Zachary Wu

I am currently a Staff Research Scientist at Google DeepMind, working on methods to identify, design, and improve proteins. Here, I have been fortunate to work with wonderful colleagues and collaborators on a number of projects, such as AlphaFold for protein complex prediction and AlphaProteo for protein design. Before this, I was a graduate student in Frances Arnold’s research group at the California Institute of Technology, where I co-established the machine learning subgroup. Prior to this, I did my undergraduate research at Cornell University with Matthew DeLisa.

And before that, I grew up in a small Texas town and spent a significant part of my childhood practicing piano and cello. My favorite composers are Bach, Beethoven, and Tchaikovsky.

If you’d like to drop a message, I am pretty responsive on twitter (@zvxywu), and slightly slower on linkedin.

Papers

Google Scholar

2026

Designing enzymes for new-to-nature chemistry and non-natural substrates with AlphaProtein Novo

Z. Wu, J. Abramson, T. Frerix, A. E. Chu, R. K. Zhang, L. Schulz, A. E. Danson, T. O. C. Kwan, W. K. Li, J. Kelly, Z.-Q. Li, R. G. Schneider, A. Thillaisundaram, H. Patani, V. F. Zambaldi, S. Singh, D. La, M. Domecillo, A. N. Mora, J. C. Reisenbauer, Y. Zhang, E. Papa, A. Žemgulytė, Y.-H. Wu, A. Žídek, J. Shi, G. Margand, N. Assem, K. Stephen, C. Emrich, P. Liu, L. Colwell, D. Hassabis, R. Fergus, F. H. Arnold, P. Kohli, J. Wang

bioRxiv Preprint First Author

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

Z.-Q. Li, R. K. Zhang, P.-P. Xie, J. Kelly, Y. Zhang, Z.-Y. Qin, T. Lambert, O. Mukhopadhyay, D. Hanley, A. E. Danson, J. Abramson, S. Singh, Z. Wu, P. Kohli, P. Liu, J. Wang, F. H. Arnold

bioRxiv Preprint Senior Author

2024

Accurate structure prediction of biomolecular interactions with AlphaFold 3

J. Abramson, J. Adler, J. Dunger, R. Evans, T. Green, A. Pritzel, O. Ronneberger, L. Willmore, A. J. Ballard, J. Bambrick, S. W. Bodenstein, D. A. Evans, C.-C. Hung, M. O’Neill, D. Reiman, K. Tunyasuvunakool, Z. Wu, A. Žemgulytė, E. Arvaniti, C. Beattie, O. Bertolli, A. Bridgland, A. Cherepanov, M. Congreve, A. I. Cowen-Rivers, A. Cowie, M. Figurnov, F. B. Fuchs, H. Gladman, R. Jain, Y. A. Khan, C. M. R. Low, K. Perlin, A. Potapenko, P. Savy, S. Singh, A. Stecula, A. Thillaisundaram, C. Tong, S. Yakneen, E. D. Zhong, M. Zielinski, A. Žídek, V. Bapst, P. Kohli, M. Jaderberg, D. Hassabis, J. M. Jumper

Nature 630, 493–500 Core Contributor

De novo design of high-affinity protein binders with AlphaProteo

V. Zambaldi, D. La, A. E. Chu, H. Patani, A. E. Danson, T. O. C. Kwan, T. Frerix, R. G. Schneider, D. Saxton, A. Thillaisundaram, Z. Wu, I. Moraes, O. Lange, E. Papa, G. Stanton, V. Martin, S. Singh, L. H. Wong, R. Bates, S. A. Kohl, J. Abramson, A. W. Senior, Y. Alguel, M. Y. Wu, I. M. Aspalter, K. Bentley, D. L. V. Bauer, P. Cherepanov, D. Hassabis, P. Kohli, R. Fergus, J. Wang

arXiv Preprint

2023

Machine learning for protein engineering

K. E. Johnston, C. Fannjiang, B. J. Wittmann, B. L. Hie, K. K. Yang, Z. Wu

Machine Learning in Molecular Sciences, 277–311 Corresponding Author

2021

Highly accurate protein structure prediction for the human proteome

K. Tunyasuvunakool, J. Adler, Z. Wu, T. Green, M. Zielinski, A. Žídek, A. Bridgland, A. Cowie, C. Meyer, A. Laydon, S. Velankar, G. J. Kleywegt, A. Bateman, R. Evans, A. Pritzel, M. Figurnov, O. Ronneberger, R. Bates, S. A. A. Kohl, A. Potapenko, A. J. Ballard, B. Romera-Paredes, S. Nikolov, R. Jain, E. Clancy, D. Reiman, S. Petersen, A. W. Senior, K. Kavukcuoglu, E. Birney, P. Kohli, J. Jumper, D. Hassabis

Nature 596, 590–596