Augmented Intelligence in Drug Discovery Xchange
Europe 2021, December 3rd
Welcome to hubXchange’s European Augmented Intelligence (AI) in Drug Discovery Xchange 2021, 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, Lead 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 now a fully VIRTUAL meeting.
Opening Address & Keynote Presentation
Closing the data-insight gap
- The volume of biological data is growing exponentially
- But analytic capabilities are not keeping up – broadening the data-insight gap
- Computational disease models can help. They’re designed to process high throughput and support R&D in every phase of the drug lifecycle
Co-founder and Chief Scientist
Professor Shai Shen-Orr is the Co-founder and Chief Scientist of CytoReason, a technology company developing computational disease models. He serves as Associate Professor in the Faculty of Medicine at the Technion—where he directs the laboratory of Systems Immunology and Precision Medicine. In his research, Prof. Shen-Orr develops new analytical methodologies for grappling with the intricate complexities of the immune system, especially as they occur in advanced age, and to define biomarkers to evaluate immune health. His work has been featured and cited in numerous top-tier journals and systems biology textbooks, and has laid the foundation for CytoReason. To date, six of the world’s top ten pharma companies use CytoReason’s technology to speed up drug discovery and dramatically cut the cost of developing new medicines.
The road to achieving seamless operational ML – how to solve the last mile of ML delivery
- What lessons can we implement from other industries?
- How to move from PoC (Data Science work) to enterprise ready ML models in production
- Which skills do we need (ML engineer vs. data engineer, vs data science), how to manage the handover (self-service vs. managed service)
- Discuss the training aspects to enable organizational readiness for ML
Head Data Science, pREDi
Anna is a Bioinformatician by training and leads the Data Science Department in Roche Pharma Research and Early Development in Germany. Her team supports pre-clinical and clinical research teams through data management and data analysis, applying in particular Artificial Intelligence methods. We are analyzing high-dimensional data such as imaging data, genomic information, and data from electronic health records to better understand diseases and develop personalized therapies.
Anna holds a PhD in Bioinformatics and Biomedical Informatics from the University of Pompeu Fabra in Barcelona and Master in Bioinformatics from the Ludwig-Maximilians-Universität and the Technische Universität München. She completed her postdoc education at the Faculty of Biomedical Informatics at Stanford University in the USA.
- How do we assess and test the quality of a dataset? What threshold does it need to hit in order to be included in training an AI/ML model?
- How do we verify possible dataset challenges (e.g. outliers) at scale? Is it even possible beyond a handful of data points?
- What do you think about the peer review process to be brought down to the data world? Is there is a parallel in that world, and if not can there be one? Should we be adopting dataset standards that already exist?
Director, Scientific Products
Norman comes with a rich product management background that includes experience in developing and launching enterprise solutions. He brings a user centric and analytically driven approach; the ability to identify areas of growth and opportunity; and experience in setting strategic goals that align with the business objectives of the organization. Norman is leading Excelra’s rapid foray into technology solutions to accelerate innovation in drug development. He holds a Master of Liberal Arts, Extension Studies with a focus in Biology, from Harvard University.
Founder and Chief Executive Officer
Noor is a serial biotech entrepreneur with a track record of achievements in AI having held an Assistant Professorship from Aalborg University. Noor has published numerous papers in the field of artificial intelligence and its application to drug discovery and is an inventor on a handful of patents. She is passionate about data and AI and on a mission to cure disease with the power of human and machine learning. She is currently the CEO at GlamorousAI, a biotech company that pushes the boundaries to what is possible with AI to cure debilitating diseases. She is a recognised healthcare leader, MIT innovator under 35 and in BBC 100 women for 2019.
Building drug discovery on stronger foundations: AI-Augmented target identification at BenevolentAI
BenevolentAI develops and applies AI to drug discovery to find treatments that are more likely to be successful in the clinic and effective in patients. In this talk, Bryn Williams-Jones, VP Drug Discovery, will explore how he bridges the world’s of science and technology to help guide technologists in their work to develop AI tools that meet hyper-specific scientific requirements. He will then demonstrate how BenevolentAI applies these tools to empower target identification, precision medicine and molecular design. Bryn will cite case studies from BenevolentAI’s work in novel target discovery, drug repurposing and in collaboration with AstraZeneca.
Vice President Drug Discovery
Bryn is responsible for driving the application of AI-augmented drug discovery to disease hypothesis generation across the BenevolentAI portfolio bringing together the key scientific and AI strands of research. Before joining BenevolentAI Bryn led the Open PHACTS IMI project, and is CEO of the Open PHACTS Foundation project building open semantic web infrastructure for drug discovery. Prior to that Bryn worked at Pfizer across a wide range of Therapeutic Areas applying data and computational science to early drug discovery hypothesis generation.
- Homogeneity and heterogeneity in pharmaceutical research data sources (public and corporate)
- Practical aspects of FAIRification processes
- Deployment of FAIRified data to bench scientists
Senior Scientist In-silico Toxicology
Organic chemist by education (PhD at University of Goettingen) and chemoinformatics scientist by heart. He combines 9 years of experience in Computer-Aided Drug Design with 8 years in leading the physicochemical wet-lab in Frankfurt. In 2020, he switched to Sanofi’s In-silico Toxicology group with main focus on workflow digitalisation of existing toxological processes and assessments.
This combination of drug design experience with deep insights in wet-lab processes and data management approaches was the foundation to FAIRify a significant part of Sanofi’s data warehouse, e.g. ADME properties, physicochemical properties, in-vivo PK and biological compound profile.
The strength of the FAIRification process was proven by using FAIRified solubility data to take part in the global “Solubility Challenge” contest of 2019, submitting predictions within Top3 of all contributors.
4:10 – 4:40pm
- Share experiences in multi-omics data acquisition and curation.
- Discuss sources of bias in data, and approaches to overcome these problems.
- What are the best methods to facilitate data integration?
- How do we best leverage multi-omics data for drug discovery?
Director, Data Science, Oncology
Eric Durand is Director of Data Science, Novartis Institute of Biomedical Research Oncology. Based in Basel, his group develops and applies state-of-the-art machine learning, bioinformatics and computational biology methods in oncology drug discovery and development. The group impacts the entire drug development process, from target identification to biomarker strategy and analysis in late stage clinical trials. Eric obtained his PhD in Bayesian statistics from the Grenoble Institute of Technology, France. Then, he undertook postdoctoral studies at UC Berkeley, CA, during which he contributed to the analysis of the first draft of the Neanderthal and Denisovan genomes. He moved to 23andMe, inc. in 2011 where he developed Ancestry Composition that became the most comprehensive ancestry inference product available to the public, before joining Novartis in 2015.