West Coast Augmented Intelligence in Drug Discovery Xchange
30 September, 2022
Welcome to hubXchange’s West Coast Augmented Intelligence (AI) in Drug Discovery Xchange 2022, 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.
NOTE: This will be an In-Person event
VENUE DETAILS: DoubleTree by Hilton San Francisco Airport Hotel, 835 Airport Blvd., Burlingame CA 94010-9949
Hindsight is said to be 20/20. What if we could bring future clinical insight into today’s pre-clinical stages? What if patient-drug response data combinations were used to inform development decisions? What if we could more effectively find that needle in a haystack?
The anticipated variation of patient heterogeneity plays a significant role in the complexity and efficacy of the drug development process. This presentation provides a vision for how to leverage AI to bring valuable and relevant patient-centric information into the pre-clinical drug development process stages to increase the probability of success.
SVP, Predictive Oncology
Pamela A. Bush, Ph.D., has over 20 years of experience in venture creation, finance and business development in the life sciences industry. Dr. Bush is the Senior Vice President of Strategic Sales and Business Development at Predictive Oncology (POAI) where she leads activities across the portfolio. Prior to joining POAI, Dr. Bush worked at Eli Lilly & Company in various functions including corporate business development, finance and patient services. In addition, she has worked in business consulting and economic development supporting the creation and growth of 80+ life sciences start-ups. Dr. Bush holds a Ph.D. in Molecular Biology and an MBA from the Tepper School of Business at Carnegie Mellon University.
The challenges of inefficient data integration – what methods can be used to ensure the integration of more data?
Share examples where data integration challenges are getting in the way or holding us back
Explore both technical and human approaches to solve these challenges
What key challenges have you faced, and solutions you’ve found?
Thoughts on green field vs. evolutionary approaches to achieve data integration – and how to deal with legacy and technical debt
Executive Director of Research Informatics & Software Engineering, Genentech
Dana leads the Research Informatics & Software Engineering department within Genentech Research & Early Development (gRED). Her team of engineers, scientists, business analysts and project managers develop, implement and support informatics solutions that enable drug discovery and development processes within Genentech Research and across its interfaces. Dana received her PhD in Chemistry at UC Berkeley and completed a postdoc at Lawrence Berkeley National Lab. Dana joined Genentech in 2008, and every day works with incredibly talented and passionate people to solve hard problems to make a difference in people’s lives.
Building and maintaining quality data sets for cutting-edge analytics
Technical Consultant, SciBite
Thomas (Tom) Woodcock is a Technical Consultant with SciBite and former Data Science Consultant in Elsevier’s Professional Services group. Tom is an accomplished data scientist offering 20+ years’ experience in biological and pharmaceutical science. He brings specialist scientific domain experience together with comprehensive data science skills to design and implement both large and small scale projects for our customers. His skills include predictive modeling, data mapping, competitive analysis, and data analysis. In this capacity he leverages a variety of SciBite technologies as well as SQL, PHP, Java, Python, KNIME, and data mining. Tom holds a Ph.D. in Pharmaceutical Sciences University of Kentucky, and a Master of Science in Molecular And Cellular Biology University of Bath.
- Drug discovery is powered by the Design-Make-Test-Analyze cycle
- Right now, cycle times are long, and only a fraction of drug-like chemical space is being evaluated
- Combining wet lab automation with Machine Learning allows us to close the loop, with rapid data generation synergizing with a more efficient exploration of a wider chemical space
- This enables an accelerated progression from target to high-quality hit, lead, and candidate
1-2-1 Meetings / Networking Break
Poster presentation “De-Risking” Predictive ML with Indisputable Data Quality
- Predictive models used in drug discovery require a viable level of data quality. A faulty model can lead to completely off-the-mark predictions and sunk project costs.
- In sharp contrast, much of the available biomedical data is unstructured and prone to errors due to varying experimental protocols (incomplete metadata information, missing annotations, inconsistent file formats)
- To ensure their datasets are ML-Ready, R&D teams must set up a system that continuously assesses and iterates on the data and metadata quality.
- This session will demonstrate Elucidata’s data quality assessment approach, which ensures an input dataset is standardized, and has accurate, complete and a breadth of metadata information before it is considered model quality.
CEO & Co-Founder, Elucidata
Dr. Abhishek Jha was an early member of the platform team at Agios Pharmaceuticals and supported multiple drug discovery programs, two of which have been approved by the FDA. As a founder of Elucidata, he is committed to building a transformative biotech company for the future that will provide clean and linked machine learning-ready data at every stage of drug discovery.
Closing Address & Canape/Drinks Reception