Breaking Eroom’s Law: The blueprint for AI-native clinical discovery

For decades, the pharmaceutical industry has been governed by Eroom’s Law: Drug discovery is becoming slower and more expensive even as technology improves. Despite deeper biological insight, faster cures remain elusive.

Breaking this cycle requires a new approach. Organizations must move beyond fragmented AI tools toward a unified, AI-native clinical discovery engine.

This white paper outlines the engineering blueprint for building a regulator-trusted discovery ecosystem and turning AI into a scalable advantage in modern drug development.

Why read this paper

This paper provides practical guidance for life sciences leaders navigating the intersection of drug discovery, AI, and digital engineering. It examines the infrastructure, governance, and strategic choices required to transform AI from experimentation into a trusted clinical capability.

What you’ll learn

The biological paradox
Why traditional R&D productivity is plateauing and how AI-native architecture can help break Eroom’s Law.
The buy vs. build dilemma
How to decide between owning your discovery platform and relying on external AI solutions.
Regulator-trusted AI
How emerging FDA credibility frameworks and GxP-compliant MLOps are shaping trusted AI.
An engineering framework for AI credibility
How a seven-step risk-based methodology establishes AI credibility and digital assurance.
Balancing speed with compliance
How to accelerate innovation while maintaining data integrity and regulatory confidence.

Success in the next era of drug discovery will depend on AI-driven engineering excellence. This paper outlines the actionable steps to move from experimental AI to an integrated, AI-native discovery ecosystem.

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