Augmented Intelligence in Drug Discovery Xchange
East Coast 2021
May 18 & 20
Welcome to hubXchange’s East Coast 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.
- Augmented Intelligence: emerging realism after the hype?
- Eroom’s Law: the ever pressing need for data driven drug development
- Data quality and machine readability: the elephant in the AI room?
- Collaborative Intelligence: evolving towards ever closer human-machine interaction
Arie Baak is one of the co-founders of Euretos (est. 2012), an AI product & services company that enables data driven disease and drug research. Before moving into life sciences, Arie worked in various strategic innovation roles in the mobile telecoms and internet infrastructure markets. In these high performance/data volume environments he got deeply involved in generating actionable insight to end users long before the term ‘big data analytics’ became fashionable. For the past 10 years, Arie has been applying this expertise to enabling data driven disease and drug research working with many of the world’s leading pharma, biotech and academic institutions.
- 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.
- Explore the effective, and realistic approaches to annotate and share data; define data quality aspects for ML applications
- How do we best utilize and interpret data of quality?
- Discuss why executing data quality is a continuous process
- Share experiences in tackling data quality challenges
Co-Founder & CEO
Bryce Allen is the Head of Integrated Data Sciences, leading the data analytics and machine learning team. Bryce has contributed significantly to the company in several key areas, including conformational genetics, free energy calculations, uncertainty quantification, de novo design, and database infrastructure. Before joining SiTX, he was a Postdoctoral Fellow in Biomedical Informatics at Harvard Medical School, where he worked on clinical trajectory modeling in Inflammatory Bowel Disease. Additionally, during his PhD training, Bryce worked as a Biomedical Data Scientist at the Library of Integrated Network-based Cellular Signatures (LINCS) Data Coordination and Integration Center and helped lead the architecture and development of data storage, query, and analysis infrastructure. He also developed custom ETL processes and machine-learning models for chemical and genomic entities used in the LINCS Consortium.
- Cause inference can be a powerful way of discovering novel biological insights around targets, biomarkers and indications
- Cause-and-effect relationships can be difficult to ascertain, yet remain abundant within the literature and biomedical text
- In this talk I will show how this data is captured by Galactic AI and can be used to reveal hidden biomedical insights to empower better decisions in early stage drug discovery
CEO and Founder
Daniel is the CEO and founder of Biorelate. Daniel founded Biorelate during a PhD in computational biology at the University of Manchester, after supporting the successful identification of drug repurposing opportunities with Pfizer. Daniel’s focus remains on growing Biorelate into a world leading enterprise, helping pioneering companies in their mission to develop life-saving innovations.
- Share experiences in data curation
- What are the best methods in facilitating data integration for ML application?
- Discuss how data bias can be lessened via parallel analysis of omics data and how can we achieve this?
- Explore the most efficient application integrated data for drug discovery
Senior Scientist, Bioinformatics
Eisai Center for Genetics Guided Dementia Discovery (G2D2)
Dr. Zhang have a Ph.D in Computational Biology from Washington University in St. Louis. He is experienced in multi-omics data integration and is currently the data science lead at Eisai G2D2 research unit.
1:00 – 1:30pm
Combining AI with Robotics to Accelerate Project Progression from Idea to IND
- Drug discovery comes down to making a series of critical go/no-go decisions – which target to pursue, which molecule to select for further testing, etc. The main bottleneck in conventional and in AI-augmented drug discovery is access to reproducible, well annotated, fully structured wet lab data that powers these decisions
- Explore how we combine our partners’ AI with our robotic platform in Oxford, employing end-to-end automation of entire drug discovery processes to generate and capture 100x more datapoints per assay for better and faster decision-making
- In addition to a look under the hood of the unique technology platform developed by Arctoris, the session will also highlight success stories from biotech and pharma companies (including several of the world’s leading AI drug discovery companies) in the US, Europe and Asia-Pacific who already work with Arctoris to accelerate their work
1:40 – 2:40pm
- Discuss how to balance data obfuscation with data utility for meaningful data sharing
- Explore if organisations are over-valuing their data assets and preventing meaningful data sharing
- Explore the ideal composition of data-sharing consortiums and initiatives- larger pharma and biotech versus smaller start-ups