Interactive Breakout Discussions

Engage in in-depth discussions with industry experts and your peers about the progress, trends and challenges you face in implementing ML/AI in your work! Interactive discussion groups play an integral role in networking with potential collaborators, provide an opportunity to share examples from your work, and allow you to be part of a group problem-solving endeavor.

These will take place IN-PERSON ONLY.

Machine Learning in Early Discovery Track

The Transition of Experimentalists into a Computational Paradigm in Pharmaceutical R&D
Qing Chai, PhD, Executive Director, Eli Lilly & Company

• Addressing skills gaps
• Benchmarking progress compared with traditional structures
• Best practices for collaboration between experimentalists and data scientists
• Examples of successful transitions
• Implementing models/tools and workflows based on AI/ML approaches

Training Data Generation and Quality Track

Internal Data Generation and Curation
Kevin Metcalf, PhD, Senior Scientist, Merck

• Amplification strategies
• Avoiding bias
• Closed-loop experimentation
• Controls and validation
• Dealing with skewed data
• Historical data

Machine Learning in Biologic Drug Discovery: Leveraging External Data Sources
David Noble, Data Scientist, A-Alpha Bio

• Quantity: Availability challenges, scaling laws, synthetic data
• Quality: Diversity, leakage, reproducibility, quality vs. quantity
• Collaborative data generation: Industry-academia partnerships, data sharing consortia
• Federated learning: Technical challenges, open-source foundation models
• Intellectual property: Data ownership, balancing openness with commercial interests
• Open-source data: Curation quality, integrating diverse sources with proprietary data

Predicting Developability and Optimization Using Machine Learning Track

AI/ML-Driven Design of Conditionally Active Molecules
Hunter Elliott, PhD, Senior Director, Machine Learning, BigHat Biosciences

• What is the therapeutic potential of conditional activity and how do we best balance this against increased complexity and risk?
• What challenges are unique to ML-driven design of conditional molecules?
• How does the optimal ML toolkit vary between conditional and unconditional design?
• How best can we overcome challenges in data acquisition and availability?
• What are the currently tractable forms of conditional activity and what can we envision for the future?​

Practical Impacts of Machine Learning on Biologics Preclinical Pipeline
Andrew B. Waight, PhD, Senior Director, Machine Learning, Discovery Biologics & Protein Sciences, Merck Research Labs

Applying AI to Improve Manufacturability and Developability of Multispecific Biologics
Mahiuddin Ahmed, PhD, President and CSO, VITRUVIAE
Jeffrey J. Gray, PhD, Professor & Research Mentor & Outreach Advisor, Chemical & Biomolecular Engineering, Johns Hopkins University

• Improving humanization and predicting immunogenicity
• Reducing off-target binding
• Predicting aggregation, viscosity, and excipient formulation
• Combining targets for improved efficacy

Models for de novo Design Track

How Open Competitions Provide Valuable Benchmarking to Novel Technologies
Andrew R.M. Bradbury, MD, PhD, CSO, Specifica, Inc., a Q2 Solutions Company
Matthieu Schapira, PhD, Principal Investigator, Structural Genomics Consortium, Professor, Pharmacology & Toxicology, University of Toronto

• Why benchmarking is needed
• Designed competitions, and accidental ones
• Lessons from CACHE
• The AIntibody competition to assess computational methods in antibody discovery​

The Use of Tools for Building Gene Editors for Going Beyond Proteins
Jeffrey Ruffolo, PhD, Head of Protein Design, Profluent Bio

AI-Driven Biologics: Accelerating Discovery, Overcoming Challenges
Per Greisen, PhD, President, BioMap

• Motivation: The urgent need for novel biologics is driving the exploration of AI in drug discovery
• Focus: AI's potential in accelerating biologic drug discovery, particularly de novo antibody design
• Showcase: Successful AI-driven VHH and mAb designs
• Discussion: AI's strengths in predicting antibody structures, challenges in translating designs into functional molecules, achieving industrial-scale reliability, and closing the gap between computational and experimental results