DATA-DRIVEN DRUG DISCOVERY STAGE

Harnessing Big Data to Accelerate Breakthroughs in Drug Discovery & Development

The Data Driven Drug Discovery Stage covers integrating multi-omics data, using AI for multi-modal data analysis and making actionable drug discovery decisions whilst addressing privacy and FAIR data standards.

DAY 1

On Day 1, The Data Driven Drug Discovery Stage will focus on Omics in Drug Discovery, Multi-Omics in Drug  Discovery and Data Driven Drug Discovery.

[topic] OMICS IN DRUG DISCOVERY
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Using Genomics to Infer Networks of Causal Genes and Choose New Drug Targets
  • Industrializing use of large scale GWAS and WGS/WES to identify genes causal for disease.
  • Using gene editing at genome-scale to infer cell- and condition-specific causal networks.
  • Applying mechanism and pathway insights to prioritize drug targets.
Speaker
Executive Director, Genomics Technologies
GSK
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Uncovering New Evidence to Inform and Validate Drug Discovery Decisions
  • By incorporating cause-and-effect data into knowledge graphs, researchers can access unique insights that arise when data is aggregated in this manner
  • Dr Ben Sidders will demonstrate the power of advanced cause-and-effect capturing methods, coupled with generative AI (genAI), in facilitating groundbreaking conclusions for drug discovery
  • When combined together, these technologies allow for more efficient and targeted experimentation, accelerating the development of novel treatments and therapies. 
Speaker
CSO
Biorelate

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Accelerating Drug Discovery Through the Power of Genomics, Machine Learning and Medical Big Data
  • Data and analytics are key for learning and improvement as we translate research insights for patients
  • We increase translational probability of success through:
  • (1) Mapping causal human biology through genomics across disease spaces
  • (2) Understanding longitudinal patient journeys
  • (3) Innovation opportunities through multi-disciplinary teamwork between in silico / therapeutic areas 
Speaker
Director and Head of Human Genetics
Boehringer Ingelheim
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[break] Lunch
  • Exhibition Floor: Wander around the vibrant exhibition floor to see the latest technologies and services on offer
  • Food Options: Grab a sandwich at The Tree of Life Cafe or head on over to the Food Village for a range of hot food options, including Indian, Jamaican and Lebanese food
  • Live Lounge Over Lunch: 15-minute technology-focused presentations
  • Poster Zone: Browse the posters and reach out to potential new collaborators
  • Talkaoke: The Flying Saucer of Chat, a fun and interactive pop-up show that offers you the opportunity to sit down with others and discuss any topic you like
[topic] MULTI-OMICS IN DRUG DISCOVERY
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The Future of Genomics in Drug Discovery

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AI Agents in Disease Genomics: Challenges and Opportunities in Advancing Biomarker Discovery
  • Chatbot Applications: showcasing interactive AI agents for interrogating DISGENET
  • Opportunities in Disease Genomics: Harnessing AI agents and agents-as-judge to interrogate large-scale biomedical data
  • Automated Information Extraction: Leveraging AI agents to extract quantitative data about disease biomarkers for research and clinical applications
  • Challenges Ahead: Addressing issues like data quality, hallucinations, reproducibility and provenance
Speaker
Co-Founder & Head of Bioinformatics
MedBioinformatics

