NVIDIA Healthcare United States

Terray Therapeutics

Terray Therapeutics needed to search an almost infinite chemical space with generative models, but multi-node training, terabyte-scale datasets, and iterative hyperparameter experiments were too cumbersome on a mix of on-premises and generic cloud systems.

32-GPU cluster
3–4x
4x more utilization
50 billion experimental biophysical measurements

Reported impact

Business outcome

Terray increased resource utilization by 4x compared with alternative clouds, cut model training from a week to a day, and later used cuEquivariance to accelerate TerraBind by 3x to 4x with about 70% cost savings, giving researchers far more room to search for rare high-value molecules.

  • Cost Reduction
  • Productivity Improvement
  • Speed to Market

Solution

Terray built the COATI and TerraBind model-development workflow on NVIDIA DGX Cloud, NVIDIA Base Command Platform, and later NVIDIA cuEquivariance. The company starts from a massive experimental engine that measures hundreds of millions of molecule-target interactions daily and stores more than 50 billion biophysical measurements. Those data are used to train COATI, a multimodal encoder-decoder that translates molecules into numerical representations and back again so the company can generate new compounds with desired properties. DGX Cloud simplified multi-node training and Base Command Platform standardized job launch and orchestration, which let Terray move from manual remote-cluster management to push-button 32-GPU experiments. Terray then accelerated TerraBind models further with cuEquivariance, improving potency-prediction performance while cutting the time and cost required to iterate on new drug candidates.

Data flow

Terray's experimental measurements feed COATI training on DGX Cloud, Base Command orchestrates large distributed runs, generated molecule embeddings flow into screening and optimization loops, and cuEquivariance-accelerated TerraBind models return potency and structure predictions for new candidates.

Solution architecture

4 components · 2 layers
  1. Compute
    • NVIDIA DGX Cloud Provides Terray with a dedicated multi-node training environment for generative chemistry models.
    • COATI Encodes and decodes molecular structures so Terray can generate compounds with target properties.
    • NVIDIA cuEquivariance Accelerates TerraBind kernels for faster potency-prediction training and inference.
  2. Orchestration
    • NVIDIA Base Command Platform Schedules and orchestrates Terray's cloud training jobs and dataset workflows.

Architecture clues

  • Researchers can now scale from a single node to a 32-GPU cluster with push-button simplicity.
  • Terray's platform measures hundreds of millions of interactions daily.
  • cuEquivariance accelerated TerraBind by 3–4x compared to PyTorch.

Evidence from the source

DGX Cloud offered us a solution that worked seamlessly with the ease and simplicity of cloud.
It used to take us a week to train a model, and we were getting it done in a day.
Terray Therapeutics has further advanced its computational capabilities by integrating NVIDIA's cuEquivariance technology into its proprietary TerraBind models.
The Terray platform measures hundreds of millions of interactions between small molecules and biological targets daily, with a growing database of 50 billion experimental biophysical measurements.
Transparency Evidence & methodology 100/100 reference value · 92% confidence · 2 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency9/10
    2 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. NVIDIA

    Terray Therapeutics customer story

    First-party
    https://www.nvidia.com/en-us/case-studies/generative-ai-for-small-molecule-drug-discovery/
    Source type
    Vendor-published customer story
    Published
    Mar 10, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date
  2. LinkedIn

    Terray Therapeutics | LinkedIn

    Independent secondary
    https://www.linkedin.com/company/terray-therapeutics/

    numberOfEmployees":{"value":142,"@type":"QuantitativeValue"}

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

  • Published on source page Mar 10, 2026 · NVIDIA · First-party
    Open date source
Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.