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 drug discovery using AI to deliver meaningful results, it must be grounded in high‑quality, structured data, strong AI data integrity, and a deep understanding of discovery processes. Ensuring FAIR data, robust AI data governance, and adherence to ALCOA+ principles is essential for building trustworthy systems in modern AI biopharma research and development.
Join expert Dr. Jana Hersch as she shares how an AI‑native framework seamlessly integrates experimental, processed, and in silico data to enable real‑time insights across diverse antibody modalities, including bispecifics, multispecifics, and antibody‑drug conjugates (ADCs). This approach demonstrates how AI in scientific discovery and AI in scientific research can bring greater clarity, speed, and intelligence to complex biologics pipelines.
You’ll also learn how scalable AI cloud solutions and next‑generation AI in biopharma platforms support organizations in realizing the full potential of AI drug discovery by unifying data, enhancing decision‑making, and enabling more predictive, automated R&D environments.
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 and ensure FAIR, high‑integrity datasets.
- Discover how agentic systems can interrogate complex datasets sourced from diverse systems, powered by strong Data governance AI foundations and scalable cloud‑native architectures.
Who should watch?
- Antibody and biologics R&D leadership
- Scientific and technical experts
- Data and digital specialists
