DeepMind Blog
Nov 25, 2025
AlphaFold: Five years of impact
Explore how AlphaFold has accelerated science and fueled a global wave of biological discovery.
Revealing a key protein behind heart disease
AlphaFold has revealed the structure of a key protein behind heart disease
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arXiv AI
Aug 18
Protein Structure Prediction: From Evolutionary Constraints to Generative Modeling
arXiv:2608. 16094v1 Announce Type: new Abstract: Accurate protein structure prediction is fundamental to structural biology because protein structure underlies molecular function and provides a basis for mechanistic interpretation.
arXiv Machine Learning
Jun 25
Two Stages of Folding: Convergent Mechanisms in AI Protein Folding Trunks
arXiv:2602. 06020v3 Announce Type: replace Abstract: How do protein structure prediction models fold proteins?
arXiv Machine Learning
Jul 20
Neural spectroscopy of AlphaFold2 reveals encoded protein conformational landscapes
arXiv:2607. 16087v1 Announce Type: new Abstract: AlphaFold2's 93 million parameters, shaped by the evolutionary record of protein structure encoded in the Protein Data Bank and in sequence alignments, are conventionally treated only as machinery for converting sequence to structure.
Towards Data Science
Jun 18
Proteins: A Mosaic Pattern to Rule Them All?
For decades, the existence of the hydrophobic core, a region in the 3D structure of proteins where hydrophobic amino acids reside together, has been considered a general property in proteins. What we have found now may extend that model.
arXiv AI
Jun 29
CPAgents: Agentic Composite Phenotype Generation for Cardiac Disease Association
arXiv:2606. 28179v1 Announce Type: cross Abstract: Identifying robust associations between cardiac imaging phenotypes and clinical diseases is fundamental to population-scale cardiovascular research and reliable risk stratification.
arXiv Machine Learning
Jul 1
Explainable Artificial Intelligence For The Detection and Characterisation of Stage B Heart Failure
arXiv:2606. 30665v1 Announce Type: cross Abstract: Stage B heart failure is characterized by asymptomatic structural or functional cardiac abnormalities.
arXiv AI
Jun 2
Motif-based morphology signatures for interpretable ECG screening and monitoring
arXiv:2606. 00107v1 Announce Type: cross Abstract: Electrocardiography (ECG) remains central to cardiovascular screening, yet interpretation remains largely manual and episodic.
arXiv AI
Jul 7
GenShin: Guiding Rational Liposome Design by Ranking Liposomal Protein Corona through a Docking-Pose-Free GNN
arXiv:2504. 13853v2 Announce Type: replace-cross Abstract: Rational design of lipid nanoparticles (LNPs) for tissue-specific delivery critically depends on predicting the composition of the protein corona that forms on the lipid surface after intravenous administration.
arXiv Machine Learning
Sep 22
ORION-CMR: On-scanner Reporting with Integrated Foundation Model for End-to-End Cardiac MRI Analysis and Interpretation
ORION‑CMR is a scanner‑native, end‑to‑end foundation model for cardiac MRI that performs sequence classification, ventricular function assessment, LGE detection, disease classification, and generates reports in about 90 seconds. Trained on 12.9 million images, it outperformed supervised baselines and a prior CMR foundation model, achieving state‑of‑the‑art LGE classification and scar segmentation. In a multi‑vendor clinical cohort, it reached an AUC of 0.96 for normal‑vs‑abnormal detection and 0.88 for multiclass disease classification, with generated reports agreeing 81.4% with expert interpretation.
arXiv AI
Sep 18
TorchCraft: Unified binder design by inverting an all-atom structure predictor
TorchCraft is a unified binder‑design framework that optimizes sequence logits using a frozen all‑atom structure predictor. It integrates confidence, contact, geometric, and sequence‑prior objectives within TorchFold to design minibinders, framework‑conditioned VHHs, cyclic peptides, and ligand‑binding proteins. Using pretrained AlphaFold 3 weights, TorchCraft produced experimentally validated binders across four targets without post‑hoc redesign, and computational tests confirmed its applicability to cyclic peptides and ligand‑conditioned pocket design.
arXiv AI
Sep 2
SymFold: Synergizing Evolutionary and Structural Priors for Accurate Protein Inverse Folding
SymFold introduces a symmetric dual‑path architecture that combines protein language models (PLMs) and multimodal protein language models (MPLMs) to iteratively guide protein sequence generation for inverse folding. By leveraging pretrained sequence evolution knowledge from PLMs and structural knowledge from MPLMs, the method improves upon the traditional serial pipeline where structure encoders produce coarse sequences refined by PLMs. Experiments on standard inverse‑folding benchmarks show state‑of‑the‑art performance, and ablation studies confirm the effectiveness of the symmetric design.