Realizing the full potential of AI/ML in life science R&D depends on bridging a persistent divide between wet lab experimentation and dry lab modeling. Fragmented data systems, manual handoffs, and a lack of automation restrict access to high-quality and contextualized data for model training and fine tuning. This creates a barrier preventing wet lab scientists from testing predictive insights from computational workflows.
In this webinar, Milton Yu, head of automation & analytics strategy, and Sandy Li, head of scientific AI/ML market strategy at Benchling, will discuss how to build AI-ready data foundations to close the wet-lab/dry-lab loop. They will demonstrate how Benchling’s products transform raw instrument outputs into structured, contextualized, and analysis-ready data that can seamlessly feed into AI models.
Additionally, attendees will learn how to make data flow bi-directionally between the bench and computational models, to allow experimental scientists to more easily adopt and test AI-driven hypotheses while maintaining familiar workflows.
In this webinar, you will learn:
- Requirements for automating the generation of AI-ready wet lab data
- How to embed dry lab models directly into experimental workflows
- Practical approaches for creating a continuous wet-lab/dry-lab feedback loop
- Lessons from biopharma organizations advancing AI-driven R&D with Benchling
A live Q&A session will follow the presentations, offering you a chance to pose questions to our expert panelists.
Produced with support from:
