LONDON & SAN FRANCISCO, December 16, 2025 -   Today, Latent Labs announces Latent-X2, a frontier AI model that can design drug-like biologics without iteration. Drug hunters can use Latent Lab's AI platform, built on Latent-X2, to access difficult targets and accelerate development timelines by reducing wet lab work. The generated designs display drug-like properties including low ex vivo immunogenicity, significantly shortening the path from hit to clinical candidate. Alongside the release, Latent Labs welcomes Stefan Oschmann, former CEO of Merck KGaA, to its strategic advisory board. Latent Labs is opening access to the model for selected partners.

The Development Bottleneck. Current wet lab approaches are costly not merely in development effort but in clinical failure. Hits rarely possess properties needed for clinical success, and optimization to address liabilities frequently fails or produces zero-sum tradeoffs. Suboptimal starting points risk costly downstream failure in clinical programs, and addressing shortcomings requires long development timelines.

Latent Labs Platform. Through the Latent Labs Platform, partners and customers can generate antibodies and peptides for disease targets of their choice. It produces high-affinity binders across VHH, scFv, and macrocyclic peptide formats - approaching drug-like quality from the first generation. The platform provides a scientist-friendly workflow, accessible to customers via web browser or by integrating their own systems with the Latent Labs API.

Zero-Shot Antibody and Peptide Design. Latent-X2 generates antibodies that bind challenging targets from the first generation - achieving hits against half of 18 targets selected for diversity and difficulty, with picomolar to nanomolar affinities and each requiring only 4 to 24 designs. The model generalizes beyond antibodies: macrocyclic peptides bind K-Ras, long considered undruggable, matching or exceeding hits from trillion-scale mRNA display screens while testing 11 orders of magnitude fewer sequences.

Drug-Like by Default. Antibodies designed by Latent-X2 exhibit developability profiles matching or exceeding approved therapeutic controls in head-to-head comparison. This extends to proxies for immunogenicity: in the first such assessment of any AI-generated antibody, de novo VHH binders were evaluated across a ten-donor human panel in ex vivo T-cell activation and cytokine release assays, confirming both potent target engagement and low immunogenicity. While animal studies and clinical trials remain ahead, these results demonstrate that AI-generated molecules can now clear preclinical hurdles that previously required lengthy optimization.

"Semiconductors, satellites and aircraft once required repeated build-test cycles, consuming years and billions of dollars. Today they're designed computationally before anything is fabricated. With Latent-X2, drug discovery can move towards that same step change - designing the right molecule from the start," said Simon Kohl, CEO and founder of Latent Labs.

Strategic Advisory Board. Latent Labs announces the appointment of Stefan Oschmann, former CEO of Merck KGaA (until 2021), to its strategic advisory board. "The pharmaceutical industry has spent decades optimizing around the limitations of iterative lab work. Latent Labs is doing something different - building the capability to design molecules that work from first principles. That shift, if it holds, changes the entire logic of drug discovery," said Oschmann.

Access. Latent-X2 will be available to selected partners. Interest for access can be expressed at partnerships@latentlabs.com.
Latent-X2 builds on the success of Latent-X1, released just five months ago. Latent-X1 has been adopted by industry and academic groups worldwide, who value its performance and no-code interface for real lab applications.

Ten months ago Latent Labs announced its $50M funding round, co-led by Radical Ventures and Sofinnova Partners, with participation by Anthropic's CEO Dario Amodei, Eleven Labs' CEO Mati Staniszewski, and Google's Chief Scientist Jeff Dean. The team includes former AlphaFold 2 co-developers and ex-DeepMind team leads, with experience from Microsoft, Apple, Exscientia, Mammoth Bio, Altos Labs, and Zymergen.

• Latent-X generates lab-ready macrocycles and protein mini-binders at all-atom resolution to accelerate drug design
• The model can be accessed through Latent's web-based platform for push-button protein design. Sign ups are open now for early access: platform.latentlabs.com
• Extensive lab validation shows picomolar binding affinities outperforming prior models, with 91-100% hit rates for macrocycles and 10-64% for mini-binders

LONDON & SAN FRANCISCO, July 22, 2025 — Today, Latent Labs is launching Latent-X, a frontier AI model for push button protein design, outperforming competing models under identical laboratory conditions. The model is available for early access on Latent's no-code AI protein design platform, where users can upload protein targets and generate cyclic peptides and mini-binders directly in the browser. Through the platform, users can generate, explore, and score binder designs, selecting top-ranked structures for further lab testing. The platform includes a free tier for both commercial and non-commercial users. Sign up is available at platform.latentlabs.com.

Latent Labs is a frontier AI lab working to transform the expensive, labor intensive, and high failure rate processes of drug discovery into automated drug design. Traditional drug discovery requires screening millions of random molecules—a process where hit rates are typically well below 1% and each experiment takes months and costs thousands of dollars. With Latent-X, drug designers can generate high-confidence binders with the push of a button, achieving what would typically require testing millions of candidates by testing as little as 30 candidates per target.

AI models have recently enabled solutions to previously insurmountable technical challenges in biology. With generative models, frontier AI can go beyond predicting structures to creating new sequences and structures of candidate drugs.

"We envision a future where effective therapeutics can be designed entirely in a computer, much like how space missions or semiconductors are designed today," said Simon Kohl, CEO and founder of Latent Labs. "Our platform empowers scientists with lab-validated protein binder design at their fingertips, whether they're experts or new to AI-powered drug design, and without needing AI infrastructure. This is the first step on our mission toward making biology programmable in order to make drug design instantaneous."

Latent-X generates functional, high affinity de novo binders with breakthrough laboratory performance. In extensive wet lab experiments across 7 therapeutic targets, Latent-X achieved 91-100% hit rates for macrocycles and 10-64% hit rates for mini-binders. The model delivered picomolar binding affinities for mini-binders and single-digit micromolar affinities for macrocycles, with generated binders showing strong target specificity. In head-to-head experimental comparisons, Latent-X exceeded the prior state-of-the-art, outperforming existing generative tools in both in silico evaluations and laboratory validation. Macrocycles are a sought after drug modality for their potential oral deliverability, with their compactness promising tissue permeability while retaining specificity. Mini-binders are a versatile new binder modality that offers high specificity in a flexible format. Full results are available in our technical report: latent-x.latentlabs.com.

The Latent Labs Platform allows users to access the state of the art in protein binder design in an intuitive platform for target upload, hotspot selection, binder design, and computational ranking. The platform features structure visualization, predicted structure overlays, and computational metric rankings allowing to replicate the AI workflows used to generate our successfully lab-validated binders.

Latent-X is a general purpose frontier model that creates binders from scratch for unseen or previously untargeted proteins, solving the geometric puzzle of binding at the all-atom level. The model generates designs over 10x faster than previous methods and co-samples sequence and structure simultaneously, allowing for computational experimentation within seconds. Latent-X generalizes beyond nature's repertoire by generating all-atom binder structures that obey atomic-level biochemical rules, opening doors to other therapeutic modalities that depend on target-specific binding—nanobodies and antibodies being prime examples. The company is now open to partnerships to bring these expanded capabilities to new drug applications.

Only five months ago Latent Labs announced its $50M funding round co-lead by Radical Ventures and Sofinnova Partners, with participation by Google’s Chief Scientist Jeff Dean, Anthropic’s CEO Dario Amodei and Eleven Labs’ CEO Mati Staniszewski. The team consists of former AlphaFold 2 co-developers, ex-DeepMind team leads, and brings rich experience from Microsoft, Apple, Stability AI, Exscientia, Mammoth Bio, Altos Labs and Zymergen.