Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908930111700992 |
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| author | Latent Labs Team Schmon, Sebastian M. Pretorius, Daniella Mathis, Simon Bartke-Croughan, Rebecca Puvanendran, Aishaini Vuckovic, James Kenlay, Henry Vlachynská, Mária Bridgland, Alex Grishin, Ivan Over, Sven Li, David Li, Bridget Crabbé, Jonathan Hilmkil, Agrin Nelson, Alexander W. R. Yuan, David Obika, Annette Kohl, Simon A. A. |
| author_facet | Latent Labs Team Schmon, Sebastian M. Pretorius, Daniella Mathis, Simon Bartke-Croughan, Rebecca Puvanendran, Aishaini Vuckovic, James Kenlay, Henry Vlachynská, Mária Bridgland, Alex Grishin, Ivan Over, Sven Li, David Li, Bridget Crabbé, Jonathan Hilmkil, Agrin Nelson, Alexander W. R. Yuan, David Obika, Annette Kohl, Simon A. A. |
| contents | Drug discovery relies on iterative expert workflows that are slow to parallelize and difficult to scale. Here we introduce Latent-Y, an AI agent that autonomously executes complete antibody design campaigns from text prompts, covering literature review, target analysis, epitope identification, candidate design, computational validation, and selection of lab-ready sequences. Latent-Y is integrated into the Latent Labs Platform, where it operates in the same environment as drug-discovery experts with access to bioinformatics tools, biological databases, and scientific literature. The agent can run fully autonomously end-to-end, or collaboratively, where researchers review progress, provide feedback, and direct subsequent steps. Candidate antibodies are generated using Latent-X2, our frontier generative model for drug-like antibody design. We demonstrate the agent's capability across three distinct campaign types: epitope discovery guided by therapeutic specifications, cross-species binder design, and autonomous design from a scientific publication targeting human transferrin receptor for blood-brain barrier crossing. Across nine targets, Latent-Y produced lab-confirmed nanobody binders against six, achieving a 67% target-level success rate with binding affinities reaching the single-digit nanomolar range, without human filtering or intervention. In user studies, experts working with Latent-Y completed design campaigns 56 times faster than independent expert time estimates, compressing weeks of work into hours. Because Latent-X2 is a general-purpose atomic-level model for biologics design, the same agent architecture naturally extends to macrocyclic peptide and mini-binder design campaigns, broadening autonomous discovery across therapeutic modalities. Latent-Y is available to selected partners at https://platform.latentlabs.com. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29727 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design Latent Labs Team Schmon, Sebastian M. Pretorius, Daniella Mathis, Simon Bartke-Croughan, Rebecca Puvanendran, Aishaini Vuckovic, James Kenlay, Henry Vlachynská, Mária Bridgland, Alex Grishin, Ivan Over, Sven Li, David Li, Bridget Crabbé, Jonathan Hilmkil, Agrin Nelson, Alexander W. R. Yuan, David Obika, Annette Kohl, Simon A. A. Biomolecules Drug discovery relies on iterative expert workflows that are slow to parallelize and difficult to scale. Here we introduce Latent-Y, an AI agent that autonomously executes complete antibody design campaigns from text prompts, covering literature review, target analysis, epitope identification, candidate design, computational validation, and selection of lab-ready sequences. Latent-Y is integrated into the Latent Labs Platform, where it operates in the same environment as drug-discovery experts with access to bioinformatics tools, biological databases, and scientific literature. The agent can run fully autonomously end-to-end, or collaboratively, where researchers review progress, provide feedback, and direct subsequent steps. Candidate antibodies are generated using Latent-X2, our frontier generative model for drug-like antibody design. We demonstrate the agent's capability across three distinct campaign types: epitope discovery guided by therapeutic specifications, cross-species binder design, and autonomous design from a scientific publication targeting human transferrin receptor for blood-brain barrier crossing. Across nine targets, Latent-Y produced lab-confirmed nanobody binders against six, achieving a 67% target-level success rate with binding affinities reaching the single-digit nanomolar range, without human filtering or intervention. In user studies, experts working with Latent-Y completed design campaigns 56 times faster than independent expert time estimates, compressing weeks of work into hours. Because Latent-X2 is a general-purpose atomic-level model for biologics design, the same agent architecture naturally extends to macrocyclic peptide and mini-binder design campaigns, broadening autonomous discovery across therapeutic modalities. Latent-Y is available to selected partners at https://platform.latentlabs.com. |
| title | Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2603.29727 |