AUGMENTED INTELLIGENCE IN DRUG DISCOVERY XCHANGE
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
- Improving data robustness and utility to drive biological hypothesis
- Integrating multiple data sources for target identification in augmented intelligence for drug discovery
- Building augmented intelligence for therapeutic target identification: foundations, best practices, challenges
- Exploring key issues in drug target identification
- 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
- Machine Learning-Based Approaches for Predicting Drug Response in Patients
- Augmented intelligence-driven strategies for personalized medicine in drug response prediction
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?
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
1-2-1 Meetings / Networking Break
1-2-1 Meetings / Networking Break
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.
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.