Latent Labs, established by a former DeepMind employee, raises $50M to enable programmable biology
Latent Labs Launches with Promising Funding
A fresh startup called Latent Labs, founded by Simon Kohl, a former scientist from Google DeepMind, has recently emerged from stealth mode with a significant funding boost of $50 million. This startup aims to leverage artificial intelligence to revolutionize the field of biology by creating AI foundation models that make biological processes programmable.
The Importance of Proteins in Biology
To grasp the impact of advances made by DeepMind and similar entities, it is crucial to understand proteins and their fundamental roles in living organisms. Proteins are responsible for various functions within cells, including acting as enzymes, hormones, and antibodies. They consist of about 20 different amino acids linked together, forming chains that fold into specific three-dimensional shapes. The structure of each protein directly influences its function.
Traditionally, determining the shape of proteins has been a lengthy and labor-intensive endeavor. DeepMind made a significant breakthrough with AlphaFold, an AI system that uses machine learning and biological data to predict the shapes of approximately 200 million proteins. This advancement enables scientists to better understand diseases, design new medications, and even create synthetic proteins for new applications.
Latent Labs’ Ambitious Goals
Latent Labs aims to accelerate drug discovery and development by enabling researchers to computationally create new therapeutic molecules. The startup’s mission is to make biology more manageable through computational methods, ultimately reducing the reliance on extensive biological experiments.
Simon Kohl’s Transition from DeepMind to Startup
Simon Kohl began his career as a research scientist at DeepMind, where he worked closely with the AlphaFold2 team and later co-led the protein design group. In 2022, motivated by the growth of related ventures like Isomorphic Labs, Kohl decided to focus his efforts on a more targeted approach to protein design. He founded Latent Labs in mid-2023 as an agile company dedicated to developing cutting-edge models for protein design.
“I had a fantastic and impactful time at DeepMind,” Kohl mentioned in a recent interview, emphasizing his belief in the transformative potential of generative modeling in biology. He perceived a unique opportunity in the expansive field of protein design, which remains largely untapped.
Latent Labs started small with a team of 15 employees, including individuals with backgrounds from DeepMind and other prestigious institutions. The workforce is distributed across two locations: one in London, where the core model development occurs, and another in San Francisco, which houses a wet lab as well as a computational protein design team.
The Role of Wet Labs in Validation
The establishment of a wet lab allows Latent Labs to test and validate its models in real-world scenarios, ensuring that their predictions are accurate. However, the long-term vision aims at reducing the necessity for wet lab experiments altogether.
Kohl envisions a future where biology can be programmed with precision, allowing for the custom design of molecules with the desired properties for specific therapeutic targets. The ultimate goal is to streamline the drug discovery process, which can currently take years and involve numerous experiments.
Business Strategy of Latent Labs
Latent Labs adopts a partnership-driven business model rather than developing its own therapeutic candidates. The startup aims to collaborate with biopharmaceutical companies and life science businesses, facilitating access to its innovative models and supporting discovery programs through project-based partnerships.
The recent $50 million funding includes an initial $10 million seed round followed by a $40 million Series A round led by Radical Ventures and Sofinnova Partners. The funding will primarily be directed towards hiring new talent, expanding computational infrastructure, and scaling the company’s operations.
“Compute is a significant expense for us, as we are building large models that require extensive GPU resources,” noted Kohl. This capital infusion positions Latent Labs to enhance its computational capabilities and pursue strategic partnerships necessary for commercial advancement.
Although there are other startups attempting to merge computation and biology, with ventures like Cradle and Bioptimus emerging, Kohl believes that the field remains in the early stages. There is still much to explore regarding the best modeling approaches and business models that will effectively bridge the gap between computation and biological processes.
