Hassabis, Demis

Highly accurate protein structure prediction with AlphaFold

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 43,764

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 30,357

Accurate structure prediction of biomolecular interactions with AlphaFold 3

Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 15,861

Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature

Artificial Intelligence in GamesArtificial Intelligence Applications

Cited by 15,612

Mastering the game of Go without human knowledge

Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 9,084

Overcoming catastrophic forgetting in neural networks

Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al. (2017) · Proceedings of the National Academy of Sciences

Domain Adaptation and Few-Shot LearningReinforcement Learning in Robotics

Cited by 7,899

AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022) · Nucleic Acids Research

Protein Structure and DynamicsEnzyme Structure and Function

Cited by 7,850

Protein complex prediction with AlphaFold-Multimer

Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory)

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 3,808

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 3,562

Grandmaster level in StarCraft II using multi-agent reinforcement learning

Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al. (2019) · Nature

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 3,455

Improved protein structure prediction using potentials from deep learning

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 3,378

International evaluation of an AI system for breast cancer screening

McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature

AI in cancer detectionDigital Radiography and Breast Imaging

Cited by 3,287

Highly accurate protein structure prediction for the human proteome

Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 3,025

Clinically applicable deep learning for diagnosis and referral in retinal disease

De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 2,639

Accurate proteome-wide missense variant effect prediction with AlphaMissense

Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum, T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., et al. (2023) · Science

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 2,163

AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences

Varadi, M., Bertoni, D., Magana, P., Paramval, U., Pidruchna, I., Radhakrishnan, M., Tsenkov, M., Nair, S., Mirdita, M., Yeo, J., et al. (2024) · Nucleic Acids Research

Protein Structure and DynamicsEnzyme Structure and Function

Cited by 2,052

Neuroscience-Inspired Artificial Intelligence

Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron

Neural Networks and ApplicationsCognitive Computing and Networks

Cited by 1,557

Patients with hippocampal amnesia cannot imagine new experiences

Hassabis, D., Kumaran, D., Vann, S. D., & Maguire, E. A. (2007) · Proceedings of the National Academy of Sciences

Memory and Neural MechanismsMemory Processes and Influences

Cited by 1,414

Hybrid computing using a neural network with dynamic external memory

Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwińska, A., Colmenarejo, S. G., Grefenstette, E., Ramalho, T., Agapiou, J., et al. (2016) · Nature

Neural Networks and ApplicationsTopic Modeling

Cited by 1,412

The future of memory: remembering, imagining, and the brain

Schacter, D. L., Addis, D. R., Hassabis, D., Martin, V. C., Spreng, R. N., & Szpunar, K. K. (2012) · Neuron

Memory Processes and InfluencesMemory and Neural Mechanisms

Cited by 1,398

Deconstructing episodic memory with construction

Hassabis, D., & Maguire, E. A. (2007) · Trends in Cognitive Sciences

Memory Processes and InfluencesMemory and Neural Mechanisms

Cited by 1,229

Mastering Atari, Go, chess and shogi by planning with a learned model

Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., et al. (2020) · Nature

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 1,214

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., et al. (2019) · arXiv

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 1,214

Gemma 3 Technical Report

Gemma Team, Kamath, A., Ferret, J., Pathak, S., Vieillard, N., Merhej, R., Perrin, S., Matejovicova, T., Ramé, A., Rivière, M., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 1,156