AUGMENTED INTELLIGENCE IN DRUG DISCOVERY XCHANGE
EAST COAST 2023
Boston
May 25, 2023
Welcome to hubXchange’s Augmented Intelligence (AI) in Drug Discovery Xchange East Coast 2023, 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 an In-Person meeting.
VENUE DETAILS: Hilton Boston Woburn Hotel, 2 Forbes Road, Woburn MA 01801
SNAPSHOTS OF DISCUSSION TOPICS
- Overcoming current limitations in data generation and management
- Guidance on standard practice in clinical genomic data generation and data analysis pipelines
- Use of AI in integrating multi-dimensional datasets for target discovery
- Using AI to make a complex drug discovery process smarter
- Molecular design cycle: computation-first approach
- Challenges and approaches to building deep learning models for antibody lead optimization
- Can machine learning methods provide insights and predictions for cancer drug response?
Full Xchange Agenda
Click on each track for detailed agenda
Data Quality
Addressing the challenge of integration of omics data [Topic TBC]
Group Lead Pharma Discovery, Abbvie
Networking Lunch
1-2-1 Meetings / Networking Break
1-2-1 Meetings / Networking Break
Guidance on standard practice in clinical genomic data generation and data analysis pipelines
- Vendor Qualification and fit for purpose assay selection
- Global regulatory guidance for CRO based data generation
- Integration of new technologies in clinical research
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Senior Scientist, Takeda
Afternoon Refreshments
Data quality, variability and integrity: how do we achieve it? [Topic TBC]
Director of Computational Biology, Immunitas Therapeutics
Target Identification
Use Of AI in integrating multi-dimensional datasets for target discovery
Associate Director, Computational Biology & Data Sciences, Lexicon Pharmaceuticals
Lakshmi Kuttippurathu is an accomplished interdisciplinary scientist with 15+ years of experience in Computational Biology. During her research career at Harvard MIT Health Science and Technology, Lakshmi developed a computational tool to study regulatory dynamics of transcription factors. As a postdoctoral fellow and later as a Faculty at Thomas Jefferson University, she contributed to the field of Liver regeneration and Neuroscience with focus on understanding regulatory network dynamics driven by perturbations, using Systems Biology approach. Currently, she is the Associate Director, Computational Biology and Data Sciences at Lexicon Pharmaceuticals, where she is spearheading the computational efforts on developing and implementing a strategy for preclinical target discovery.
13:55 – 14:25
15:35 – 16:35
Mining of literature using NLP techniques [Topic TBC]
Principal Scientist, Frontier Medicines
16:45 – 17:45
Disconnection between in vitro/in vivo models and patients [Topic TBC]
Data Scientist, Novartis
Lead Generation
How can artificial intelligence accelerate drug discovery and development
[Topic TBC]
Chief Data Officer, Relay Therapeutics
Using AI to make a complex drug discovery process smarter
Head of Data Science and Data Engineering, Dewpoint Therapeutics
Molecular design cycle: computation-first approach
Head of Data Sciences and Machine Learning, Psivant Therapeutics
Lead Optimization
09:05 – 10:15
Senior Principal Scientist, Sanofi
12:20 – 13:20
Networking Lunch
13:55 – 14:25
15:35 – 16:35
Collaborative drug discovery data, models, and application [Topic TBC]
Head of AI Platforms, Insilico Medicine
16:35 – 16:45
16:45 – 17:45
Challenges and approaches to building deep learning models for antibody lead optimization
Senior Scientist, Global Biologics Discovery, Abbvie
Drug Response Prediction
Can machine learning methods provide insights and predictions for cancer drug response?
Senior Director, Head of Data Science and AI, Early Oncology, AstraZeneca