Frontier labs

Pushmeet Kohli

Google DeepMind Science AI

Why we track: AlphaFold series

Career moves

No published career moves.

Papers

Paper2026-10-20 · Zenodo (CERN European Organization for Nuclear Research)

Release of Structural Data Used in AI-Based Enzyme Design Paper

Supplementary Data 2. Designed and predicted structures of all designs

Demis Hassabis · Pushmeet Kohli · Rob Fergus +35 more

What: The research team has released all structural data files covered in the paper on AI-driven enzyme design for novel chemical reactions.
Key result: The compressed file contains structures generated by the AI model, redesigned structures, and additional predicted structural data.
Why it matters: The released data allows related researchers to verify the results of AI-based enzyme design and conduct further research.AI summary

AI for science

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Paper2026-10-08 · Science

Automatically proving unsolved math problems with AI

Advancing mathematics research with AI-driven formal proof search

Pushmeet Kohli +20 more

What: We developed an AI agent that searches for mathematical proofs at scale by utilizing a language where a compiler verifies the proof steps.
Key result: This AI agent successfully and autonomously solved several unsolved math problems and conjectures.
Why it matters: It demonstrated the potential for AI to drive autonomous mathematical discoveries.AI summary

Agents & coding AI for science

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Paper2026-10-02 · arXiv (Cornell University)

Modular memory design for quantum computer error correction significantly reduces resource consumption

Low-Overhead Quantum Error Correction with Boundary-Connected Planar Modules

Pushmeet Kohli +13 more

What: Researchers divided the quantum processor into planar modules and demonstrated through simulations that utilizing inter-module connections can reduce the number of physical qubits required for quantum error correction by more than 10 times.
Key result: The new modular code design was shown to reduce physical qubit consumption by more than 10 times (and over 30 times in large-scale systems) compared to surface codes.
Why it matters: This study demonstrates the potential for high-density storage devices that maintain the manufacturing advantages of planar modules while leveraging the benefits of highly efficient codes.AI summary

HBM & memory

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Paper2026-10-02 · bioRxiv (Cold Spring Harbor Laboratory)

New enzyme chemistry technology combining artificial intelligence protein design and evolution

Design-Evolution Synergy Enables New-to-Nature Enzyme Chemistry

Pushmeet Kohli +16 more

What: Developed an enzyme that performs carbon-hydrogen amination reactions not found in nature by combining artificial intelligence-based protein design and evolution techniques.
Key result: Succeeded in synthesizing a new enzyme with programmable positional and enantioselectivity, high catalytic efficiency, and superior enzyme stability.
Why it matters: Demonstrates that protein design technology can be utilized to create enzymes that promote demanding chemical reactions.AI summary

AI for science

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This page compiles published papers, official announcements and press reports for information only. Paper summaries are written by AI from the abstract and reviewed by the editor; they may differ from the original, so check the paper for anything important. Nothing here is a recommendation to buy or sell any security or investment advice. Career moves cite official announcements or major press reports; some career records come from Wikidata (CC0). Summaries on this page were translated by AI from the editor-reviewed Korean version.