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
- 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
Data Quality
Opening Address & Keynote Presentation
Taming the beast: A one year vision on Generative AI in knowledge discovery on scientific literature and more
The keynote will emphasize the rapid evolution of Generative AI, commencing with GPT-2 in 2019 and progressing swiftly through GPT-3 to the recent introduction of ChatGPT and GPT-4. This transformation significantly impacted data science and innovation departments worldwide, particularly those in research-driven sectors like life sciences and pharmaceuticals. Our talk will give Digital Science’s perspective on this dynamic technological landscape, projecting past experiences into the future to formulate a vision for the coming year. We will highlight the rise of open-source community efforts in developing efficient transformer models, enabling comprehensive analysis of scientific literature.
Additionally, underscoring the integration of symbolic AI and structured data, emphasizing the challenge of effectively harnessing AI’s potential while ensuring trust, reliability, cost-efficiency, and sustainability in the field of data science and innovation.
Peter Dörr PhD is a business strategist with hands-on experience in sales, product marketing and product development. His experience expands over several industries including IT, consumer electronics to Knowledge and AI solutions for the pharma and other research intensive industries.
Peter is a regular speaker at events such as biotechX, Bio IT World, Reuters Pharma Europe. Digital innovation is his topic, and today that means above all how we can make existing knowledge more usable with the help of artificial intelligence. In his role at Digital Science he is an interface between customers, product development and professional services.
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 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
Challenges and opportunities of imaging-based AI for drug discovery
- Data access & quality: The what, why and how
- Curation pipelines
- AI computational workflows
Dr. Amina Chebira serves as the Director of Relationship Management and Field Application Scientist at Flywheel, overseeing EMEA customer solutions. Previously, she managed ELCA’s data science team in western Switzerland and led AI projects at CSEM. Amina holds degrees from University Paris 7, Swiss Federal Institute of Technology in Lausanne, and a Ph.D. in Biomedical Engineering from Carnegie Mellon University. With over 15 years of leadership experience, her expertise spans data science, imaging, and biomedical image processing. She has been an active member of IEEE’s bioimaging committee, co-chairing sessions at prominent conferences and contributing as a reviewer for several journals. Additionally, she holds multiple patents
Networking Lunch
Spotlight Presentation
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Knowledge Extraction with Smart Data Management
IPA is pioneering antibody discovery and characterization by integrating cutting-edge neuro-symbolic techniques. We elucidate how Retrieval Augmented Generation (RAG) models in combination with knowledge graphs play a pivotal role in mitigating life sciences data-specific challenges while significantly enhancing
model performance. Furthermore, our exploration extends to the LENSai platform, a dynamic tool that streamlines access to a unified knowledge repository, efficiently manages data, and optimizes processes. This seamless integration serves as the linchpin for unleashing the full potential of amalgamating insights from experimental and computational domains, thereby expediting the clinical development of biotherapeutics.
Arnout Van Hyfte’s journey with BioStrand, now a subsidiary of IPA
(ImmunoPrecise Antibodies), began in 2019 as a key member of the founding team. He has played a pivotal role in shaping the company’s commercial strategies, setting the stage for its remarkable growth, and building a robust infrastructure that forms the very foundation of BioStrand’s operations. Arnout oversees the development team, ensuring their seamless coordination and fostering a culture of innovation. His leadership extends to crafting effective sales and marketing strategies, as well as engaging with the market to cultivate meaningful relationships
1-2-1 Meetings / Networking Break
1-2-1 Meetings / Networking Break
Poster Session
Lead Generation & Optimization topic: Synergistic Strategies for Enhanced Lead Optimization
The pursuit of efficacious compounds demands a comprehensive approach to lead optimization. This presentation explores our latest advancements, incorporating strategies that seamlessly integrate AI-driven molecular design and physics-based simulations. Key aspects covered include:
- Overcoming AI over-creativity with a streamlined process, ensuring optimal activity without sacrificing feasibility.
- Approaching reality by integration of generative AI with an understanding of structural relationships.
- Balancing efficiency by adjusting methods to data, highlighting why large models may not always be the optimal fit for molecular tasks.
- Implementing a new paradigm for molecule discovery with the emergence of phenotypic-powered lead optimization.
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Ardigen, a pioneering AI CRO, stands at the forefront of enabling AI transformation for biotech and pharmaceutical sectors. Our mission is to increase the probability of success in the drug development process. Positioned at the intersection of biology and computational science, we utilize our advanced platforms and deep expertise to turn vast amounts of data into pivotal, ready-to-use scientific insights. This proficiency aids in areas such as finding the right therapeutic targets, optimizing small molecules, and advancing biologics.
Leveraging our expertise, we also enhance the infrastructure that allows our clients to integrate and utilize these capabilities seamlessly within their organizations. Our specialized solutions and services focused on data engineering such as digital transformation, data processing, and data management, to fully support the needs of our partners.
By empowering companies with both essential knowledge and advanced tech solutions, Ardigen is committed to shaping the future of an AI-driven biopharma landscape.
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.
Frank Dondelinger is the group lead for the Basel team in Oncology Data Science at Novartis Biomedical Research, and the strategy lead for AI in Oncology. His team focus on pre-clinical and early stage clinical research in the Novartis oncology drug portfolio, and employ AI and computational biology for data integration, data augmentation and predictive modelling. He hold a PhD in Machine Learning for Systems Biology and has previously led an academic research group on machine learning for computational biology at Lancaster University, as well as working as data analysis lead for digital biomarkers at Roche.
