Day One

• Optimized AI generation achieves higher hit rates, nearly tripling computational design success and drastically slashing early-stage trial-and-error costs
• Wet-lab results confirm that our AI-designed sequences successfully fold and exhibit real catalytic activity, providing validated functional enzymes
• Sequence optimization significantly boosts antibody expression, thus improving production yields and directly derisking downstream manufacturing bottlenecks

This structured networking session provides the perfect opportunity to connect with industry frontrunners and key opinion leaders working at the interface of AI/ML and computational drug design for biologics. Establish meaningful connections to build upon at the rest of the conference.

• Demonstrating that miniproteins are a powerful, stable therapeutic modality with high-affinity, specific binding
• Discussing development using a combination of de novo design, synthetic biology, and laboratory automation
• Creating a toolbox of modular miniprotein domains with ideal drug-like and developability profiles

• Discussing epitope clustering using the latest structure prediction tools
• Exploring agreement with experimental results
• Proposing a strategy on how such results could benefit labs in prioritizing candidate measurement

• Developing an end-to-end AI-MPRO platform to design miniprotein therapeutics against challenging targets, including ion channels and complex multi-protein interfaces that are difficult to address with conventional modalities
• Combining de novo protein design with rapid in silico experimental iteration, enables efficient progression from computational discovery to in vitro and in vivo preclinical proof-of-concept
• Generating compact, highly optimized miniprotein candidates with the potential to deliver first-in-class or best-in-class therapies for targets traditionally considered intractable

• Shifting from screening-based discovery towards biologics by design through AI momentum and applications
• Exploring how real impact today is accelerating design-make-test cycles and reducing experimental iteration
• Recognizing that success requires integrated AI platforms that combine generative models, predictive models, and experimental feedback

• Exploring how a de novo protein was designed to target a heterodimeric receptor in the absence of a solved structure
• Achieving biased agonism with a de novo protein
• Emphasizing the importance of a human-in-the-loop AI/ML pipeline for biologics design

• Exploring how an adversarial generation engine with continuous in silico evaluation dynamically steers de novo miniprotein design to improve multi-objective hit rates
• Discussing the use of labelled design and click chemistry–based modular assembly to enable efficient scaling from single miniproteins and VHHs to multispecific constructs
• Identifying informative labels using model heuristics to bias generative sampling towards optimized affinity, specificity, stability, and immunogenicity profiles

Connect with peers in a relaxed atmosphere and continue to forge new and existing relationships, while exploring the latest advancements at the intersection of computation and AI for biologics design.

To submit a poster, please contact: info@hansonwade.com