Case Study: Hypercholesterolemia Drug Discovery

Open in Google Colab

Explore a full AI-driven drug discovery workflow using ToolUniverse and Gemini 2.5 Pro!

This tutorial walks you through the hypercholesterolemia case study presented in our paper, “Democratizing AI scientist systems using ToolUniverse.” You can run the entire workflow interactively in Google Colab: ToolUniverse Case Study Notebook.

What does the case study cover?

  • End-to-end drug discovery pipeline for hypercholesterolemia

  • AI scientist reasoning: See how Gemini 2.5 Pro uses ToolUniverse tools to select targets, analyze tissue expression, choose drugs, and optimize molecules

  • Human-in-the-loop: Integrates expert feedback for final decision making

  • Tool calls and outputs: All code and tool calls are shown step-by-step

Sections in the tutorial:

  1. Target Selection: Identify top protein targets for hypercholesterolemia using OpenTargets and literature review

  2. Target Characterization: Analyze tissue/organ expression of selected target to assess drug side effects

  3. Drug Selection: Review current drugs targeting the protein and select a molecule to optimize

  4. Analog Search & ADMET Prediction: Find structural analogs and predict pharmaceutical properties

  5. IP Review: Check intellectual property status for the optimized molecule

How to use this tutorial:

  • Open the Colab notebook: Click the badge or link above

  • Follow the step-by-step code: Each section includes code, tool calls, and reasoning

  • Learn by example: See how ToolUniverse enables agentic workflows for real-world biomedical research