13:55 – 14:25

1-2-1 Meetings / Networking Break

14:25 – 14:55

1-2-1 Meetings / Networking Break

15:00 – 15:30

Poster Session by Ardigen

15:35 – 16:35

Transformation and technical challenges in augmenting lead optimisation with generative models

How to control innovativeness and accuracy of generative models?
How to efficiently establish collaboration between chemists and generative models?
How much scientists’ time and wet lab budget should be spent on improving models instead of doing work for drug discovery projects?

Krzysztof Brzozka

Chief Scientific Officer, Executive Vice President, Ryvu Therapeutics

Dr. Brzozka is a highly accomplished Chief Scientific Officer at Ryvu Therapeutics, having joined the company in 2007. With his interdisciplinary expertise, he has played a pivotal role in building a robust pipeline of small molecule oncology therapeutics and a high throughput discovery engine platform. Dr. Brzozka is responsible for managing the early pipeline candidates, covering their full development until clinical phase, and he has brought two Ryvu first-in-class oncology programs into the clinic: dual PIM/FLT3 inhibitor SEL24/MEN1703 and SEL120, CDK8/19 inhibitor. He also sits on the board of Ardigen, a bioinformatics and precision medicine company, and played a key role in the founding of NodThera, a biotechnology company focused on therapeutics related to inflammasome biology.

16:35 – 17:35

Evening Drinks Reception

Drug Response Prediction

Time

Titles and Bullets

Facilitator

8:00 – 8:30

Registration

8:30 – 9:00

Opening Address & Keynote Presentation

9:05 – 10:05

Machine Learning-Based Approaches for Predicting Drug Response in Patients

Maren Lang

Director Bioinformatics, BioNTech

10:10 – 10:40

1-2-1 Meetings / Networking Break

10:40 – 11:10

1-2-1 Meetings / Networking Break

11:10 – 11:20

Morning Refreshments

11:20 – 12:20

Sponsor-led Roundtable

12:20 – 13:20

Networking Lunch

13:20 – 13:50

Spotlight Presentation

13:55 – 14:25

1-2-1 Meetings / Networking Break

14:25 – 14:55

1-2-1 Meetings / Networking Break

15:00 – 15:30

Poster Session

15:35 – 16:35

Augmented intelligence-driven strategies for personalized medicine in drug response prediction

There is a large number of datasets available for many disease indications and clinical treatments, how can these be repurposed to serve new drug discoveries and response predictions for new therapies and/or indications?
How can we better incorporate domain knowledge into our models and discover new insights into the biological mechanisms behind drug mode of action?
Our models are increasing in complexity as we add more modalities and neural net types, how can we put interpretability and explainability in front of the process so that we don’t lose sight of the biology behind the questions being asked.

Julian Spagnuolo

Senior Data Scientist, Scailyte

Julian is a senior data scientist at Scailyte with a background in immunoinformatics and has extensive experience with a broad range of data types and analyes from flow-cytometry to single cell sequencing and T cell receptors. After finishing a PhD in bacterial stress responses in New Zealand he moved to Switzerland for a 6 year post-doc in the Experimental Immunology lab of Gennaro de Libero at the University of Basel.

16:35 – 17:35

Evening Drinks Reception

AUGMENTED INTELLIGENCE IN DRUG DISCOVERY XCHANGE
EUROPE 2023
Zurich, November 23