blockbuster-therapy-predictor

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

# 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

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

# 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