Articles in 2023

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.

The electrocatalytic nitrogen reduction reaction is a promising alternative to the Haber–Bosch process. However, the reproducibility and reliability of this process suffer from the persistence of false positives. Computational tools have the potential to alleviate this issue but several challenges must be addressed.

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.

A graph-based contrastive learning framework, LACL, is proposed for geometric domain-agnostic prediction of molecular properties to alleviate the need for molecular geometry relaxation, enabling large-scale inference scenarios.

The Year of Open Science has highlighted the importance of sharing the code associated with peer-reviewed manuscripts. We at Nature Computational Science provide support — via policies and implementations within our submission system — to facilitate this task.

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.

Autoencoders are versatile tools for molecular informatics with the opportunity for advancing molecule and drug design. In this Review, the authors highlight the active areas of development in the field and explore the challenges that need to be addressed moving forward.

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.

Using deep learning methods to study gene regulation has become popular, but designing accessible and customizable software for this purpose remains a challenge. This work introduces a computational toolkit called EUGENe that facilitates the development of end-to-end deep learning workflows in regulatory genomics.

Designing accessible, interoperable and reusable software for applying deep learning to the study of gene regulation has been a challenge in genomics research. EUGENe is a toolkit that addresses this gap and streamlines end-to-end analyses.

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.

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.

A deep-learning model, DetaNet, is proposed to efficiently and precisely predict molecular scalars, vectorial and tensorial properties, as well as the infrared, Raman, ultraviolet–visible and nuclear magnetic resonance spectra.

The capability of predicting stable materials is important to further accelerate the discovery of novel materials. In this Review, the authors discuss recent developments in machine learning techniques for assessing the stability of materials and highlight the opportunities in further advancing the field.