Solena Materials sits at the intersection of artificial intelligence, synthetic biology, and materials science, with a goal of reinventing how textiles are made. Spun out of Imperial College London, the company uses AI techniques to design entirely new protein-based fibres at the molecular level, then manufactures them using engineered microbes fed on renewable feedstocks. The result is a class of fibres that can be tuned for performance characteristics such as appearance, hand-feel, and tensile strength while remaining biodegradable.
The core innovation is computational fibre design. Rather than relying on the limited palette of natural and petroleum-derived fibres available today, Solena's platform optimizes the protein sequences that determine a fibre's properties, allowing it to dial in attributes for specific end uses. Because the fibres are produced through fermentation by engineered microbes rather than extracted from petroleum, they offer a potentially far lower environmental footprint and avoid the persistence and microplastic problems associated with synthetics like polyester.
Solena was founded in 2022 by Dr. James MacDonald together with Professor Paul Freemont and Professor Milo Shaffer, drawing on research MacDonald conducted at Imperial. In 2025 the company raised $6.7 million (about £5.1 million) in seed funding, following a $4.1 million pre-seed in 2022 and bringing total funding to roughly $10.8 million. The seed round was led by billionaire physicist-turned-investor Sir David Harding, alongside SynBioVen and existing backer Insempra.
The funding is being used to scale production of its protein fibres, and the company has since relocated to a new pilot facility to move from lab-scale demonstration toward commercial volumes. Solena targets fashion, sports apparel, and technical textiles for defence and aerospace, markets hungry for sustainable, high-performance materials. The path from engineered microbe to industrial textile supply chain is long and capital-intensive, but Solena's AI-driven design engine and academic pedigree give it a differentiated approach to a multi-trillion-dollar materials problem.