A new startup founded by a former Google DeepMind scientist is exiting stealth with $50 million in funding.

Latent Labs is building artificial intelligence foundation models to “make biology programmable,” and it plans to partner with biotech and pharmaceutical companies to generate and optimize proteins.

Understanding proteins is crucial to comprehend human biology, as they are made up of 20 distinct amino acids that link together in strings to create a 3D structure, determining how a protein functions. Historically, figuring out the shape of each protein was a slow and labor-intensive process. This changed with DeepMind’s breakthrough using AlphaFold, which meshed machine learning with real biological data to predict the shape of some 200 million protein structures.

With the data provided by AlphaFold, scientists can better understand diseases, design new drugs, and even create synthetic proteins for entirely new use-cases. This is where Latent Labs enters the picture, aiming to enable researchers to “computationally create” new therapeutic molecules from scratch.

Latent potential

Simon Kohl, the founder of Latent Labs, started as a research scientist at DeepMind, working with the core AlphaFold2 team. He then co-led the protein design team and set up DeepMind’s wet lab at London’s Francis Crick Institute. At the time, DeepMind also launched a sister company, Isomorphic Labs, focusing on applying DeepMind’s AI research to transform drug discovery.

Kohl departed DeepMind to focus on building a leaner outfit focused specifically on building cutting-edge models for protein design. He incorporated the business in London at the end of 2022 and hired around 15 employees, including two from DeepMind, a Microsoft engineer, and PhDs from the University of Cambridge.

“I had a fantastic and impactful time at DeepMind, and became convinced of the impact that generative modelling would have in biology and protein design in particular,” Kohl said. “At the same time, I saw that with the launch of Isomorphic Labs, and their plans based on AlphaFold2, that they were starting many things at once. I felt like the opportunity was really in going in a laser-focused way about protein design. Protein design, in itself, is such a vast field, and has so much unexplored white space that I thought a really nimble, focused outfit would be able to translate that impact.”

To translate that impact as a venture-backed startup, Kohl needed to hire more employees. Today, Latent’s headcount is split across two sites — one in London, where the frontier model magic happens, and another in San Francisco, with its own wet lab and computational protein design team.

“This enables us to test our models in the real world and get the feedback that we need to understand whether our models are progressing the way we want,” Kohl said.

While wet labs are on the agenda, the ultimate goal is to eliminate the need for them in the future.

“Our mission is to make biology programmable, really bringing biology into the computational realm, where the reliance on biological, wet lab experiments will be reduced over time,” Kohl said.

This highlights the key benefit of making biology programmable — upending the drug-discovery process that currently relies on countless experiments and iterations.

“It allows us to make really custom molecules without relying on the wet lab — at least, that’s the vision,” Kohl continued. “Imagine a world where someone comes with a hypothesis on what drug target to go after for a particular disease, and our models could, in a ‘push-button’ way, make a protein drug that comes with all the desired properties baked in.”

The business of biology

Latent Labs doesn’t see itself as “asset-centric” — meaning it won’t be developing its own therapeutic candidates in-house. Instead, it plans to work with third-party partners to expedite and de-risk the earlier R&D stages.

“We feel the biggest impact that we can have as a company is by enabling other biopharma, biotechs, and life science companies — either by giving them direct access to our models, or supporting their discovery programs via project-based partnerships,” Kohl said.

The company’s $50 million funding includes a previously unannounced $10 million seed tranche, and a fresh $40 million Series A round co-led by Radical Ventures — specifically, partner Aaron Rosenberg, who was formerly head of strategy and operations at DeepMind.

Other participants in the round include Flying Fish, Isomer, 8VC, Kindred Capital, Pillar VC, and notable angels such as Google’s chief scientist Jeff Dean, Cohere founder Aidan Gomez, and ElevenLabs founder Mati Staniszewski.

While a portion of the funding will go towards salaries, the company will need more money to cover infrastructure, especially with the building of large models that require a significant amount of GPU compute.

“Compute is a big cost for us as well — we’re building fairly large models. This funding really sets us up to double-down on everything — acquire compute to continue scaling our model, scaling the teams, and also starting to build out the bandwidth and capacity to have these partnerships and the commercial traction that we’re now seeking.”

Several venture-backed startups are bringing together computation and biology, including Cradle and Bioptimus. Kohl believes we are still at an early stage, where it is unsure what the best approach for decoding and designing biological systems will be.

“There have been some very interesting seeds planted — for example, with AlphaFold and some other early generative models from other groups,” Kohl said. “But this field hasn’t converged in terms of what is the best model approach, or in terms of what business model will work here. I think we have the capacity to really innovate.”