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AI Co-Pilot for Chemical Process Optimization
Our Mission: We aim to accelerate and automate chemical process development by equipping wet-lab chemists with the power of data-driven optimization and robotic execution of experiments. The Problem: The discovery of novel pharmaceuticals is one of our most important weapons in fighting disease. However, the drug development pipeline is often held up for many months during the design of chemical processes to manufacture these drugs at scale, delaying FDA trials and lengthening the time until drug launch. Designing chemical processes involves the identification of suitable parameters such as catalyst/temperature/solvent. Currently process development is often done via tedious trial-and-error experimentation (slow) or exhaustive screening (expensive and wasteful). Our Approach: In our research, we have developed algorithms for chemical process optimization, which leverage transfer learning and Bayesian optimization. We validated the algorithms in the wet lab, showing an up to 95% reduction in required experiments and cost compared to exhaustive screening. We have made our approaches accessible to chemists through our user-friendly no-code software platform and to automated laboratory equipment with our API.
ReactWise is an AI copilot for chemical process optimization that uses data-driven methods to accelerate reaction development. It offers machine learning guidance, descriptor-based insights, and closed-loop hardware integration to improve yields and reduce wasted experiments.
The platform provides AI-driven optimization for chemical processes, including a multi-objective optimization workflow, molecular descriptors to turn experimental data into chemical insights, automated kinetic modeling and mechanism discovery for scale-up, and real-time closed-loop optimization through hardware-integration with lab instruments. It supports features like Yield & Impurity calculations, temperature and catalyst parameter guidance, and an interactive interface to visualize and navigate experimental design and results. It also advertises integration with laboratory hardware for automatic experimentation and a database of proprietary chemical descriptors to ground predictions in chemistry.
Who it’s for: Pharmaceutical, chemical development, contract research, and industrial chemistry labs seeking data-driven optimization, faster experimentation cycles, and scale-up readiness.
Funding and partnerships cited (Pfizer x ReactWise), case studies and client testimonials, emphasis on industrial case studies and lab hardware integration
Alexander holds a PhD in chemical engineering, and has been working at the intersection of machine learning, chemistry and lab automation for the past 5 years. In his research, Alexander developed optimization algorithms to aid in designing manufacturing processes for novel pharmaceuticals. Alexander launched ReactWise to democratize access to AI in pharma & biotech with no-code software.
Daniel holds a PhD in chemical engineering, and has been working at the intersection of machine learning and chemistry for the past 5 years. In his research, Daniel developed optimization algorithms to aid in designing manufacturing processes for novel pharmaceuticals. Daniel launched ReactWise to democratize access to AI in pharma & biotech with no-code software.
No-code chemical process optimization software for pharma and biotech
ReactWise provides a no-code software platform that enables wet lab chemists to use data-driven optimization and robotic execution to accelerate chemical process development for pharma and biotech. The launch highlights algorithms for chemical process optimization, a claim of up to 95% reduction in experimental burden/cost versus exhaustive screening, and an invitation to talk to innovation managers, lab heads, and equipment manufacturers.
From the original launch (May 2024) — may be outdated.
Foundation Model for Chemical Manufacturing
Chemical Copilot for Pharma and Biotech
