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The authors develop a general method that combines machine learning and physics to construct macroscopic dynamics directly from microscopic observations, leading to an intuitive understanding of polymer stretching in elongational flow.
EmerGNN, a method to predict interactions for emerging drugs, may improve patient care and drug development by providing insight into the effects of using biomedical networks in interaction predictions.
Using registry data from Denmark, Lehmann et al. create individual-level trajectories of events related to health, education, occupation, income and address, and also apply transformer models to build rich embeddings of life-events and to predict outcomes ranging from time of death to personality.
A diffusion model that generates chemical reactions in 3D with all desired symmetries preserved is established and shown to reduce transition state search from days to seconds and complement intuition-based reaction exploration with generative AI.
Developing predictive mechanistic models in biology is challenging. Elektrum uses neural architecture search, kinetic models and transfer learning to discover CRISPR–Cas9 cleavage kinetics, achieving high performance and biophysical interpretability.
Signal peptides (SPs) are vital for protein–transmembrane communication. In this work, the authors introduce USPNet, a deep learning method based on a protein language model for SP prediction that shows both high sensitivity and efficiency, thereby contributing to the identification of novel SPs.
SRDTrans is a self-supervised denoising method for fluorescence images powered by spatial redundancy sampling and a dedicated transformer network that achieves good performance on fast dynamics and various imaging modalities.
VSSR-MC is a Markov chain method based on virtual adsorption sites that interfaces with a neural network force field to provide fast, accurate and comprehensive sampling of material surfaces.
Zhi Liu et al. develop a method to measure disparities in reporting delays in urban crowdsourcing systems, uncovering socioeconomic disparities and providing actionable insights for interventions that enhance the efficiency and equity of city services.
LOVAMAP is an analysis software that accurately identifies 3D pores in packed particle systems by exploiting information about the particle configuration as a basis for segmentation. Using the software, the authors were able to uncover striking relationships between particle and pore properties.
A hybrid machine learning–physics model is developed that reduces simulation cost by two orders of magnitude while retaining high ab initio accuracy, to predict free-energy transition states for hydrogen combustion reactions.
A physics-informed deep learning model, PBCNet, is proposed for predicting the relative binding affinity of ligands in order to improve guiding structure-based drug lead optimization.
A graph attention neural network tool is introduced to integrate multiple spatial transcriptomics data from different individuals, technologies and developmental stages, enabling consensus spatial domain identification and three-dimensional tissue reconstruction.
SurfGen is a structure-based drug design approach that delves into topological and geometric deep learning techniques for interaction learning, echoing the classical lock-and-key model.
GaUDI is a guided diffusion method for the design of molecular structures that features a flexible and scalable target function and that achieves high validity of generated molecules.
This work unifies an interdisciplinary literature of over 230 computational methods for measuring interactions from complex systems, revealing previously unreported theoretical connections and demonstrating practical benefits of broad methodological comparison.
KarmaDock, a deep learning approach, is proposed to improve the speed, accuracy and pose quality of molecular docking and is validated on multiple datasets and a real-world virtual screening.
The computational platform u-signal3D defines a shape-invariant representation of the spatial scales of molecular organization at the cell surface to enable comparison and machine learning of signaling across morphologically diverse cell populations.
A graph-based artificial intelligence model for urban planning outperforms human-designed plans in objective metrics, offering an efficient and adaptable collaborative workflow for future sustainable cities.
Real-world social networks are often ephemeral and subject to exogenous restructuring. Q. Su et al. show that dynamic networks can foster cooperative behavior.