| name | blockbuster-therapy-predictor |
| description | Predict which early-stage biotechnology platforms (PROTAC, mRNA, gene editing, etc.) have the highest potential to become blockbuster therapies. Analyzes clinical trial progression, patent landscape maturity, and venture capital funding trends to generate investment and R&D prioritization scores. Trigger when: User asks about technology investment potential, platform selection, or therapeutic modality comparison. |
| version | 1.0.0 |
| category | Pharma |
| tags | ["investment","prediction","biotech","clinical-trials","patents"] |
| author | AIPOCH |
| license | MIT |
| status | Draft |
| risk_level | Medium |
| skill_type | Tool/Script |
| owner | AIPOCH |
| reviewer | |
| last_updated | 2026-02-15 |
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.
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
python scripts/main.py
python scripts/main.py --tech PROTAC,mRNA,CRISPR
python scripts/main.py --output json
Parameters
| Parameter | Type | Default | Required | Description |
|---|
--mode | str | full | No | Analysis mode: full or quick |
--tech | str | None | No | Comma-separated list of technologies to analyze |
--output | str | console | No | Output format: console or json |
--threshold | float | 0 | No | Minimum blockbuster index threshold (0-100) |
--save | str | None | No | Save report to file path |
Advanced Usage
python scripts/main.py \
--threshold 70 \
--output json \
--save high_potential_report.json
python scripts/main.py \
--mode quick \
--tech CAR-T,ADC,Bispecific \
--output console
