Exploring an AI-Native Approach to Antibody Discovery

Artificial intelligence (AI) and machine learning (ML) are poised to significantly accelerate antibody discovery, but their impact depends on thoughtful integration into scientific workflows. For AI to deliver meaningful results, it must be underpinned by high-quality, structured data and a deep understanding of discovery workflows.

Hear from expert Dr. Jana Hersch, as she shares how an AI-native framework seamlessly integrates experimental, processed, and in silico data enabling real-time insights across diverse antibody modalities, including bispecifics, multispecifics, and antibody drug conjugates (ADCs). Learn how this platform can bring more clarity, speed, and intelligence to antibody discovery by supporting biopharma organizations in realizing the full potential of AI and ML in their R&D pipelines.

Key learning objectives:

• Learn new ways to integrate AI into antibody discovery workflows, including bispecifics, multispecifics, and ADCs.
• Explore approaches for unifying experimental, processed, and in silico data to support global research teams.
• Discover how agentic systems can interrogate complex datasets sourced from diverse systems.

Who should attend?

• Antibody and biologics R&D leadership
• Scientific and technical experts
• Data and digital specialists