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AI-Assisted Scientific Discovery for Drugs and Materials

Research & Development · Healthcare

What it collects

Operational data
Anonymized data
  • Scientific datasets describing material properties, molecular structures, experimental results, and known chemical compositions — no personal information is involved. Examples include crystallographic data for lithium cathode candidates and protein sequence databases for T-cell editing.

Government of Canada AI Register — 2526-NRC-CNRC-021

Run by
National Research Council Canada (NRC)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization
Your copy
You cannot see the data it holds about you. What you can do

What it is for

Research & DevelopmentHealthcare

This system uses AI to accelerate scientific research at the National Research Council of Canada, with current applications focused on discovering new battery materials (such as lithium cathodes) and designing T-cell protein edits for personalized cancer therapies. It is used by Government of Canada employees working with research collaborators under the AI for Design Challenge program. The system is in development and does not process personal information.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Scientific datasets describing material properties, molecular structures, experimental results, and known chemical compositions — no personal information is involved. Examples include crystallographic data for lithium cathode candidates and protein sequence databases for T-cell editing.

Government of Canada AI Register — 2526-NRC-CNRC-021

Processing

Classification & Prediction
  • Machine learning models predict or score properties of candidate molecules and materials — for example, predicting whether a lithium cathode composition has suitable electrochemical properties, or whether a protein edit will achieve a desired therapeutic effect.

Government of Canada AI Register — 2526-NRC-CNRC-021

What it does

Deciding (Analytical AI)
Human decides
  • The AI predicts, scores, or ranks candidate materials and molecular structures (e.g. lithium cathode compositions, T-cell protein edits) from structured scientific data. Researchers review and decide which candidates to pursue experimentally.

Government of Canada AI Register — 2526-NRC-CNRC-021

Creating (Generative AI)
Human decides
  • The system may generate novel molecular or material designs — such as new cathode compositions or protein edit sequences — that did not previously exist, for scientists to evaluate and validate through experiment.

Government of Canada AI Register — 2526-NRC-CNRC-021

Outputs

A recommendation or prediction
Anonymized data
  • The system produces rankings, predicted property scores, or suggested molecular/material designs for scientists to evaluate. These are advisory outputs that guide experimental prioritization — a human researcher decides which candidates to pursue.

Government of Canada AI Register — 2526-NRC-CNRC-021

Run by

National Research Council Canada (NRC)
  • The National Research Council Canada (NRC) is the federal government department developing and deploying this AI system for scientific discovery. It is accountable for the system's development and use under the AI for Design Challenge program.

Government of Canada AI Register — 2526-NRC-CNRC-021

Built by

Government of Canada
  • The system is developed by the Government of Canada in collaboration with external research partners as part of the AI for Design Challenge program. The register identifies the developer as the Government of Canada.

Government of Canada AI Register — 2526-NRC-CNRC-021

Kept for

Not stated by the Helpful Places.

Shared with

Not available to me
  • As a research tool used internally by GC employees and collaborators, outputs and intermediate data are not accessible to the general public.

Government of Canada AI Register — 2526-NRC-CNRC-021

Available to the accountable organization
  • Outputs and data produced by the system are available to NRC Canada and its designated research collaborators under the AI for Design Challenge program.

Government of Canada AI Register — 2526-NRC-CNRC-021

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

  • Right to Algorithmic TransparencyThe register notes that AI use is not disclosed to users (GC employees). As this system matures toward production, users should be informed of how the AI models work, what data they process, and what the limitations of their outputs are. Contact the National Research Council Canada for information about this system.

Risks and safeguards

  • Reputational harmAI predictions of molecular or material properties may be inaccurate, potentially leading researchers to invest effort in dead-end candidates or, in the healthcare domain, overstating the promise of a therapeutic approach. The system is in development; human expert review of all AI outputs is expected before any experimental or clinical decision is made.