AI at Eli Lilly: How Diogo Rau Sees the Future of Drug Discovery and Engineering Talent

Diogo Rau on AI’s Role in Pharmaceutical Discovery

Diogo Rau, Global Chief Information and Technology Officer at Eli Lilly, brings a straightforward perspective on artificial intelligence in drug discovery. With experience spanning consulting firms, Apple, and a start-up, Rau now leads transformative efforts at one of pharma’s giants. His core message: investing in AI is essential for survival in the pharmaceutical industry.

AI’s Growing Impact on Drug Research

Rau is optimistic about AI's future in medicine. Although no AI-generated molecule is yet in patient use, the lengthy drug development process means results take time. Currently, AI helps identify promising molecules in early discovery stages. Eli Lilly dedicates over 25% of its $14 billion R&D budget to genetic medicine, a long-term investment aiming for breakthroughs beyond immediate revenue.

Designing Proteins with AI

Proteins are fundamental to biological functions, and AI advancements like AlphaFold have made it possible to predict protein structures from genetic sequences. Rau highlights a shift from prediction to design: instead of reading genetic codes, scientists are beginning to specify desired protein properties and ask what sequences produce them. This approach opens new avenues for targeted drug design.

Challenges in AI-Driven Molecule Discovery

One major hurdle is the scarcity of comprehensive datasets. Most published data focuses on successful drug candidates, while failures remain unpublished. For AI to be effective, it needs examples of both successes and failures to avoid chaotic or misleading results.

AI’s Advantage Over Human Exploration

The chemical space for drug discovery is vast—far larger than the number of stars in the universe. Human researchers have limited time and prioritize compounds most likely to succeed. AI, however, can explore unlikely candidates tirelessly, increasing chances of discovering novel molecules that humans might never consider.

The Value of “Lazy” Engineers in AI

Rau values engineers who dislike repetitive tasks because they seek to automate them. This mindset is crucial for progress in AI-driven research. Engineers who prefer action over endless planning help accelerate innovation by focusing on practical solutions rather than prolonged discussions.

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