Vecurate —
A Reimagined Natural Compound Library
Designed to break through siloed legacy compound libraries, NYB's Vecurate serves as a proprietary digital compound library that capture, structure and enrich nature’s bioactive molecules.
Vecurate integrates multiple forms of intelligence into a single system, combining scientific knowledge from biology and chemistry, natural biological signals from plant, fungal, and marine sources, and computational modeling with experimental validation.
Data Layer
The Data Layer organises complex biological and chemical information into AI-ready formats, enabling relationships between molecules, cells, and disease to be understood computationally:
- Learn from publicly available scientific literature, databases, and experimental data, including ChEMBL, the Protein Data Bank (PDB), and UniProt
- Connect molecular structures to biological function
- Continuously update as new knowledge becomes available
Model Layer
Using tools such as mass spectrometry, metabolomics and extraction analytics, the model layer decodes biochemical signals from natural sources to:
- Map relationships between species, molecules, and disease
- Identify active compounds
- Predict molecular structures
This transforms biodiversity into a searchable, computable, and model-ready dataset.
One of the World’s Largest Natural Compound Libraries
Together, the data and model layers power Vecurate, NYB's Natural Compound Library—a curated, expanding dataset of bioactive compounds. When this compound library is used within Vecura, it covers a number of human protein targets.
Each compound is:
- Curated around natural, especially plant-derived compounds
- Enriched with AI-ready annotations
- Linked with bioactivity data and experimental validation
A Scalable Discovery Engine
From Screening to Discovery
Vecura enables large-scale in silico screening of compound libraries before wet lab validation. This allows:
- Faster identification of viable candidates
- Reduced experimental cost and time
- Shorter pathways from discovery to application
A Continuously Learning System
Vecura is designed to evolve continuously. Models are:
- Updated with new experimental and computational data
- Retrained as datasets expand
- Improved iteratively over time