# 04_QUEST_OPEN_TARGETS

Congratulations on reaching the final, open-ended quest! In the previous steps, we reproduced the core analysis from the SCimilarity paper and extended it to new datasets. Now, we're transitioning from pure data analysis to **Competitive Intelligence and Translational Strategy**.

In this quest, you will investigate the clinical viability of the biological markers identified in your earlier analysis. You will utilize advanced LLM concepts, such as Model Context Protocols (MCPs) or external APIs (like Open Targets or ClinicalTrials.gov), to generate a comprehensive clinical landscape report.

In this document, overwrite anything in `{{}}` to get Gemini to work with you. Because this is the most open-ended quest, the LLM will rely heavily on the specifics of your SPEC to assist you. 

---

## 1. Background and Goals

**Objective:** Extract actionable competitive intelligence for key targets identified via SCimilarity (e.g., Fibrosis-Associated Macrophage markers) by querying external databases and structuring the results into a visual report.

**Example Scenarios:**
- **Target Intel:** Identify competitive crowding and current clinical phases for `SPP1`, `MARCO`, and `CD163`.
- **Diligence/Whitespace:** Assess if the identified novel targets are already under development, and if trials are actually stratifying patients using these biomarkers.

**Deliverables:**
1. {{Describe the specific Python script, Agent logic, or skill you intend to build}}
2. {{Describe the final output format. E.g., An HTML dashboard with specific charts, or a Markdown landscape report, that is incorporated as a sub-page of the original HTML report}}

## 2. Tech Stack and Project Structure

You can continue using the existing project structure and virtual environment. For this quest, you will need to integrate external APIs or Agentic frameworks. 

**Suggested Tooling Approaches (Choose one or propose your own):**
- **Direct API querying:** Using `requests` and Python to query the Open Targets GraphQL API or ClinicalTrials.gov REST API.
- **K-Dense AI:** An open-source Agent Skills framework for querying public databases (ClinicalTrials.gov, Open Targets, ChEMBL, FDA).
- **Gosset MCP:** A commercial MCP providing curated biotech competitive intelligence (if you have access/API keys).

{{Specify which tools, APIs, and libraries you will use to build your pipeline.}}

## 3. Inputs & Target Selection

You need to select specific targets from the SCimilarity paper (or your novel dataset analysis) to investigate. For example, the paper highlights Fibrosis-Associated Macrophage markers.

**Targets Chosen:**
- {{Target 1: Provide the gene symbol and the biological/SCimilarity justification for choosing it}}
- {{Target 2: Provide the gene symbol and the biological/SCimilarity justification for choosing it}}
- {{Target 3: Provide the gene symbol and the biological/SCimilarity justification for choosing it}}

**Research Questions:**
{{Define the exact questions your code should answer. For example: Are there any trials using these markers for patient stratification/inclusion? What are the top indications by phase?}}

## 4. Outputs

{{Describe exactly what the resulting report should look like. What sections must it include?}}

*Example considerations for an HTML report:*
*   **Landscape Overview:** Known drugs and highest phase.
*   **Trial Context:** Number of trials by disease in descending order with stacked barplots (Phase 0/1/2/3/Approved).
*   **Precision Medicine:** Analysis of inclusion criteria (e.g., checking for "Biopsy confirmed" or "SPP1 high").
*   **Outcomes:** Efficacy and safety stats if available.

## 5. Implementation Strategy

### Connecting to APIs / MCPs
{{How will your script or agent fetch the data? Describe the prompt strategy or specific API calls required.}}

### Data Processing
{{How will you handle raw JSON responses? How will you extract and aggregate trial phases and indications?}}

### Visualizations & Report Generation
{{Describe how you will generate the final visual report. Will you generate static images, an interactive HTML file with Plotly, or rely entirely on LLM summarization?}}

## 6. Open Exploration

Because you are at the end of the workshop, there are few restrictions! If you finish your target analysis early, use this space to outline other advanced AI workflows, custom agents, or biological questions you'd like to explore with Gemini.

{{Your open-ended ideas here}}
copyright: © 2026 Sonia Timberlake & Ryan Bellmore
license: Proprietary - Authorized Workshop Participants Only
distribution_allowed: false