互动式分组讨论

与产业专家和同业者深入讨论实施 ML/AI 的进展、趋势和挑战。互动讨论小组在与潜在合作者建立人脉方面发挥着不可或缺的作用，提供了分享研究实例和小组解决问题的机会。

互动式分组讨论仅以面对面方式进行。

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