Apply AI to optimize R&D portfolio decisions — from target identification and asset valuation to pipeline prioritization and resource allocation.
Before
Select a publicly disclosed Phase 2 pipeline asset from a pharma company's investor presentation. Use the PTRS estimation prompt to estimate success probabilities. Then compare your AI-generated estimates with published industry benchmarks (BIO/QLS Advisors publishes annual clinical success rate data). Note where the specific program characteristics cause you to deviate from baseline rates.
After
R&D portfolio decisions hinge on estimating two things: the probability that a program will succeed (PTRS) and the commercial value if it does (risk-adjusted NPV). AI improves both estimates by analyzing thousands of historical development programs to calibrate success probabilities, synthesizing competitive intelligence across dozens of pipeline competitors, and stress-testing valuation assumptions through automated scenario analysis. Better inputs lead to better decisions — and in pharma R&D, where average program costs exceed $1B, even marginal improvements in decision quality are worth hundreds of millions.
Tip
Be specific about what you need. The more context you provide, the better the result.
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AI for Target & Asset Valuation
Use AI to estimate probability of technical and regulatory success (PTRS) for pipeline assets
AI for Pipeline Prioritization & Resource Allocation
Apply AI to multi-criteria portfolio optimization that balances risk, reward, and strategic fit
AI for BD&L and External Innovation
Use AI to scan and evaluate external innovation opportunities (licensing, acquisition, partnerships)