3 · The Frontier
Design and stress-test therapeutics and diagnostics against a model of the disease before wet-lab spend, then test the predictions back through the Virtual Lab. Groundwork now; no model claims are sold.
Public cohorts from the Atlas and consented Virtual Lab data, all reprocessed through the same pipelines so they are comparable.
A disease-specific model of the malignant clone, its normal counterpart and its microenvironment, with calibrated uncertainty on every prediction.
Targets ranked by replication; candidates designed and screened against the model.
Efficacy against toxicity, resistance under selective pressure, off-target effects, sensitivity to model assumptions.
Prioritised candidates and predictions return through the Virtual Lab pipelines for wet-lab testing by partners.
In-silico therapeutics
Targets that survive independent replication across cohorts (Target-Replication Reports) and show a therapeutic window against normal tissue.
Small-molecule and biologic candidates proposed against the ranked targets; antigen selection for cell and bispecific therapies.
Predicted effect on the malignant clone against predicted effect on its normal counterpart and the microenvironment.
Clonal escape modelled from clonal-evolution data: which subclones survive, and what second line follows.
In-silico assessment of biomarker and MRD assay designs before samples are spent.
Every prediction carries a calibrated confidence and the data it rests on. The model abstains where evidence is absent.
First disease area: hematologic malignancies, where the founder's data and the clonal-evolution modules already exist. The model is independently evaluated before any prediction is offered to a partner, and the independence firewall applies: the Trust Layer never evaluates our own model, so evaluation is commissioned from outside.
Custom biomedical AI systems
Retrieval systems over a client's own literature and data, and scoped agents for defined research workflows, built on the Virtual Lab agent stack. Each ships with a pre-registered fitness-for-purpose evaluation harness. Fixed-scope engagements on request; never sold as independent evaluation.
Physical AI
Safety-engineered laboratory systems in which agents operate and audit instruments through published driver interfaces: fail-closed, human approval for high-risk steps, tamper-evident audit records. First open-source release is live; pilots run under qualification protocols. Not sold as a validated GxP system.
Research use only. Not for use in diagnostic procedures. BioMedsAI is not a CLIA-certified laboratory and does not perform clinical testing. BioMedsAI is not a notified body and holds no accreditation for conformity assessment. Evaluations are independent scientific opinions, not regulatory determinations. Sequencing is performed by qualified partner laboratories under their own accreditations.
Collaborations on virtual cells, in-silico design and laboratory automation are scoped in writing, with the independence firewall applied.