AlphaFold Reveals Structure of Key Heart Disease Protein
DeepMind has used AlphaFold to determine the structure of a key protein implicated in heart disease. The announcement highlights a new scientific application of AlphaFold's protein structure prediction capabilities to cardiovascular research. This represents a continued expansion of AlphaFold's impact on biomedical discovery beyond its initial structural biology applications.
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AlphaFold: Five Years of Impact
DeepMind published a retrospective on AlphaFold's five-year impact on biological research and scientific discovery. The post surveys how the protein structure prediction system has accelerated science globally since its initial release. As a tier-1 source anniversary piece, it likely highlights cumulative usage statistics, downstream research enabled, and future directions.
Deep Learning with Proteins
A Hugging Face blog post covering the application of deep learning techniques to protein science, likely covering protein language models, structure prediction, and related tooling. Published in late 2022, this sits in the context of AlphaFold2's impact and the emerging ecosystem of protein ML models. The post likely surveys models, datasets, and frameworks available for computational biology on the Hugging Face platform.
AlphaGenome: DeepMind's Unified DNA Sequence Model for Regulatory Variant-Effect Prediction
DeepMind has introduced AlphaGenome, a new unified DNA sequence model designed to advance regulatory variant-effect prediction and improve understanding of genome function. The model is now available via API, making it accessible to researchers. AlphaGenome represents a significant step in applying large-scale AI to genomics, particularly for interpreting non-coding regulatory regions of the genome.
Accelerating discovery of liver disease mechanisms with Co-Scientist
DeepMind's Co-Scientist AI system is being used by researcher Filippo Menolascina to identify new treatment mechanisms for liver disease and explain differential drug response across patients. The application demonstrates Co-Scientist's utility in biomedical hypothesis generation and drug discovery workflows. This represents a concrete scientific use case for AI-assisted research in a clinical domain.
Finding the molecular switches behind new infectious diseases
DeepMind's Co-Scientist AI tool is being used by researcher Clare Bryant to identify genetic triggers in emerging infectious diseases. The application demonstrates Co-Scientist's utility in accelerating biological discovery, specifically in understanding molecular mechanisms underlying new pathogens. This represents a concrete scientific use case for AI-assisted research in infectious disease biology.
Uncovering repurposed medicines to fight liver fibrosis using Co-Scientist
A Stanford geneticist used Google DeepMind's Co-Scientist AI system to identify potential drug repurposing candidates for chronic liver disease and liver fibrosis. The work represents a real-world application of AI-assisted scientific discovery in a clinical domain. Co-Scientist is DeepMind's AI research assistant designed to accelerate hypothesis generation and experimental planning for scientists.
ESMFold2: The Bitter Lesson is Coming for Proteins — Alex Rives, BioHub
A Latent Space interview/commentary piece featuring Alex Rives of BioHub discussing ESMFold2 and the application of the 'bitter lesson' (scale and general methods beating hand-crafted inductive bias) to protein structure prediction and biology. The piece covers the tension between dataset scale versus domain-specific inductive bias in biological ML, and touches on world models and programmable biology. This represents a significant perspective from a leading researcher in protein language models on the next generation of biological foundation models.
DeepMind Discovers New Solutions to Century-Old Fluid Dynamics Problems
DeepMind has published a new AI-driven method for solving long-standing problems in fluid dynamics, targeting challenges that have remained open for over a century. The approach is positioned as a general framework for leveraging AI techniques to advance mathematics, physics, and engineering. This follows DeepMind's broader research program applying machine learning to fundamental scientific problems, including prior work on protein folding and mathematical reasoning.