West Coast Augmented Intelligence in Drug Discovery Xchange
San Francisco
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

Data Quality

Time
Titles and Bullets
Facilitator
8:00 – 8:30am
Registration
8:30 – 9:00am
Opening Address & Keynote Presentation
Finding a needle in the haystack: Using AI to increase the probability of technical success in drug development

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.  

Pam Bush Headshot
9:05 – 10:05am

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.

dana caulder headshot
10:10 – 10:40am
1-2-1 Meetings / Networking Break
10:40 – 11:10am
1-2-1 Meetings / Networking Break
11:10-11:20 am

Morning Refreshments

11:20 am-12:20 pm

Building and maintaining quality data sets for cutting-edge analytics

  • The central role of ontologies in data harmonization efforts
  • Semantic enrichment of unstructured data sources
  • Finding meaningful information and relationships in free-text documents
  • Data quality requirements and gold set creation for advanced analytics and ML model training
  

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.

image001
12:20-1:20 pm

Networking Lunch

1:20-1:50 pm
Spotlight Presentation:
Closed-loop drug discovery: Combining wet lab automation and Machine Learning for accelerated program progression from target to hit, lead, and candidate
 
  • 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
Drug discovery is powered by the Design-Make-Test-Analyze cycle. Right now, cycle times in the industry are long, and only a fraction of drug-like chemical space is being evaluated, with suboptimal chemical design choices driving clinical failure. Computational approaches can help, but availability of and access to high-quality data is the bottleneck. Combining wet lab automation with Machine Learning allows us to close the loop, with rapid data generation powered by robotics synergizing with a more efficient exploration of a wider chemical space. Taken together, we can significantly accelerate the progression from target to high-quality hit, lead, and candidate molecules towards the clinic. 

CEO, Arctoris

Martin-Immanuel Bittner MD DPhil FRSA is the CEO of Arctoris, a biotech company combining automated wet lab operations and machine learning for accelerated small molecule discovery. Martin graduated as a medical doctor in Germany, followed by his DPhil in Oncology as a Rhodes scholar at the University of Oxford, where he co-founded Arctoris. He has extensive research experience covering both clinical trials and preclinical drug discovery, is an elected member of the Young Academy of the German National Academy of Sciences and of Sigma Xi, and has been named Innovator of the Year in Biotechnology 2020 by SBR.
Presentation title: 
 
Closed-loop drug discovery: Combining wet lab automation and Machine Learning for accelerated program progression from target to hit, lead, and candidate
Martin-Immanuel Bittner

3:00-3:30 pm

1-2-1 Meetings / Networking Break

3:30-4:00 pm
1-2-1 Meetings / Networking Break
4:00-4:10 pm
Afternoon Refreshments

4:10-4:40 pm

 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.  

Abhishek Jha (1)

5:45-6.45 pm

Closing Address & Canape/Drinks Reception

Partners

Augmented Intelligence in Drug Discovery Xchange | US West Coast 2022
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