NexusXp: Advanced DMTA Strategies for the Modern Lab

Lab automation has moved beyond simply replacing manual tasks; it's now about creating intelligent, self-optimizing research engines. This advanced course is for experienced professionals who want to lead the charge and explore what's next for the modern lab. We'll move past the basics and focus on how to supercharge the DMTA cycle with cutting-edge technology and forward-thinking strategies.

We'll delve into practical applications of AI and machine learning for predictive modeling and smarter experimental design, transforming the DMTA loop into a system that learns and improves on its own. We will also tackle the tough challenges of integrating complex data and workflows to build a truly seamless "lights-out" lab environment. A key part of the session will focus on the human element, exploring how to best leverage scientific expertise in a lab driven by intelligent automation.

Who Should Attend?

This course is designed for:

• Experienced automation scientists, group leaders, and directors making strategic decisions.
• Innovators and thought leaders responsible for the technological direction of their organizations.
• Professionals focused on integrating AI and Machine Learning into their labs' workflows.
• Anyone tasked with designing and implementing complex, fully integrated automation systems.

Course Benefits

After this course, you'll be able to:

• Strategically apply AI and ML to optimize every stage of the DMTA cycle.
• Design high-level plans for integrating different data sources, scientific workflows, and instruments into one seamless system.
• Evaluate advanced hardware, software, and system architectures to build a next-generation "Lab of the Future."
• Develop a clear strategy for the "human in the loop," defining where expert input adds the most value in a highly automated lab.
• Analyze emerging trends like cloud labs, digital twins, and generative AI to anticipate their impact on drug discovery.

Course Topics

• Optimizing the DMTA Cycle with AI and Machine Learning
• Predictive Modeling and Automated Experimental Design
• Advanced Strategies for Data and Workflow Integration
• Building the "Lab of the Future": Advanced System Design
• The Role of the "Human in the Loop" in Advanced Automation
• Panel Discussion on the Future of Drug Discovery

Instructors

Instructor info will be added shortly.