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Article
Open AccessAccurate structure prediction of biomolecular interactions with AlphaFold 3
The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2–6. Here we describe ...
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Article
Open AccessGenerative models improve fairness of medical classifiers under distribution shifts
Domain generalization is a ubiquitous challenge for machine learning in healthcare. Model performance in real-world conditions might be lower than expected because of discrepancies between the data encountered...
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Article
Open AccessMathematical discoveries from program search with large language models
Large language models (LLMs) have demonstrated tremendous capabilities in solving complex tasks, from quantitative reasoning to understanding natural language. However, LLMs sometimes suffer from confabulation...
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Article
Publisher Correction: Scientific discovery in the age of artificial intelligence
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Article
Scientific discovery in the age of artificial intelligence
Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, hel** scientists to generate hypotheses, design experiments, collect and interpret ...
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Article
Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians
Predictive artificial intelligence (AI) systems based on deep learning have been shown to achieve expert-level identification of diseases in multiple medical imaging settings, but can make errors in cases accu...
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Article
Open AccessFaster sorting algorithms discovered using deep reinforcement learning
Fundamental algorithms such as sorting or hashing are used trillions of times on any given day1. As demand for computation grows, it has become critical for these algorithms to be as performant as possible. Where...
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Article
Open AccessDiscovering faster matrix multiplication algorithms with reinforcement learning
Improving the efficiency of algorithms for fundamental computations can have a widespread impact, as it can affect the overall speed of a large amount of computations. Matrix multiplication is one such primiti...
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Article
Open AccessMagnetic control of tokamak plasmas through deep reinforcement learning
Nuclear fusion using magnetic confinement, in particular in the tokamak configuration, is a promising path towards sustainable energy. A core challenge is to shape and maintain a high-temperature plasma within...
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Article
Open AccessAdvancing mathematics by guiding human intuition with AI
The practice of mathematics involves discovering patterns and using these to formulate and prove conjectures, resulting in theorems. Since the 1960s, mathematicians have used computers to assist in the discove...
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Article
Open AccessEffective gene expression prediction from sequence by integrating long-range interactions
How noncoding DNA determines gene expression in different cell types is a major unsolved problem, and critical downstream applications in human genetics depend on improved solutions. Here, we report substantia...
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Article
Open AccessHighly accurate protein structure prediction with AlphaFold
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique ...
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Article
Open AccessHighly accurate protein structure prediction for the human proteome
Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decad...
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Reference Work Entry In depth
Semantic Image Segmentation: Traditional Approach
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Living Reference Work Entry In depth
Semantic Image Segmentation: Traditional Approach
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Article
Improved protein structure prediction using potentials from deep learning
Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determ...
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Chapter and Conference Paper
Learning Shape Analysis
We present a data-driven verification framework to automatically prove memory safety of heap-manipulating programs. Our core contribution is a novel statistical machine learning technique that maps observed pr...
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Chapter and Conference Paper
Deep Disentangled Representations for Volumetric Reconstruction
We introduce a convolutional neural network for inferring a compact disentangled graphical description of objects from 2D images that can be used for volumetric reconstruction. The network comprises an encoder...
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Chapter and Conference Paper
Overcoming Occlusion with Inverse Graphics
Scene understanding tasks such as the prediction of object pose, shape, appearance and illumination are hampered by the occlusions often found in images. We propose a vision-as-inverse-graphics approach to han...
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Chapter and Conference Paper
Efficient Continuous Relaxations for Dense CRF
Dense conditional random fields (CRF) with Gaussian pairwise potentials have emerged as a popular framework for several computer vision applications such as stereo correspondence and semantic segmentation. By ...
