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Cooperation is crucial for human prosperity, and population structure fosters it through pairwise interactions and coordinated behavior in larger groups. A recent study explores the evolution of behavioral strategies in higher-order population structures, including pairwise and multi-way interactions to reveal that higher-order interactions promote cooperation across networks, especially when they are formed by conjoined communities.
A recent study introduces a machine learning approach to investigate the effects of mutations on protein sensors commonly employed in fluorescence microscopy, thus enabling the discovery of high-performance sensors.
A neural network-based method for advancing orbital-free density functional theory (OFDFT) is developed, which reaches DFT accuracy and maintains lower cost complexity.
Determining what guest can effectively bind in a host, or the reverse, is a central challenge in chemistry. To address this, an electron-density-based transformer method of generating and optimizing host–guest binders is proposed, applied to two different host systems and validated by experiment.
One of the greatest limitations of deep neural networks is the difficulty of interpreting what they learn from the data. Deep distilling addresses this problem by extracting human-comprehensible and executable code from observations.
A method for correcting errors in the spatial-genetic reconstruction of DNA microscopy is proposed, leading to more accurate results and potential to resolve new biology.
A technique that leverages duplicate records in crowdsourcing data could help to mitigate the effects of biases in research and services that are dependent on government records.
EmerGNN is a flow-based graph neural network (GNN) approach that advances on conventional methodologies for predicting drug–drug interactions in emerging drugs by effectively leveraging biomedical networks.
It is difficult to identify stable surface reconstructions of complex materials. Now a Monte Carlo sampling strategy is coupled with a machine learning interatomic potential that is iteratively improved via active learning during the search.
Enzymatic pathways control a host of cellular processes, but the complexity of such pathways has made them difficult to predict. Elektrum combines neural architecture search, kinetic models and transfer learning to effectively discover CRISPR–Cas9 cleavage kinetics.
The visualization and analysis of biological events using fluorescence microscopy is limited by the noise inherent in the images obtained. Now, a self-supervised spatial redundancy denoising transformer is proposed to address this challenge.
A recent study presents an approach for characterizing and quantifying the pore space in assemblies of particles, enabling research into pore-scale flow physics and insight into the interplay between the solid and void phases in granular materials.
The accurate prediction of molecular spectra is essential for substance discovery and structure identification, but conventional quantum chemistry methods are computationally expensive. Now, DetaNet achieves the accuracy of quantum chemistry while improving the efficiency of prediction of organic molecular spectra.
Programmability is crucial in noisy intermediate-scale quantum computing, facilitating various functionalities for practical applications. An arbitrary programmable time-bin-encoded quantum boson sampling device has been developed, specifically tailored for potential drug discovery.
A guided diffusion model pushes the boundaries of de novo molecular design, extensively exploring the chemical space and generating chemical compounds that satisfy custom target criteria.
A recent study proposes a unified framework that can compare different measures for quantifying the statistics of pairwise interactions in data from complex dynamical systems.
Deep learning approaches have potential to substantially reduce the astronomical costs and long timescales involved in drug discovery. KarmaDock proposes a deep learning workflow for ligand docking that shows improved performance against both benchmark cases and in a real-world virtual screening experiment.
A reinforcement-learning-based framework is proposed for assisting urban planners in the complex task of optimizing the spatial design of urban communities.
