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Jinkō training

Workshop series​

Each workshop lasts two hours, with 30 to 45 minutes of instruction and demonstration followed by hands-on practice. Workshops can be taken independently unless a prerequisite is listed.

Participants receive workshop assets, datasets, and access to a Jinkō training environment. The program can be adapted to your team's models, data, roles, and operating environment.

1. Literature review and AI-assisted knowledge extraction​

Objective: Extract, organize, and use evidence from scientific literature with Jinkō and Kōhai.

Topics:

  • Organizing references and scientific data in a project.
  • Extracting equations, parameters, and tables from references.
  • Connecting extracted evidence to models and documentation.
  • Applying review checks to AI-assisted extraction.

Practice: Participants extract evidence from a defined set of publications and organize it for use in a modeling project.

Scientific literature extraction workflow in Jinkō

2. Model import, editing, and creation​

Objective: Import, inspect, edit, and create Computational Models in Jinkō.

Topics:

  • Importing SBML and supported pharmacometrics formats.
  • Building and documenting a model from scratch.
  • Inspecting model structure, units, parameters, and annotations.
  • Using Kōhai to prepare a first model version from a paper.

Practice: Participants import and edit a pharmacokinetic model, then review its structure and documentation.

Computational Model editing workshop in Jinkō

3. Data import and model calibration​

Objective: Prepare data and calibrate a model against quantitative evidence.

Topics:

  • Importing structured clinical or laboratory data.
  • Translating quantitative and qualitative evidence into scoring functions.
  • Configuring calibration parameters and optimization settings.
  • Reviewing fit, uncertainty, and model behavior.

Practice: Participants import a dataset, calibrate a model, create a Virtual Population, and inspect the result.

Recommended prerequisites: Workshops 1 and 2.

Model calibration workshop in Jinkō

4. Running trial simulations​

Objective: Configure, run, and analyze a Trial in Jinkō.

Topics:

  • Defining Protocol Arms, doses, measures, and a Virtual Population.
  • Running distributed simulations and monitoring progress.
  • Comparing scenarios and interpreting outcome distributions.
  • Visualizing survival, response, and dose-response results.

Practice: Participants simulate a simplified phase 3 trial and compare its treatment arms.

Recommended prerequisite: Workshop 2.

Trial simulation workshop in Jinkō

5. Collaboration and onboarding​

Objective: Prepare traceable work that modeling and non-modeling specialists can review together.

Topics:

  • Documenting model scope, assumptions, and limitations.
  • Using project roles, comments, links, and version history.
  • Preparing simulations and analyses for review.
  • Communicating results to scientific and operational stakeholders.

Practice: Participants create a shared project, assign roles, and review a Trial as a team.

Recommended prerequisite: Workshop 4.

Collaboration workshop in Jinkō

6. SDK and skills for programmatic access​

Objective: Operate Jinkō through the SDK, skills, and REST API.

Topics:

  • Configuring jinko-sdk, an API key, and a project ID.
  • Installing and using Jinkō Skills from an external agent harness.
  • Managing models, Virtual Populations, Protocols, and Trials programmatically.
  • Defining checks and handoff points for agent-assisted scientific work.

Practice: Participants create a Virtual Population and Trial, run a distributed simulation, and review the resulting project items in Jinkō.

Recommended prerequisite: Workshop 4.

Programmatic Jinkō workflow workshop