Webinar

DMTA Innovation at Takeda—Digitalized, AI-Augmented, Autonomous

Optimization of chemical process development means exploring complex parameter spaces. Traditional manual approaches are time-consuming, tedious and low throughput.

Discover the next generation of AI-driven, digitalized design, make, test, analyze (DMTA) cycles in pharmaceutical R&D with ACD/Labs and Atinary SDLabs™ platform.

Presented By

Adrian Ramirez Galilea

Associate Director of Automation & HTE, Takeda

Hermann Tribukait

Co-Founder & CEO, Atinary Technologies Inc.

Alexander Waked

Katalyst D2D Product Coordinator, ACD/Labs

Enabling Self-Driving Labs for Synthetic Molecule Process Development

Takeda has invested significant time and effort in digitalizing and automating R&D. In synthetic molecule process development (SMPD) this involved building up high-throughput experimentation (HTE) and automation capabilities to accelerate process optimization.

Join Adrian Ramirez Galilea, Associate Director of Automation & HTE at Takeda, as he shares his experience and insights. He will discuss:

How experiment time has been reduced from weeks to days (or minutes) with integrated systems that enable data flow and experiment execution with minimal human intervention.
The importance of automating analytical data processing for a seamless feedback loop.
Generating high-quality, reproducible, AI-ready, FAIR data from consistent, repeatable experiments.
Designing standardized workflows suited for self-driving labs.
Takeda’s vision and progress towards a 24/7 autonomous lab.

An Integrated Environment for AI-enabled, Autonomous HTE

Atinary’s SDLabs (Self-Driving Labs®) is a no-code platform for AI-driven experimentation to accelerate R&D. Katalyst D2D® software from ACD/Labs offers end-to-end workflow and data management for high throughput experimentation (HTE). Learn how integration of these informatics solutions is helping make autonomous labs a reality.

Other Resources

Case Study

Facilitating AI/ML in Chemistry R&D

How pharmaceutical researchers are using HTE and Katalyst D2D to realize AI/ML goals. From those that see the potential for building robust models with data managed with Katalyst; successfully building AI/ML-friendly datasets; to leveraging integrated ML-powered experiment design to optimize reactions with 50% fewer experiments.