Richard Taylor

• Digital antibody design is advancing rapidly, and we will highlight the momentum building across the biotech industry over the past 20 years
• We draw lessons from the more mature field of computational small-molecule design and suggest that hybrid AI and physics-based workflows offer a promising path toward robust, transferable antibody design
• Real progress will depend on rigorous experimental validation of structure, function, developability and generalizability

To kick off, this industry leaders panel will discuss the computational x AI x biologics industry collaborations and advancements shaping the space over the last year, key challenges to be overcome, and identify the biggest opportunities based on industry trends.

• Taking a strategic SWOT analysis approach, what are the strengths, weaknesses, opportunities and threats to AI/ML-derived biologics?
• What advancements in AI/ML and computational tool convergence should we be most excited about when it comes to biologics design and optimization?
• What are the technical or scientific bottlenecks that are slowing down computational x AI x biologics design and optimization?
• Reflecting on the last year, what have been some of the most exciting industry collaborations that have given momentum to the computational x AI x biologics space?
• What can we learn from these collaborations in terms of where the space is heading?