Articles in 2024

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.

The authors develop the tool RESHAPE to share reference panels in a safer way. The genome–phenome links in reference panels can generate re-identification threats and RESHAPE breaks these links by shuffling haplotypes while preserving imputation accuracy.

MISATO is a database for structure-based drug discovery that combines quantum mechanics data with molecular dynamics simulations on ~20,000 protein–ligand structures. The artificial intelligence models included provide an easy entry point for the machine learning and drug discovery communities.

A method based on a vector-quantized variational autoencoder, called CASTLE, can interpretably extract discrete latent embeddings and quantitatively generate the cell-type-specific feature spectrum for single-cell chromatin accessibility sequencing data.

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.

We highlight the vibrant discussions on quantum computing and quantum algorithms that took place at the 2024 American Physical Society March Meeting and invite submissions that notably drive the field of quantum information science forward.

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.

SANGO efficiently removed batch effects between the query and reference single-cell ATAC signals through the underlying genome sequences, to enable cell type assignment according to the reference data. The method achieved superior performance on diverse datasets and could detect unknown tumor cells, providing valuable functional biological signals.

Approaches are needed to accelerate the discovery of transition metal complexes (TMCs), which is challenging owing to their vast chemical space. A large dataset of diverse ligands is now introduced and leveraged in a multiobjective genetic algorithm that enables the efficient optimization of TMCs in chemical spaces containing billions of them.

Cooperation is not merely a dyadic phenomenon, it also includes multi-way social interactions. A mathematical framework is developed to study how the structure of higher-order interactions influences cooperative behavior.

Wildfires have increased in frequency and intensity due to climate change and have had severe impacts on the built environment worldwide. Moving forward, models should take inspiration from epidemic network modeling to predict damage to individual buildings and understand the impact of different mitigations on the community vulnerability in a network setting.

This study introduces SANGO, a method for accurate single-cell annotation leveraging genomic sequences around accessibility peaks within single-cell ATAC sequencing data. SANGO consistently outperforms existing methods across diverse datasets for identification of cell type and detection of unknown tumor cells. SANGO enables the discovery of cell-type-specific functional insights through expression enrichment, cis-regulatory chromatin interactions and motif enrichment analyses.

A fast and versatile three-dimensional cell-based model, called SimuCell3D, is developed for high-resolution simulations of large and complex biological tissues. SimuCell3D natively integrates intra- and extracellular entities, including extracellular matrix, nuclei and polarized cell surfaces.

A method is developed for the directional optimization of multiple properties without prior knowledge on their nature. Using a large ligand dataset, diverse metal complexes are found along the Pareto front of vast chemical spaces.

This issue of Nature Computational Science includes a Focus that highlights recent advancements, challenges, and opportunities in the development and use of digital twins across different domains.

Digital twins hold immense promise in accelerating scientific discovery, but the publicity currently outweighs the evidence base of success. We summarize key research opportunities in the computational sciences to enable digital twin technologies, as identified by a recent National Academies of Sciences, Engineering, and Medicine consensus study report.

Digital twins of Earth have the capability to offer versatile access to detailed information on our changing world, helping societies to adapt to climate change and to manage the effects of local impacts, globally. Nevertheless, human interaction with digital twins requires advances in computational science, particularly where complex geophysical data is turned into information to support decision making.

While there is a clear opportunity for digital twins to bring value in mechanical and aerospace engineering, they must be considered as an asset in their own right so that their full potential can be realized.

Urban digital twins hold immense promise as live computational models of cities, synthesizing diverse knowledge, streaming data, and supporting decisions towards more inclusive planning and policy. The size, heterogeneity, and open-ended character of cities, however, pose many difficult questions, at the frontiers of what is currently possible in computational science. Overcoming these challenges provides pathways for fundamental progress in the field and a proving ground for its economic value and social relevance.