Main Conference Day 1 - PT (Pacific Time, GMT-08:00)
Next-gen immunotherapies demand seamless integration of multimodal data—sequence, structure, assay, and biophysical insights. Traditional tools can’t keep pace. This talk introduces a new paradigm: a Multimodal Scientific Intelligence Platform built to unify antibody/protein workflows, enhance collaboration, and accelerate AI-ready discovery. Includes a case study from a major biopharma showing how multimodal workflows improve outcomes in multispecific antibody engineering.
- Christian Olsen - Vice President, Strategy - Protein Therapeutics, Luma, Dotmatics
Traditional antibody discovery approaches often prioritize single objectives, failing to balance multiple properties simultaneously which yield candidates with compromised developability profiles. We present a selection framework using Pareto optimization across rank-normalized scores with hierarchical property classification. This approach generates balanced candidate shortlists with AI-assisted explanations of property trade-offs, enabling efficient identification of optimal molecules for validation while reducing costly experimental iterations and accelerating therapeutic antibody development.
- Kemal Sonmez, PhD - Senior Applied AI Scientist, Amazon Web Services
Artificial intelligence (AI) is transforming antibody discovery and engineering. Ailux's platform synergistically combines the best of our comprehensive wet lab, AtlaX biologics database, and three proprietary AI engines. We will explore our latest case studies that exemplify our AI-driven approach for tackling challenging targets, identifying unique functional antibodies, and achieving multi-objective optimization. This presentation provides our realistic and evidence-based perspective on the impact of AI on developing next-generation antibody therapeutics.
- Barry Duplantis, PhD - Director of Global Business Development, Ailux