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AI-Accelerated Molecule Discovery for Healthcare and Clean Energy

Research & Development · Healthcare · Energy Efficiency · Ecology

What it collects

About a measurement
Anonymized data
  • Large datasets of molecular structures, properties, and experimental measurements used to train and run the AI system for candidate molecule identification. No personal information is involved.
Run by
National Research Council Canada (NRC)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Not stated by the Helpful Places.

What it is for

Research & DevelopmentHealthcareEnergy EfficiencyEcology

This system uses artificial intelligence to accelerate the discovery of molecules relevant to healthcare treatments and clean energy technologies, including battery materials and carbon capture. It is operated by the National Research Council Canada in collaboration with Mila, McGill University, and SickKids Hospital. The system processes scientific data rather than personal information, and is currently in development. The use of AI in this project is not yet disclosed to users.

What it collects and what happens to it

Data taken in

About a measurement
Anonymized data
  • Large datasets of molecular structures, properties, and experimental measurements used to train and run the AI system for candidate molecule identification. No personal information is involved.

Processing

Not stated by the Helpful Places.

What it does

Deciding (Analytical AI)
Human decides
  • The AI sorts through large amounts of molecular data and the vast candidate space to predict and rank which molecules are the best fit for a given application. Researchers review and decide which candidates to pursue.

Outputs

A recommendation or prediction
Anonymized data
  • The system produces ranked recommendations of molecule candidates most likely to be useful for a given healthcare or clean energy application. These are advisory outputs reviewed by researchers, not binding decisions.

Run by

National Research Council Canada (NRC)
  • The National Research Council Canada (NRC) is the department responsible for deploying and operating this AI system for molecule discovery, in collaboration with Mila, McGill University, and SickKids Hospital.

Government of Canada AI Register — 2526-NRC-CNRC-006

Built by

Not stated by the Helpful Places.

Kept for

Not stated by the Helpful Places.

Shared with

Not stated by the Helpful Places.

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 TransparencyThis system is currently in development and AI use has not yet been disclosed to users. As the system matures, GC employees and collaborating researchers should have access to information about how the AI model operates and the logic behind its molecule ranking outputs. Contact the National Research Council Canada for more information.

Risks and safeguards

  • Societal & cultural harmAI-driven molecule prioritization could inadvertently narrow research focus toward commercially viable applications, potentially sidelining non-profitable but socially important areas of discovery.Safeguard: The project includes collaboration with academic and hospital partners (Mila, McGill, SickKids) to maintain diverse research objectives, and human researchers retain final decision-making authority over which molecular candidates to pursue.