Automated executions of chemical synthesis and discovery has risen as a critical enabling technology. New tools combining advanced robotics with experiment planning by machine learning, known as self-driving labs, are now attainable. However, the optimal deployment of these technologies remains under development, requiring a recursive design-make-build cycle dedicated to tuning and evolving the robotic platform.
This new approach has been termed â€˜flexible automationâ€™ and enables the cost and time-effective design, construction and reconfiguration of automated experiments. By leveraging many of the principles from Industry 4.0, including rapid fabrication, Internet-of-Things devices, cloud-based software, and open-source API we have created a set of simple reconfigurable tools to address a diverse set of applications spanning pharmaceutical process chemistry and even impacting mining and resource extraction. These rapid improvements are made possible through the fusion of best-in-class existing hardware (eg, Unchained Labs Junior for liquid handling and HTE, Mettler-Toledo EasyMax for large scale automated lab reactor, Agilent UPLC for online analysis, Vaportec R-Series for continuous plug-flow reactions) with a variety of bespoke tools and code to integrate complicated workflows. This presentation will introduce this method and provide case studies.
Key Learning Objectives:
- Learn strategies to rapidly develop hybrid and fully automated experimental workflows
- Learn best practices to design and deploy autonomous reaction optimization for chemical synthesis
- See case studies for integrating automation into small molecule synthesis programs
Who Should Attend:
- Process development and medicinal chemists
- Automation scientists and engineers
- Scientists involved in high throughput experimentation