BioMate AI

Research Engines

Understand biology.
Design what comes next.

We are advancing models for cellular understanding and molecular generation, with a focus on immune biology and cyclic peptides. These research programs build on the data, execution and evaluation capabilities of the BioMate workbench.

Conceptual illustration of a virtual cell research environment

Virtual Cell LLM

Active research program

From cellular state
to testable hypotheses.

Our Virtual Cell LLM research explores how models of cellular state and response can inform target identification and other biomedical decisions.

We aim to connect multi-omics evidence, biological context and perturbation reasoning so researchers can prioritize hypotheses for experimental testing.

Molecular generation

Active research program

Explore new
cyclic-peptide candidates.

We are developing generation models focused on cyclic peptides, bringing target context and molecular structure into candidate design.

The intended loop connects generation, computational evaluation and assay feedback, allowing each round to inform the next. Candidates remain hypotheses until tested experimentally.

A molecular binding model displayed for computational evaluation

One scientific foundation

Models connected
to the research process.

Biological context

Ground scientific questions in evidence, data and disease mechanisms.

Computational evaluation

Compare model outputs through explicit tasks, baselines and reproducible analyses.

Experimental learning

Use observed results to challenge predictions and guide future research.