AI-driven DMTA cycles transform pharmaceutical R&D

ACD/Labs has released a new two-part white paper series exploring how AI-enabled design-make-test-analyse (DMTA) cycles are reshaping pharmaceutical and biotech research. By integrating AI with laboratory experiments and digital workflows, organisations can overcome fragmented processes, reduce manual data handling, and accelerate decision-making across drug discovery and development.

The first paper focuses on drug discovery, highlighting how digital twins and AI can streamline lead optimisation, reduce the burden of manual synthesis design, and improve experimental decision-making. By unifying design, synthesis, testing, and analysis into a single digital-physical cycle, researchers can identify viable clinical candidates faster while maintaining scientific rigour.

The second paper examines pharmaceutical development, with a focus on Chemistry, Manufacturing, and Controls (CMC). AI-augmented DMTA cycles support quality by design principles, enable iterative optimisation through design of experiments and Bayesian approaches, and improve reproducibility in drug substance and product formulation. By creating structured, machine-readable data from experiments, organisations can enhance regulatory readiness and shorten development timelines.

“The scientific method is being redefined,” said Andrew Anderson, Vice President of Innovation and Informatics Strategy at ACD/Labs. “Even organisations that have embraced digitalisation often operate in fragmented environments that rely on manual data transfer. AI-digital-physical DMTA cycles give researchers more control, reduce errors, and accelerate the path from insight to clinical application.”

The series showcases best practices from leading R&D organisations, emphasising how automation, informatics, and AI can unlock faster, more cost-effective innovation. By digitalising the physical DMTA cycle, pharmaceutical scientists can work more efficiently, collaborate seamlessly, and focus on higher-value scientific questions.

The white paper series, AI-Digital-Physical Convergence: The Future of DMTA in Drug Discovery and Development, is available for download.

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