Nature Machine Intelligence最新文献

Deep learning methods have been considered promising for accelerating molecular screening in drug discovery and material design. Due to the limited availability of labelled data, various self-supervised molecular pre-training methods have been presented. Although many existing methods utilize common pre-training tasks in computer vision and natural language processing, they often overlook the fundamental physical principles governing molecules. In contrast, applying denoising in pre-training can be interpreted as an equivalent force learning, but the limited noise distribution introduces bias into the molecular distribution. To address this issue, we introduce a molecular pre-training framework called fractional denoising, which decouples noise design from the constraints imposed by force learning equivalence. In this way, the noise becomes customizable, allowing for incorporating chemical priors to substantially improve the molecular distribution modelling. Experiments demonstrate that our framework consistently outperforms existing methods, establishing state-of-the-art results across force prediction, quantum chemical properties and binding affinity tasks. The refined noise design enhances force accuracy and sampling coverage, which contribute to the creation of physically consistent molecular representations, ultimately leading to superior predictive performance.

Rapid, reliable and accurate interpretation of medical time series signals is crucial for high-stakes clinical decision-making. Deep learning methods offered unprecedented performance in medical signal processing but at a cost: they were compute intensive and lacked interpretability. We propose sparse mixture of learned kernels (SMoLK), an interpretable architecture for medical time series processing. SMoLK learns a set of lightweight flexible kernels that form a single-layer sparse neural network, providing not only interpretability but also efficiency, robustness and generalization to unseen data distributions. We introduce parameter reduction techniques to reduce the size of SMoLK networks and maintain performance. We test SMoLK on two important tasks common to many consumer wearables: photoplethysmography artefact detection and atrial fibrillation detection from single-lead electrocardiograms. We find that SMoLK matches the performance of models orders of magnitude larger. It is particularly suited for real-time applications using low-power devices, and its interpretability benefits high-stakes situations.

Understanding the spatial heterogeneity of tumours and its links to disease initiation and progression is a cornerstone of cancer biology. Presently, histopathology workflows heavily rely on hematoxylin and eosin and serial immunohistochemistry staining, a cumbersome, tissue-exhaustive process that results in non-aligned tissue images. We propose the VirtualMultiplexer, a generative artificial intelligence toolkit that effectively synthesizes multiplexed immunohistochemistry images for several antibody markers (namely AR, NKX3.1, CD44, CD146, p53 and ERG) from only an input hematoxylin and eosin image. The VirtualMultiplexer captures biologically relevant staining patterns across tissue scales without requiring consecutive tissue sections, image registration or extensive expert annotations. Thorough qualitative and quantitative assessment indicates that the VirtualMultiplexer achieves rapid, robust and precise generation of virtually multiplexed imaging datasets of high staining quality that are indistinguishable from the real ones. The VirtualMultiplexer is successfully transferred across tissue scales and patient cohorts with no need for model fine-tuning. Crucially, the virtually multiplexed images enabled training a graph transformer that simultaneously learns from the joint spatial distribution of several proteins to predict clinically relevant endpoints. We observe that this multiplexed learning scheme was able to greatly improve clinical prediction, as corroborated across several downstream tasks, independent patient cohorts and cancer types. Our results showcase the clinical relevance of artificial intelligence-assisted multiplexed tumour imaging, accelerating histopathology workflows and cancer biology.

The primary challenge in the development of large-scale artificial intelligence (AI) systems lies in achieving scalable decision-making—extending the AI models while maintaining sufficient performance. Existing research indicates that distributed AI can improve scalability by decomposing complex tasks and distributing them across collaborative nodes. However, previous technologies suffered from compromised real-world applicability and scalability due to the massive requirement of communication and sampled data. Here we develop a model-based decentralized policy optimization framework, which can be efficiently deployed in multi-agent systems. By leveraging local observation through the agent-level topological decoupling of global dynamics, we prove that this decentralized mechanism achieves accurate estimations of global information. Importantly, we further introduce model learning to reinforce the optimal policy for monotonic improvement with a limited amount of sampled data. Empirical results on diverse scenarios show the superior scalability of our approach, particularly in real-world systems with hundreds of agents, thereby paving the way for scaling up AI systems.

Nonlinear operators with long-distance spatiotemporal dependencies are fundamental in modelling complex systems across sciences; yet, learning these non-local operators remains challenging in machine learning. Integral equations, which model such non-local systems, have wide-ranging applications in physics, chemistry, biology and engineering. We introduce the neural integral equation, a method for learning unknown integral operators from data using an integral equation solver. To improve scalability and model capacity, we also present the attentional neural integral equation, which replaces the integral with self-attention. Both models are grounded in the theory of second-kind integral equations, where the indeterminate appears both inside and outside the integral operator. We provide a theoretical analysis showing how self-attention can approximate integral operators under mild regularity assumptions, further deepening previously reported connections between transformers and integration, as well as deriving corresponding approximation results for integral operators. Through numerical benchmarks on synthetic and real-world data, including Lotka–Volterra, Navier–Stokes and Burgers’ equations, as well as brain dynamics and integral equations, we showcase the models’ capabilities and their ability to derive interpretable dynamics embeddings. Our experiments demonstrate that attentional neural integral equations outperform existing methods, especially for longer time intervals and higher-dimensional problems. Our work addresses a critical gap in machine learning for non-local operators and offers a powerful tool for studying unknown complex systems with long-range dependencies.

Unexpected drug–drug interactions (DDIs) are important issues for both pharmaceutical research and clinical applications due to the high risk of causing severe adverse drug reactions or drug withdrawals. Many deep learning models have achieved high performance in DDI prediction, but model interpretability to reveal the underlying causes of DDIs has not been extensively explored. Here we propose MeTDDI—a deep learning framework with local–global self-attention and co-attention to learn motif-based graphs for DDI prediction. MeTDDI achieved competitive performance compared with state-of-the-art models. Regarding interpretability, we conducted extensive assessments on 73 drugs with 13,786 DDIs and MeTDDI can precisely explain the structural mechanisms for 5,602 DDIs involving 58 drugs. Besides, MeTDDI shows potential to explain complex DDI mechanisms and mitigate DDI risks. To summarize, MeTDDI provides a new perspective on exploring DDI mechanisms, which will benefit both drug discovery and polypharmacy for safer therapies for patients.