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MISATO, a dataset for structure-based drug discovery combines quantum mechanics property data and molecular dynamics simulations on ~20,000 protein–ligand structures, substantially extends the amount of data available to the community and holds potential for advancing work in drug discovery.
Multicellular modeling is increasingly being used to understand biological systems. SimuCell3D is a tool that allows mechanically realistic simulations, using the deformable cell model, to be developed and run.
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.
