Scindo Secures £4 Million Seed Funding to Revolutionize Enzyme Discovery with AI

[Disclosure: AgFunderNews’ parent company AgFunder is an investor in Scindo]

UK-based startup Scindo has successfully raised a £4 million ($5.4 million) seed round to advance its AI-driven enzyme discovery and design platform. The funding round was co-led by Kadmos Capital and Clay Capital, with additional contributions from PINC, the venture arm of food and beverage giant Paulig, as well as several existing investors including Synbioven, AgFunder, SOSV, Farvatn Venture, and Savantus Ventures.

Innovative Enzyme Solutions

Founded in 2020 by Dr. Gustaf Hemberg, Dr. Ben Davis, and Juliet Sword, Scindo harnesses AI models with proprietary data to accelerate the discovery and optimization of enzymes—biological catalysts capable of converting a variety of feedstocks into valuable ingredients previously derived from petrochemicals. This innovative approach is set to disrupt multiple industries, including food, flavorings, cosmetics, and specialty chemicals.

With the recent funding, Scindo plans to enhance its platform, scale wet-lab capabilities, and strengthen its team.

Reducing Reliance on Petrochemicals

Ali Morrow, a partner at Clay Capital, emphasizes the need for alternatives in the specialty chemicals industry: “The industry has long aimed to reduce its dependency on petrochemical-sourced ingredients, but conventional methods have struggled with complex natural feedstocks.” He believes that Scindo’s enzyme design can provide cost-effective, natural solutions while also accessing previously untappable feedstocks, thereby creating global opportunities to curtail reliance on crude oil.

Designer Enzymes for Complex Tasks

Scindo’s CEO, Gustaf Hemberg, shared with AgFunderNews that the startup began its journey by mapping enzymes with difficult-to-achieve functionalities using traditional chemistry. This included targeting C–H activations and C–C bond cleavages, which allows for the breakdown of stubborn materials like plastics. By identifying underutilized enzymes, Scindo has implemented machine learning models to discover new enzymes with unique capabilities.

“We gather new examples and create proprietary datasets of enzymes. This feeds our models to determine which sequences or structures drive performance,” Hemberg explained.

Closing the Loop

Once suitable enzyme candidates are identified, they undergo engineering to enhance selectivity and transformation efficiency, alongside improvements in physical traits like thermostability. Scindo’s extensive chemistry screening platform enables quick testing of enzymes, feeding real-world results back into their machine learning cycle, a method that has proven critical for their innovation strategy.

Cell-Free Biomanufacturing

Scindo’s initial products include enzymes for creating essential flavor and fragrance building blocks, as well as those facilitating the cost-effective, petrochemical-free production of high-value cosmetic ingredients via cell-free biomanufacturing.

By capitalizing on a wide array of agricultural fatty acid feedstocks, Scindo has engineered enzyme systems to selectively convert them into flavor compounds. The company is now moving towards pilot-scale production in partnership with an undisclosed entity.

“Although some flavor ingredients can be generated through precision fermentation, they do come with higher costs. Our cell-free approach offers a more efficient alternative,” Hemberg noted.

Operational Efficiency

The technology runs faster reactions and accommodates a broader range of conditions without the need for cellular frameworks, leading to reduced energy consumption and cleaner product outputs.

“We are looking to target a market launch within the next year for our initial two products,” Hemberg confirmed.

Proprietary Data Sets: The Competitive Edge

Analyzing the broader enzyme industry, Hemberg noted that most large enzyme companies concentrate on a select few enzyme families, primarily in traditional applications. “We are navigating novel applications that existing chemistry has found challenging to address. Our key differentiator lies in our proprietary data on novel enzymes and their diverse functionalities, which enables carbon-chain transformations previously deemed difficult,” he concluded.