Foundation Model Data Generation

Powering the Future of AI Drug Discovery with High-Fidelity Foundation Model Data.

Capabilities Overview

Foundation models for drug discovery—whether for generative chemistry, target prediction, or bioactivity modeling—demand one thing above all: vast, high-quality, structured data that is consistent, reproducible, and biologically relevant. Arctoris is uniquely positioned as the industry’s preferred wet-lab partner for AI-first biotech, delivering the kind of multi-modal, multi-scale, high-density datasets required to train and fine-tune robust foundation models.

Our proprietary automation platform, Ulysses®, is built to meet these demands—executing millions of data-rich experiments across biochemical, cellular, and structural assays with zero human variability and maximum annotation depth. Arctoris doesn’t just generate data; we generate AI-native intelligence at scale.

Why Foundation Models Need Better Data

To train a foundation model that is predictive, generalisable, and translatable, your data must be:

foundation model table

Key Features and Value

High-Throughput, High-Precision Automation: Generate data at industrial scale—across thousands of compounds and targets—with precision instrumentation and fully automated workflows.
Bespoke Dataset Design: Work with our team to define the structure, composition, and distribution of your training set—tailored to modality, mechanism, or target class.
Rich, FAIR-Compliant Output: All data is machine-readable, deeply annotated, and conforms to FAIR principles—ready for AI model ingestion.
Multi-Modal Interlinking: Link biophysical, biochemical, phenotypic, and structural data at the compound and target level to train models that understand mechanism, not just correlation.
Demonstrated Impact: Used by Isomorphic Labs to train their generative drug design models, leading to clinical candidates progressing in record time.
Closed-Loop Support: Integrate Arctoris into your active learning pipeline—run experimental validation and feed results directly into your next model iteration.

Key Capabilities

Ultra-scale biochemical and cellular screening (2M+ datapoints per program)
High-content phenotypic profiling with >5,700 image-derived features
Full kinetic and thermodynamic binding datasets (SPR, DSF, ITC)
Target engagement and MOA studies across 2D/3D and patient-derived systems
Structural biology integration: crystallography, cryo-EM, docking data
Statistical and mechanistic annotation: IC₅₀/EC₅₀, Kᴅ, Ki, Vmax, mechanism classification
Automated, reproducible execution of complex design-of-experiment protocols
Standardized export formats (JSON, Parquet, HDF5) for model training pipelines
Data feedback integration for active learning and foundation model fine-tuning

Validation Data

Project Tiger (Isomorphic Labs): Delivered >2.2M annotated datapoints over 65 targets
Model Predictivity: Arctoris datasets yielded significantly superior model performance compared to CRO and public data (e.g. ChEMBL)
Reproducibility Benchmarking: Outperformed intra-CRO and inter-dataset reproducibility metrics by >2x
Speed to Data: Delivered multi-assay panels for foundation model training within weeks
Clinical Impact: Underpinned multiple AI-designed candidates entering trials in 2025