Artificial Intelligence 🤖

Inspirational Works

Accurate structure prediction of biomolecular interactions with AlphaFold 3

Accurate structure prediction of biomolecular interactions with AlphaFold 3
Abramson, Adler, Dunger, et al.
Nature, 630, 493–500 (2024)

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Advancing regulatory variant effect prediction with AlphaGenome

Advancing regulatory variant effect prediction with AlphaGenome
Avsec, Latysheva, Cheng, Novati, Taylor, Ward, Bycroft, Nicolaisen, Arvaniti, Pan, Thomas, Dutordoir, Perino, De, Karollus, Gayoso, Sargeant, Mottram, Wong, Drotár, Kosiorek, Senior, Tanburn, Applebaum, Basu, Hassabis, Kohli
Nature, 649, 1206-1218 (2026)

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Mapping the topography of spatial gene expression with interpretable deep learning

Mapping the topography of spatial gene expression with interpretable deep learning
Chitra, Arnold, Sarkar, et al.
Nat Methods, 22, 298–309 (2025)

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Discovering faster matrix multiplication algorithms with reinforcement learning

Discovering faster matrix multiplication algorithms with reinforcement learning
Fawzi, Balog, Huang, Hubert, Romera-Paredes, Barekatain, Novikov, R Ruiz, Schrittwieser, Swirszcz, Silver, Hassabis, Kohli
Nature, 610, 47-53 (2022)

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Is regulatory science ready for artificial intelligence?

Is regulatory science ready for artificial intelligence?
Hartung, Whelan, Tong, et al.
npj Digit. Med., 8, 200 (2025)

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Squidiff: predicting cellular development and responses to perturbations using a diffusion model

Squidiff: predicting cellular development and responses to perturbations using a diffusion model
He, Zhu, Tavakol, et al.
Nat Methods, 23, 65–77 (2026)

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MedAgentBench: a virtual EHR environment to benchmark medical LLM agents

MedAgentBench: a virtual EHR environment to benchmark medical LLM agents
Jiang, Black, Geng, Park, Zou, Ng, Chen
NEJM AI, 2, AIdbp2500144 (2025)

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Highly accurate protein structure prediction with AlphaFold

Highly accurate protein structure prediction with AlphaFold
Jumper, Evans, Pritzel, et al.
Nature, 596, 583–589 (2021)

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Neural general circulation models for weather and climate

Neural general circulation models for weather and climate
Kochkov, Yuval, Langmore, Norgaard, Smith, Mooers, Klöwer, Lottes, Rasp, Düben, Hatfield, Battaglia, Sanchez-Gonzalez, Willson, Brenner, Hoyer
Nature, 632, 1060-1066 (2024)

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A generative deep learning approach to de novo antibiotic design

A generative deep learning approach to de novo antibiotic design
Krishnan, Anahtar, Valeri, et al.
Cell, 188, 5962–5979.e22 (2025)

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Human-level control through deep reinforcement learning

Human-level control through deep reinforcement learning
Mnih, Kavukcuoglu, Silver, Rusu, Veness, Bellemare, Graves, Riedmiller, Fidjeland, Ostrovski, Petersen, Beattie, Sadik, Antonoglou, King, Kumaran, Wierstra, Legg, Hassabis
Nature, 518, 529-33 (2015)

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Probabilistic weather forecasting with machine learning

Probabilistic weather forecasting with machine learning
Price, Sanchez-Gonzalez, Alet, Andersson, El-Kadi, Masters, Ewalds, Stott, Mohamed, Battaglia, Lam, Willson
Nature, 637, 84-90 (2025)

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Language model-guided anticipation and discovery of mammalian metabolites

Language model-guided anticipation and discovery of mammalian metabolites
Qiang, Wang, Lu, et al.
Nature, 651, 211–220 (2026)

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Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Roberts, Driggs, Thorpe, et al.
Nat Mach Intell, 3, 199–217 (2021)

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Mathematical discoveries from program search with large language models

Mathematical discoveries from program search with large language models
Romera-Paredes, Barekatain, Novikov, Balog, Kumar, Dupont, Ruiz, Ellenberg, Wang, Fawzi, Kohli
Nature, 625, 468-475 (2024)

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Deep Visual Proteomics maps proteotoxicity in a genetic liver disease

Deep Visual Proteomics maps proteotoxicity in a genetic liver disease
Rosenberger, Mädler, Thorhauge, et al.
Nature, 642, 484–491 (2025)

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Learning the natural history of human disease with generative transformers

Learning the natural history of human disease with generative transformers
Shmatko, Jung, Gaurav, et al.
Nature, 647, 248–256 (2025)

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AI models collapse when trained on recursively generated data

AI models collapse when trained on recursively generated data
Shumailov, Shumaylov, Zhao, et al.
Nature, 631, 755–759 (2024)

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MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery

MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery
Siebenmorgen, Menezes, Benassou, et al.
Nat Comput Sci, 4, 367–378 (2024)

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

Mastering the game of Go with deep neural networks and tree search
Silver, Huang, Maddison, Guez, Sifre, van den Driessche, Schrittwieser, Antonoglou, Panneershelvam, Lanctot, Dieleman, Grewe, Nham, Kalchbrenner, Sutskever, Lillicrap, Leach, Kavukcuoglu, Graepel, Hassabis
Nature, 529, 484-9 (2016)

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InterPLM: discovering interpretable features in protein language models via sparse autoencoders

InterPLM: discovering interpretable features in protein language models via sparse autoencoders
Simon, Zou
Nat Methods, 22, 2107–2117 (2025)

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AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination

AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination
Terwilliger, Liebschner, Croll, et al.
Nat Methods, 21, 110–116 (2024)

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A multimodal sleep foundation model for disease prediction

A multimodal sleep foundation model for disease prediction
Thapa, Kjaer, He, et al.
Nat Med, 32, 752–762 (2026)

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Highly accurate protein structure prediction for the human proteome

Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, Adler, Wu, et al.
Nature, 596, 590–596 (2021)

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Predicting multiple conformations via sequence clustering and AlphaFold2

Predicting multiple conformations via sequence clustering and AlphaFold2
Wayment-Steele, Ojoawo, Otten, et al.
Nature, 625, 832–839 (2024)

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Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning

Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning
Weis, Cuénod, Rieck, et al.
Nat Med, 28, 164–174 (2022)

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An explainable deep learning platform for molecular discovery

An explainable deep learning platform for molecular discovery
Wong, Omori, Li, et al.
Nat Protoc, 20, 1020–1056 (2025)

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Discovery of a structural class of antibiotics with explainable deep learning

Discovery of a structural class of antibiotics with explainable deep learning
Wong, Zheng, Valeri, et al.
Nature, 626, 177–185 (2024)

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Deep learning large-scale drug discovery and repurposing

Deep learning large-scale drug discovery and repurposing
Yu, Li, et al.
Nat Comput Sci, 4, 600–614 (2024)

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AI Governance in a Complex and Rapidly Changing Regulatory Landscape: A Global Perspective

AI Governance in a Complex and Rapidly Changing Regulatory Landscape: A Global Perspective
Zaidan, Ibrahim
Humanit Soc Sci Commun, 11, 1121 (2024)

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Artificial intelligence in drug development

Artificial intelligence in drug development
Zhang, Yang, Wang, et al.
Nat Med, 31, 45–59 (2025)

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An open source machine learning framework for efficient and transparent systematic reviews

An open source machine learning framework for efficient and transparent systematic reviews
van de Schoot, de Bruin, Schram, et al.
Nat Mach Intell, 3, 125–133 (2021)

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