Advancing novel
treatments for rare oncology.
Our pipeline focuses on diseases with significant unmet medical need — leveraging drug.AI to accelerate the path from in-silico discovery to the clinic.
Two active programs.
Both programs designed end-to-end by drug.AI — with human medicinal chemists and regulatory scientists in the loop at every gate.
■ COMPLETED · ■ IN PROGRESS · ■ PLANNED
Two modalities, one platform.
Our agents were built to design small molecules first — the modality with the deepest training data and clearest path to the clinic. Peptides come next.
ST-001 — a next-gen
WEE1 inhibitor for TNBC.
Triple negative breast cancer (TNBC) lacks the receptor targets that define other subtypes, leaving few precision options. WEE1 is a G2/M checkpoint kinase that TNBC cells rely on to survive replication stress — inhibiting it drives mitotic catastrophe in tumor cells while sparing normal tissue.
ST-001 was designed for selectivity and tolerability from day one — novel lead compounds identified in-silico using drug.AI, and now moving through biological validation.
ST-002 — FAK inhibition for Breast Cancer.
Focal adhesion kinase (FAK) drives tumor invasion, metastasis, and an immunosuppressive microenvironment — making it a compelling target across breast cancer subtypes.
In Silico Discovery — Active
Agentic AI is designing novel FAK-inhibiting candidates. Our composer agent is exploring chemical space that existing inhibitors have not reached — optimizing for potency, kinase selectivity, and drug-like properties jointly, not sequentially.
Parallel Validation
Experimental validation is running in parallel with in-silico design — every wet-lab result feeds back into drug.AI overnight, tightening the feedback loop between prediction and reality.
"We didn't screen a library. We generated a molecule that couldn't exist in any library — because the biology we're solving for is unique to this patient population."
— Dr. Dony Ang, CEO / CTO / Co-founder