LONDON & SAN FRANCISCO–(BUSINESS WIRE)–Today, Latent Labs announces Latent-X2, a frontier AI model that can design drug-like biologics without iteration. Drug huntersLONDON & SAN FRANCISCO–(BUSINESS WIRE)–Today, Latent Labs announces Latent-X2, a frontier AI model that can design drug-like biologics without iteration. Drug hunters

Latent Labs Announces Latent-X2: AI-Generated Antibodies With Drug-Like Developability and Low Ex Vivo Immunogenicity

2025/12/16 17:16

LONDON & SAN FRANCISCO–(BUSINESS WIRE)–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.

Q&A

Commercial questions

What are the terms for commercial use?

  • Latent-X2 is available on request and through commercial partnerships. For partnership inquiries, reach out to partnerships@latentlabs.com.

How can I get in touch for commercial partnerships?

  • Contact partnerships@latentlabs.com.

Scientific questions

What is a typical application for Latent-X2?

  • Typical applications include the design of antibodies (VHH, scFv) and macrocyclic peptides for drug development programs. The model is particularly suited for generating drug-like antibody molecules.

How is Latent-X2 different from Latent-X1?

  • Latent-X1 focused on macrocyclic peptides and protein mini-binders with strong binding affinities. Latent-X2 advances to antibodies (VHH, scFv) with drug-like developability and demonstrated low ex vivo immunogenicity in initial studies. These profiles emerge without post-generation iteration.

How many designs are typically needed?

  • Latent-X2 achieves strong lab results with 4 to 24 designs per target for antibodies, representing efficiency gains of many orders of magnitude compared to conventional screening methods.

What targets has Latent-X2 been validated against?

  • We tested antibody designs against 18 soluble proteins, achieving picomolar to nanomolar hits against half the targets. Macrocyclic peptides were validated against 2 targets including K-Ras.

Do generated molecules require optimization?

  • In our validation studies, developability profiles emerged directly from the model without iteration, on par or better than approved therapeutics. Nonetheless, we expect some designs to require further development and optimization.

Broader questions

How does Latent Labs ensure safe usage of the technology?

  • Latent Labs actively participates in biosafety and biosecurity discussions with government and regulatory authorities regarding responsible development and preventing potential misuse of emerging technologies. Latent Labs continuously assesses its technologies and restricts access to its services in compliance with international sanctions lists from the EU and UK.

Contacts

contact@latentlabs.com

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