Katalyst D2D®

Katalyst D2D is a scientific software application that digitalizes the design–make–test–analyze (DMTA) cycle. It integrates with software and hardware so researchers can design experiments, execute in connected third party automated platforms, test with analytical chemistry techniques, and analyze results. Katalyst provides data flow between steps so information can be managed in single system, improving efficiency and data continuity across R&D workflows.

Katalyst D2D supports the following types of automated and/or array-based experiments:

Chemical synthesis

• High-throughput (HT)/plate-based/array-based/parallel synthesis
• Reaction screening
• Reaction optimization for process development
• Scale-up synthesis (lab-scale and pilot-scale)

Materials or formulation studies 

• Solubility and solubility enhancement
• Excipient compatibility
• Physical form 
• Stability studies
• Physicochemical characterization
• Particle size analysis

It automates experiment setup, generates structured execution instructions (for machine-input and scientist use), and assembles results automatically. Katalyst eliminates manual data handling between workflows steps minimizing transcription errors, and consolidates results with the experiment for context. It allows scientists to focus on interpreting results and making next-step decisions.

Katalyst D2D connects experimental design, laboratory execution, analytical data, and results analysis within a single workflow. Scientists can define experimental parameters, generate and manage experiments, coordinate execution through connected laboratory automation, and automatically bring analytical results back into the experimental context. This creates a continuous workflow from design through analysis, supporting high-throughput experimentation, optimization, and faster decision-making.

Yes. Katalyst D2D supports DoE workflows as part of a broader experimental workflow. Scientists can define experimental factors and ranges, generate experimental designs, execute experiments, and analyze the resulting data within a connected environment. This allows DoE to be integrated with laboratory automation and analytical data, rather than requiring separate systems for experimental design, execution, and analysis.

Katalyst D2D produces structured, contextualized datasets that are suitable for machine learning workflows. By standardizing experimental data and metadata, it enables more effective model development, integration with AI tools, and reproducible analysis.

ELNs and LIMS serve as systems of record—ELNs for documenting experiments and LIMS for managing samples, tests, and results with a focus on traceability and compliance. While Katalyst D2D is sometimes described as an ELN for HT or process chemistry, it goes beyond traditional ELN capabilities. It is designed to support end-to-end workflows, enabling scientists to design, execute, and analyze experiments in a connected environment. Katalyst complements these systems by structuring experimental data for faster iteration, optimization, and decision-making.

Katalyst D2D integrates with electronic lab notebooks, analytical instruments, lab automation platforms, AI/ML models and software, in-house databases and systems (chemical inventory, registration systems, etc.), enterprise data systems, and more. It enables seamless data flow across experimental workflows and reduces the need for manual data transfer between systems.

Katalyst D2D supports both client-hosted cloud (e.g. AWS) and on-premises deployment and is designed to scale across teams and sites. It enables collaboration and standardized workflows across organizations with varying IT and data governance requirements.