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Leveraging Multi-modal Big Data in Drug Discovery and Development
  • Using the latest single cell proteomics methods to understand virus replication biology.
  • Developing new methods to improve sensitivity, depth and coverage of single-cell proteomics methods.
  • Applications to different model systems and improving analysis.
Speaker
Head of Target Discovery and Disease Data
Roche
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[break] Break
  • Speed Networking: Fun and useful sessions where you’ll rapidly meet other Festival attendees to immediately expand your network
  • Food Options: Grab a coffee and a cake at The Tree of Life Cafe or head on over to the Food Village for a range of hot food options including Indian, Jamaican and Lebanese food
  • Exhibition Floor: Wander around the vibrant exhibition floor to see the latest technologies and services on offer
  • Talkaoke: The Flying Saucer of Chat, a fun and interactive pop-up show that offers you the opportunity to sit down with others and discuss any topic you like
[topic] DATA-DRIVEN DRUG DISCOVERY
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Knowledge Graphs for Precision Oncology
  • Knowledge Graphs have in recent years gained a lot of prominence within biomedical AI applications, driven by the need for study-specific knowledge to be integrated with the world’s knowledge to obtain a deep and comprehensive view of complex disease landscapes such as in Oncology.
  • This presentation will discuss the applications of Knowledge Graphs across the drug discovery pipeline – from target discovery, combination prioritisation, patient stratification and clinical biomarker discovery.
  • The talk will also cover how KGs can help bridge the critical bench-to-bedside gap in biomedical R&D by translating Discovery knowledge to Clinical applications, including current limitations of the field. 
Speaker
Data Scientist
AstraZeneca
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Pioneering an AI Foundation Model for the Discovery of Disease Mechanisms and RNA Therapeutics
  • Accurately modeling and predicting RNA biology has been a long-standing challenge, bearing significant clinical ramifications for variant interpretation and the formulation of tailored therapeutics
  • Introducing BigRNA, an advanced RNA foundation model trained on over one trillion signals from genome-matched datasets to predict tissue-specific RNA expression, splicing, microRNA sites, and RNA binding protein specificity from DNA sequence
  • Single BigRNA model outperforms state-of-the-art models across a diverse range of tasks and extends to a wide range of R&D applications, specifically in the prediction, identification, and design of RNA-targeting therapeutic opportunities.
Speaker
Founder and Chief Innovation Officer
Deep Genomics

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The Unseen Hand: How AI-Informed Prescribing can Impact our Understanding of Real-World Medicine Safety and Effectiveness
  • AI can be used to inform prescription decisions, potentially leading to better outcomes for patients. Examples of these tools include drug-drug interaction checkers and treatment success prediction tools
  • These tools may alter our perception of medicine safety and effectiveness in the real world, as they can change when and where medicines are prescribed
  • We need to understand how these tools work, whether they are fair to everyone, and whether they are appropriate for the populations which they are used in
Speaker
RWE Director
Gilead Sciences

DAY 2

On Day 2 the focus on The Data-Driven Drug Discovery Stage will be on AI in Drug Discovery and Data Management in Pharma. The track will end with a panel discussion on AI in drug discovery from Discovery to Clinical Application.

