Reaction Lab

Ditch guesswork and optimize reactions with confidence. Reaction Lab leverages science to streamline development.

• Gather Data: Run your reaction and collect time course data using HPLC, FTIR, NMR, or calorimetry to understand reactant/product changes.
  
• Define Mechanism: Utilize your chemical intuition and data analysis tools to input a reaction mechanism.
• Build Model: Using the data and mechanism, use Reaction Lab’s fitting tools to determine if your data matches your proposed mechanism.
• Predict and Optimize: Use simulation and optimization tools to explore different conditions (temperature, pressure, ratios) in silico and to identify optimal settings for yield, purity, etc.
• Scale-up with Confidence: Predict performance at scale before ever setting foot in the plant. Significantly increases your chance of a successful first-time scale-up, saving you time, resources, and frustration. 
• Expand Beyond Batch: Reaction Lab isn't limited to simple batch processes. Use Reaction Lab to intensify your process, a model reaction performance in fed-batch and flow processes.

Reaction Lab empowers you to move beyond trial and error for efficient reaction development.

Through kinetic modeling, Reaction Lab offers an in-depth understanding of how your reaction conditions influence reaction outcomes. This characterization can reveal new opportunities for process optimization which can be run in silico and verified in the lab.

Optimize Reaction Parameters:

• Maximize yield, control impurities, minimize cost, and increase robustness.
• Explore the impact of key parameters like reagent concentrations, temperature, reaction time, and feed rates.
• Perform multi-objective optimizations, balancing several key parameters to reach a robust and reliable process. 

Explore the Design Space with Confidence:

• Predict the impact of scale-up issues before they happen, such as reagent overcharging and missed temperature setpoints. 
• Analyze trade-offs between key parameters based on your process goals. For example, estimate the impact catalyst concentration has on reaction time. 
• Perform pass-fail design space analysis, to reliably predict conditions conditions for successful batches, ensuring critical targets are met.

Reaction Lab shines across a wide range of applications, empowering you to optimize a wide variety of reactions, such as:

• Hydrogenations/Carbonylations: Maximize yield and control impurities/colorant formation by determining the optimal pressure, temperature, and catalyst loadings.
• Coupling Reactions such as Suzuki, Buchwald-Hartwig: Maximize yield, minimize catalyst degradation, and control impurities (such as ArH)
• Peptide Chemistry: Predict activation kinetics to help understand and control partial activation, deletions, HCN formation, and racemization. 
• Biocatalysis: Accurately model enzyme kinetics and determine parameters such as Km, Vmax, kcat to describe and design efficient biocatalytic processes.
• S_NAr Reactions: Achieve precise control over impurities for targeted product formation.
• Slurry and Solid Liquid: Optimize these complex systems where mass transfer plays a critical role.

Beyond these examples, Reaction Lab tackles a vast array of chemistries and has been referenced in a wide number of peer-reviewed publications. 

Reaction Lab goes beyond basic reaction optimization. It empowers you to tackle complex reaction systems with ease:

• Designed for Chemists, Not Modelers: The user interface is built with chemists in mind. It offers easy unit conversion, smooth data import from HPLC and Process Analytical Technology (PAT) tools, and requires minimal modeling expertise.
• Effortless Mass Transfer Modeling: Seamlessly incorporate mass transfer limitations, like hydrogen dissolution or solid-phase reactions, into your model. This simplifies modeling of complex systems often encountered in real-world chemistry.
• Scalability Beyond DoE: Unlike data-heavy Design of Experiments (DoE) approaches, Reaction Lab leverages mechanistic modeling. This requires significantly less data and is inherently more scalable, allowing you to optimize reactions with just a few lab-scale experiments.

Reaction Lab empowers you to leverage the power of mechanistic modeling without becoming a modeling expert.