Pharma-grade longevity
AI-driven drug discovery for human longevity.
We combine a 2B+ compound library with proprietary graph-based deep learning to discover next-generation therapeutics across metabolic health, inflammation, and oncology.
Key metrics
- Compound library
- 2B+
- Molecules generated
- 10M+
- Hit-rate uplift
- 64x
- Predictive accuracy
- 44.83%
Virtually screenable molecules accessible through our AI pipeline.
Novel candidates produced by our generative discovery models.
Measured improvement over conventional high-throughput screening.
Top-line model performance reported in our IEEE DTIGN paper.
Platform
From 2B compounds to clinical candidates.
Our DTIGN model — a drug-target interaction graph network published in IEEE — powers a screening stack that has demonstrated a 64x hit-rate uplift over traditional high-throughput screening.
Read the scienceMetabolic Health
NYB-MET-001
Preclinical
Inflammation
NYB-INF-002
Discovery
Oncology
NYB-ONC-003
IND Filing
Newsroom
Latest research & milestones
Publication
Nanyang Biologics publishes DTIGN model in IEEE
Our drug-target interaction graph network demonstrates a 64x hit-rate improvement on benchmark assays.
15 Sept 2024
Conference
NYB research accepted at NeurIPS 2025
Two papers from our generative chemistry team will be presented at NeurIPS 2025.
26 Sept 2025
Conference
Featured speaker slot at NVIDIA GTC 2026
Our CTO will present our drug discovery platform stack at NVIDIA GTC 2026.
18 Mar 2026
Research Briefs
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Monthly digest of our AI drug discovery progress, publications, and longevity science.