ReactWise, a YC-backed startup from Cambridge, U.K., is leveraging Artificial Intelligence  to expedite chemical manufacturing, a crucial phase in drug development. ReactWise claims to speed up the conventional trial-and-error procedure by 30 times by utilizing an “AI copilot for chemical process optimization,” with the goal of nearly instantly predicting the best experiment. Despite concentrating only on software development, the business has combined five years of university study into a software solution that connects with robotic lab equipment. In order to train its AI models, ReactWise has carried out thousands of reactions in its labs. This can drastically cut down on the number of iteration cycles required for drug manufacture. Twelve pilot projects are presently underway with pharmaceutical companies, and the company anticipates shortly turning these into full-scale implementations.

ReactWise recently revealed that it has raised $3.4 million in pre-seed funding, which includes contributions from YC, Innovate U.K., and unidentified angel and venture capitalists. A shorter total drug development schedule could result from the startup’s strategy, which could cut the process development time in drug manufacturing by 60%.

“Making drugs is really like cooking,” said co-founder and CEO Alexander Pomberger (pictured above left, with co-founder and CTO Daniel Wight) in a call with TechCrunch. “You need to find the best recipe to make a drug with a high purity and a high yield.”

The industry has for years relied upon what boils down to either trial-and-error or staff expertise for this “process development,” he said. Adding automation into the mix offers a way to shrink how many iteration cycles are required to land on a solid recipe for manufacturing a drug.

The startup thinks it will be able to deliver “one shot prediction” — where the AI will be able to “predict the ideal experiment” almost immediately, without the need for multiple iterations where data on each experiment is fed back in to further home predictions — in the near future

ReactWise asserts a competitive edge because of its superior in-house data sets and pretrained models that have a fundamental understanding of chemistry, even if other startups are also using AI for various elements of drug development. By providing instant process recommendations based on thorough pre-work carried out in their lab, this establishes ReactWise as a pioneer in AI-driven pharmaceutical manufacturing.

While ReactWise is focusing narrowly on a specific part of the drug development chain, Pomberger said acceleration here can make a meaningful difference in reducing the time it takes to get new pharmaceuticals to patients.

He noted “Let’s look at a typical duration of a drug from start to launch: 10 to 12 years. Process development takes one to 1.5 to two years. And if we can basically speed up the workflows here — reduce it by an average of 60% — then we can get an idea of how much of an effect it is.”

As other startups are applying AI to different aspects of drug development, including identifying interesting chemicals in the first place, there’s likely to be a multiplier effect as more automation innovations get folded in.

But when it comes to drug manufacturing, specifically, Pomberger argues that ReactWise is ahead of the pack. He said “we were the first to actually tackle this.”

The startup competes with legacy software using statistical approaches, such as JMP. He also said that there are a few others applying AI to speed up drug manufacturing, but said that ReactWise’s access to high-quality datasets on chemical reactions gives it the competitive edge.

“We are the only ones that have the capability of, and that are currently generating, these high-quality datasets in house,” he said. “Most of our competitors, they provide the software. The clients are basically prompted with instructions based on the inputs.

“But, from our side of things, we offer these pretrained models — and those are extremely powerful because they fundamentally understand chemistry. And the idea is then to really have a client just say: ‘This is my reaction of interest, hit start, and we already give them process recommendations from the very first day, based on all the pre-work that we did in our laboratory. And that’s something nobody else does at the moment.”