Exploring an AI-native approach to antibody discovery
Thursday, December 11, at 16:00 GMT | 17:00 CET | 11:00 EST | 08:00 PST
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.
Who should attend?
- Antibody and biologics R&D leadership
- Scientific and technical experts
- Data and digital specialists
Certificate of attendance
If you attend the live webinar, you will automatically receive a certificate of attendance, including a learning outcomes summary, for continuing education purposes.
If you view the on-demand webinar, you can request a certificate of attendance by emailing editor@selectscience.net.
Webinar details
- Cost: Free to attend
- Location: Online
- Duration: 60 minutes
Registration is required to secure your place. If you register but can’t attend live, you will receive a link to the on‑demand recording once it becomes available.
Speakers
What will this webinar cover?
- 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.
Join the webinar to get answers to these questions:
- How can AI and ML be practically integrated into antibody discovery workflows, including bispecifics, multispecifics, and ADCs?
- What strategies help unify experimental, processed, and in silico data across global research teams?
- How does an AI-native framework enable real-time insights from complex antibody datasets?
- In what ways can agentic systems interrogate data from diverse discovery systems and tools?
- How can this platform increase clarity, speed, and decision-making intelligence in biopharma R&D pipelines?