Self-Driving HTE Labs for Synthetic Molecule Process Development
Takeda SMPD has significantly built up its high-throughput (HTE) and automation capabilities over the last few years around the Unchained Labs family of tools and devices that integrate well into a complete solution. Our ultimate vision is to design and implement self-driving labs (SDLs) to carry out self-optimizing workflows. Process development and optimization frequently involves exploring wide parameter spaces, making these iterative algorithm-guided SDLs ideal for such optimizations, especially if coupled with automated HTE synthesis platforms.
As such, we herein present our vision for fully self-optimizing HTE workflows, with the ultimate goal of creating SDLs with workflows optimized for a variety of general applications needed in synthetic molecule drug development. To illustrate this process, case studies will be presented, highlighting both their successes, as well as the limitations of our current hardware and software tools.
Key Learning Objectives:
- Creating a successful Self-Driving Lab
- Impacts on research through case studies
- Future directions for improvement
Who Should Attend:
- Laboratory managers/directors
- Synthetic chemists
- Self-driving lab enthusiasts
Presenter: Adrian Ramirez Galilea, PhD (Associate Director, Automation & HTE, Takeda)
Presenter: Andrew Kukor, PhD (Automation Scientist, Takeda)
Presenter: Dimitrios Chatzinikolis (Robotics Engineer III, Takeda)
Presenter: Rick Sidler, PhD (Director, Field Automation Scientists, Takeda)
Presenter: Kelly McSweeney (Contributing Editor, C&EN Media Group)