[topic] AI IN DRUG DISCOVERY
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Artificial Intelligence and Machine Learning Across the Entire Drug Development Pipeline
  • It is well known that AI/ML have the potential for acceleration and innovation within pharmaceutical research and development
  • However, it is less widely known that the potential impacts span the entire drug development pipeline, from target identification, through molecule design and optimisation, clinical trials, and all the way to commercial investment decisions
  • This talk will describe how different ML/AI is being used right now across different parts of the pipeline, with examples such as the design of molecules, biological insight generation, novel biomarkers, and clinical trial optimisation
  • The talk will conclude by giving an indication of the future outlook, along with some open challenges that are faced by the industry 
Speaker
Executive Director, Head of the Centre for AI
AstraZeneca
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Innovations in Clinical Development: Harnessing Synthetic Data Generation and Multi-Modal Integration with AI
  • Introduction to Synthetic Data Generation: Exploring the role of generative models in creating synthetic datasets for clinical research, facilitating data augmentation, and addressing challenges of limited sample sizes.
  • Case Studies in Deep Learning: Highlighting successful applications of deep learning in analyzing multi-modal clinical data, including genomic sequencing, medical imaging, and electronic health records, to uncover novel insights and accelerate drug discovery.
  • Ensuring Data Privacy with Differential Privacy: Discussing the implementation of differential privacy techniques to protect patient confidentiality while enabling the sharing and analysis of sensitive clinical data across research institutions.
  • Advancements in Multi-Modal Integration: Demonstrating the importance of integrating diverse data sources, such as patient demographics, genetic profiles, and real-world evidence, to enhance the efficacy of AI-driven models in predicting treatment outcomes and optimizing clinical trial designs.
Speaker
Associate Director
UCB
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Towards Rational Drug Design with AI
  • We are advancing the field of drug design by developing new AI models like AlphaFold 3 and beyond, allowing us to better understand diseases and design new therapeutic molecules in silico
  • We can use these AI models to rapidly design drug candidates against some of the toughest targets. Learn about how AI unlocks scientific discovery, advancing rational drug design at Isomorphic Labs
Speaker
Chief Technology Officer
Isomorphic Labs
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[break] Lunch
  • Exhibition Floor: Wander around the vibrant exhibition floor to see the latest technologies and services on offer
  • Food Options: Grab a sandwich at The Tree of Life Cafe or head on over to the Food Village for a range of hot food options including Indian, Jamaican and Lebanese food
  • Biodata Showcase: 15-minute technology-focused presentations
  • Poster Zone: Browse the posters and reach out to potential new collaborators
  • Talkaoke: The Flying Saucer of Chat, a fun and interactive pop-up show that offers you the opportunity to sit down with others and discuss any topic you like
[topic] DATA MANAGEMENT IN PHARMA
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Fusion Between Data Mesh, FAIR & Data Governance
  • Data Mesh decentralizes data ownership, making each domain responsible for its own data as a product, fostering more agile and scalable data management.
  • Integrating FAIR principles ensures that data in a Data Mesh is Findable, Accessible, Interoperable, and Reusable, enabling easier data discovery, collaboration, and integration across domains.
  • Effective data governance in a Data Mesh framework maintains standards for data quality, privacy, and security, ensuring compliance while supporting innovation and trust in decentralized environments.
Speaker
Senior Director IT Data Value Office
BioNTech
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Enabling Innovation with FAIR OMICS Data Management: A Boehringer Ingelheim Perspective
  • FAIR OMICS Data Management yeilds measurable return on investment.
  • FAIR Infrastructure is essential for promoting user-adoption and generating data-driven value.
  • Governance assumes a central role in the integration of data management processes within the organization. 
Speaker
Product Owner
Boehringer Ingelheim
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PANEL

Speakers
Executive Director, Genomics Technologies
GSK
Director and Head of Human Genetics
Boehringer Ingelheim
Head of Target Discovery and Disease Data
Roche
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[break] Break
  • Speed Networking: Fun and useful sessions where you’ll rapidly meet other Festival attendees to immediately expand your network
  • Food Options: Grab a coffee and a cake at The Tree of Life Cafe or head on over to the Food Village for a range of hot food options, including Indian, Jamaican and Lebanese food
  • Exhibition Floor: Wander around the vibrant exhibition floor to see the latest technologies and services on offer
  • Talkaoke: The Flying Saucer of Chat, a fun and interactive pop-up show that offers you the opportunity to sit down with others and discuss any topic you like
[topic] PANEL: AI IN DRUG DISCOVERY
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Panel: AI in Drug Discovery: The Journey from Discovery to Clinical Application
  • How can AI support drug target ID and early drug discovery (e.g. molecule design and optimisation). Applying AI in precision medicine approaches.
  • How can AI be used in the later stages of clinical development (e.g. improving/optimising clinical trials).
  • What are the challenges associated with using AI such as integrating huge data sets, access to high-quality data and how can we demonstrate these tools are robust and reliable.
  • What are the other privacy concerns associated with using AI to process sensitive genomic and clinical data sets?
Speakers
Director of Centre of Global Oncology
ICR
Senior Director, Oncology Data Science
AstraZeneca
Senior Director of Computational Biology, Neuroscience
Recursion