Enabling AI in Biopharma—Closing the Wet Lab/Dry Lab Loop

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: