Accelerating scientific discovery with AI

This 2024 Science perspective, co-authored with David Silver and other DeepMind researchers, is the programmatic statement of DeepMind's post-AlphaFold mission. Hassabis argues that AI systems are now capable enough to serve as 'virtual scientists' — not just tools that execute human instructions, but systems that can independently formulate hypotheses, design experiments, analyze results, and propose next steps. The paper surveys the evidence: AlphaFold for structural biology, GNoME for materials discovery, AlphaDev for algorithm optimization, and AlphaCode for programming. It then outlines the components needed for a full AI scientist: the ability to conduct autonomous long-horizon research loops, generate and test hypotheses at machine speed, understand scientific context and prior work, and communicate results in human-readable form. The paper acknowledges that current systems are still narrow — AlphaFold can predict protein structures but can't design entirely new proteins with arbitrary function. But the trajectory is clear. Hassabis identifies three grand challenges where AI-accelerated discovery could be transformative within a decade: new antibiotics (drug resistance crisis), fusion energy materials, and carbon capture compounds.