Labgenius raises $10m to use ai for protein drug discovery | techcrunch

LabGenius, a London-based startup applying AI and “robotic automation” to protein drug discovery, has raised $10 million in Series A funding. The round is led by Lux Capital and Obvious

Ventures, with participation from Felicis Ventures, Inovia Capital, Air Street Capital and existing investors. Also investing is Recursion Pharmaceuticals’ founder and CEO Chris Gibson, as

well as Inovia Capital General Partner Patrick Pichette, who was formerly Google’s CFO. Lux Capital’s Zavain Dar and Obvious Ventures’ Nan Li will join the LabGenius board of directors.

Notably, the U.K. company’s early investors include Nathan Benaich, Torsten Reil, EF’s Matt Clifford, and Philipp Moehring, to name just a few. “LabGenius is a full-stack protein engineering

company: we combine artificial intelligence (AI), robotic automation and synthetic biology to evolve next-generation protein therapeutics,” founder and CEO Dr. James Field tells me. “My

central thesis, the thing that’s really the driving force behind the company, is the conviction that we’re entering an age in which humans will no longer be the sole agents of innovation.

Instead, new knowledge, technologies and sophisticated real-world products will be invented by smart robotic platforms called empirical computation engines. An empirical computation engine

is an artificial system capable of recursively and intelligently searching a solution space.” LabGenius’ flagship technology is called “EVA,” which Field describes as a “machine

learning-driven, robotic platform” capable of evolving new proteins. “As a smart robotic platform, EVA is capable of designing, conducting and critically learning from its own experiments,”

he says. The goal: to discover and develop new protein therapeutics that are currently hard for humans alone to find. “For decades, scientists, engineers and technologists have dreamt of

building ‘robot scientists’ capable of autonomously discovering new knowledge, technologies and sophisticated real-world products,” explains Field. “For protein engineers, that dream has now

entered the realm of possibility. The rapid pace of technological development across the fields of synthetic biology, robotic automation and ML has given us access to all the essential

ingredients required to create a smart robotic platform capable of intelligently discovering novel therapeutic proteins.” To that end, Field frames the development of EVA as a “long-term,

ambitious undertaking” that he says will enable the startup to address previously unsolvable protein engineering challenges and in doing so, develop urgently needed therapeutics. “My

ultimate goal for LabGenius is to establish a fully integrated biopharmaceutical company powered by the world’s most advanced protein engineering platform,” he adds. “Quite honestly, this is

a gargantuan undertaking and, while we’ve already established one of the world’s most technically sophisticated protein engineering operations, we’re only just scratching the surface of

what’s possible.” More broadly, there is a tension that many deep tech companies face, which is determining how best to develop technology that’s tightly aligned to real-world commercial

needs (before running out of capital!). “For LabGenius, we’ve achieved this in a highly intentional way by undertaking a series of commercial projects of increasing complexity from the

company’s earliest days,” Field says. One on-going project is with Tillotts Pharma AG to identify and develop new drug candidates for the treatment of inflammatory bowel disease (IBD). “Our

business model is pretty simple,” says the LabGenius founder. “We use EVA to discover and characterise new drug molecules and then partner with pharma companies who can take these molecules

to market. For example in a typical partner-financed early discovery program, we’ll take a project from concept to early pre-clinical stage. Typical deal structures include a blend of

R&D payments, milestones & royalties.” Meanwhile, LabGenius will use the capital to scale its team, expand the scope of its discovery platform and initiate an “internal asset

development program.” The next goal is to evolve novel antibody fragments capable of treating conditions that cannot be addressed using conventional antibody formats.