Blockbuster Therapy Predictor

Source: https://github.com/aipoch/medical-research-skills

Blockbuster Therapy Predictor

Comprehensive analytics tool for forecasting breakthrough therapeutic technologies by integrating multi-dimensional data sources including clinical development pipelines, intellectual property landscapes, and capital market indicators.

When to Use

• Use this skill when the task needs Comprehensive analytics tool for forecasting breakthrough therapeutic technologies by integrating multi-dimensional data sources including clinical development pipelines, intellectual property landscapes, and capital mar.
• Use this skill for evidence insight tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

See ## Features above for related details.

• Scope-focused workflow aligned to: Comprehensive analytics tool for forecasting breakthrough therapeutic technologies by integrating multi-dimensional data sources including clinical development pipelines, intellectual property landscapes, and capital mar.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Evidence Insight/blockbuster-therapy-predictor"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Features

• Multi-Source Data Integration: Aggregates clinical trials, patents, and funding data
• Predictive Scoring: Calculates Blockbuster Index combining maturity, market potential, and momentum
• Technology Landscape Mapping: Tracks 10+ emerging therapeutic platforms
• Investment Intelligence: Provides data-driven R&D and investment recommendations
• Trend Analysis: Identifies acceleration patterns and inflection points

Usage

Basic Usage

# Run complete analysis with all technologies
python scripts/main.py

# Analyze specific technologies
python scripts/main.py --tech PROTAC,mRNA,CRISPR

# Output in JSON format
python scripts/main.py --output json

Parameters

Advanced Usage

# Analyze high-potential technologies only (index ≥70)
python scripts/main.py \
  --threshold 70 \
  --output json \
  --save high_potential_report.json

# Quick analysis of specific platforms
python scripts/main.py \
  --mode quick \
  --tech CAR-T,ADC,Bispecific \
  --output console

Output

Console Output

🏆 BLOCKBUSTER THERAPY PREDICTOR Report
Generated: 2026-02-15 10:30:00
Technologies analyzed: 10

📊 Technology Rankings
Rank  Technology       Blockbuster Index    Maturity    Market Potential    Momentum    Recommendation
🥇 1   mRNA             85.2                 78.5        92.1                88.0        Strongly Recommended
🥈 2   CAR-T            82.3                 85.2        78.5                75.0        Strongly Recommended
🥉 3   CRISPR           79.8                 72.3        88.2                68.0        Recommended

JSON Output Structure

{
  "generated_at": "2026-02-15T10:30:00",
  "total_routes": 10,
  "rankings": [
    {
      "rank": 1,
      "tech_name": "mRNA",
      "blockbuster_index": 85.2,
      "maturity_score": 78.5,
      "market_potential_score": 92.1,
      "momentum_score": 88.0,
      "recommendation": "Strongly Recommended",
      "key_drivers": ["Multiple Phase III trials", "Rapid patent growth"],
      "risk_factors": ["Regulatory uncertainties"],
      "timeline_prediction": "First product expected in 2-4 years"
    }
  ]
}

Scoring Methodology

Blockbuster Index Formula

Blockbuster Index = (Market Potential × 0.5) + (Maturity × 0.3) + (Momentum × 0.2)

Component Scores

Investment Recommendation Thresholds

Supported Technologies

Technical Difficulty: MEDIUM

⚠️ AI independent acceptance status: manual inspection required This skill requires:

• Python 3.8+ environment
• Basic understanding of biotech investment analysis
• Access to clinical trial, patent, and funding databases (optional)

Required Python Packages

pip install -r requirements.txt

Requirements File

dataclasses
enum

Risk Assessment