What to Expect at the CDD & AI for Biologics Forum?
Rapid advances in foundation models, protein design and multi-objective optimization are reshaping how biologics are discovered, optimized and derisked. However, translating the output from exciting AI/ML models into scalable, experimentally validated impact, remains the industry’s shared and defining challenge.
The CDD & AI for Biologics Summit convenes the community at this critical inflection point. Collaborate with techbio pioneers, biotech innovators and pharma pacesetters to explore how the convergence of AI/ML and computational approaches are being operationalized across de novo biologics design, property prediction and multi-objective optimization, with insights spanning straightforward binders, such as miniproteins, through to complex biologics including bispecifics, multispecifics and ADCs.
Across applied, data-backed use cases and wet-lab validated learnings, gain the confidence to advance AI-enabled biologics from discovery to lead optimization, through derisked CMC and towards patients quicker.
What Sets This Meeting Apart?
Understand how to deploy AI/ML for de novo biologics design and property prediction with Ordaos Bio, Takeda and VRG Therapeutics
Leverage CDD and AI/ML to predict and optimize leads for developability parameters, and identify liabilities with Insilico Medicine, AbbVie and Regeneron
Investigate AI/ML model use cases for complex biologics design across multispecifics and conjugated drug products with insights from GSK and 3T Biosciences
Supercharge molecular dynamics approaches with AI/ML models to reflect the folding energetics of biologics with Genentech and Amgen
Deep dive into data readiness and lab automation for AI-enabled biologics design, discovery and optimization with AI Proteins, insitro and the Institute for Protein Innovation
Gain a strategic overview of recent industry investments, data foundations and the IP landscape of AI/ML use cases for biologics discovery and design with insitro and UCB
Attending Companies Include