Alexander Pritzel

DeepMind (United Kingdom) — United Kingdom

Computer Science · Physical Sciences

27h-index83.2kcitations43works3824.72yr avg
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Research Topics

Reinforcement Learning in Robotics(10), Protein Structure and Dynamics(6), Black Holes and Theoretical Physics(5), Cosmology and Gravitation Theories(4), Model Reduction and Neural Networks(4)

Publications41 total

Addendum: Accurate structure prediction of biomolecular interactions with AlphaFold 3
Nature·2024· 248 citedOpen Access
Accurate structure prediction of biomolecular interactions with AlphaFold 3
Nature·2024· 11.0k citedOpen Access
Learning skillful medium-range global weather forecasting
Science·2023· 952 citedOpen Access
Predictions for AlphaMissense
2023· 4 citedOpen Access
Predictions for AlphaMissense
2023· 2 citedOpen Access
Accurate proteome-wide missense variant effect prediction with AlphaMissense
Science·2023· 1.8k cited
Source code for AlphaMissense
2023· 1 citedOpen Access
GraphCast: Learning skillful medium-range global weather forecasting
arXiv (Cornell University)·2022· 169 citedOpen Access
MultiScale MeshGraphNets
arXiv (Cornell University)·2022· 26 citedOpen Access
Normalizing flows for atomic solids
Machine Learning Science and Technology·2022Open Access
Applying and improving <scp>AlphaFold</scp> at <scp>CASP14</scp>
Proteins Structure Function and Bioinformatics·2021· 404 citedOpen Access
Protein complex prediction with AlphaFold-Multimer
2021· 3.9k cited
Author response for "Applying and improving AlphaFold at CASP14"
2021· 1 citedOpen Access
Highly accurate protein structure prediction for the human proteome
Nature·2021· 3.1k citedOpen Access
Highly accurate protein structure prediction with AlphaFold
Nature·2021· 42.1k citedOpen Access
Optimizing Free Energy Estimation with Machine Learning
Bulletin of the American Physical Society·2021
Never Give Up: Learning Directed Exploration Strategies
arXiv (Cornell University)·2020· 80 citedOpen Access
Meta-learning of Sequential Strategies
arXiv (Cornell University)·2019· 34 citedOpen Access
Fast deep reinforcement learning using online adjustments from the past
arXiv (Cornell University)·2018· 18 citedOpen Access
Meta-learning by the baldwin effect
Proceedings of the Genetic and Evolutionary Computation Conference Companion·2018· 27 citedOpen Access
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Frequent Co-authors

Demis Hassabis(15), Charles Blundell(13), John Jumper(11), Pushmeet Kohli(11), Andrew Senior(9), Andrew J. Ballard(8), Tim Green(7), Olaf Ronneberger(7), Kathryn Tunyasuvunakool(7), Augustin Žídek(7), Anna Potapenko(7), Alex Bridgland(7), Andrew Cowie(7), Rishub Jain(7), Oriol Vinyals(7), K Taki(6), Michael Figurnov(6), Jonas Adler(6), David Reiman(6), Pablo Sprechmann(6)