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 experimental burden and cost when compared to exhaustive screening. We have made our approaches accessible to human experimentalists through our user-friendly no-code software platform, and to automated laboratory equipment with our API.

Our Background: 
We recently completed our PhDs in Machine Learning for Chemistry at the University of Cambridge. We built an automated lab to validate our optimization strategies during our studies, and are now working to accelerate and automate chemistry & biotech.
AI co-pilot for chemical reaction optimization
Title: ReactWise: AI Co-Pilot for Chemical Process Optimization | Y Combinator

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Markdown Content:


### 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.

### Jobs at ReactWise

San Francisco, CA, US

$150K - $200K

0.50% - 1.50%

3+ years

San Francisco, CA, US / Remote (US; GB)

$125K - $200K

0.10% - 0.50%

Any (new grads ok)



ReactWise

Founded:2024

Team Size:2

Location:San Francisco

### Active Founders

### Alexander Pomberger, Founder

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 Wigh, Founder

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.



### Company Launches

[### ReactWise - AI co-pilot for wet lab chemistry](

**TL;DR:** [ReactWise]( aims 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 that leverage transfer learning and Bayesian optimization. We validated the algorithms in the wet lab, showing an up to **95% reduction in experimental burden and cost** when compared to exhaustive screening. We have made our approaches accessible to human experimentalists through our user-friendly no-code software platform, and to automated laboratory equipment with our API.



**Our Background**

We ([Daniel]( and [Alexander]( recently completed our PhDs in Machine Learning for Chemistry at the University of Cambridge. We built an automated lab to validate our optimization strategies during our research and are now working to accelerate and automate chemistry & biotech.



**Our Ask**

*   We are keen to talk to **innovation managers and labheads** **at pharmaceutical and biotech** companies who
    *   Work with experimentalists who would benefit from our no-code optimization platform
    *   Want to build an automated lab but are not sure where to start
*   We are also interested in speaking with **equipment manufacturers** so we can integrate our software with their hardware

Click here to [book a demo]( with us, or email us via [info@react-wise.com](mailto:info@react-wise.com).
