Reviews & Analysis

Quantum algorithms for simulating quantum dynamics have shown promising results to overcome the difficulties from the classical counterparts. This Perspective summarizes the recent developments in the field, and further discusses the limitations and research opportunities towards the goal of quantum advantage.

As artificial intelligence begins to profoundly impact structural biology, one of the next challenges is to predict protein structures from individual sequences alone. A deep learning model addresses this challenge by representing single sequences with protein language models and distilling knowledge from multi-sequence structure predictors.

Antigen–antibody prediction remains a complex computational challenge. Simulations with the new Absolut! package provide novel insights into the models and datasets tackling this problem.

Immunotherapy has begun to make a transformative impact on oncology practice, and mathematical modeling has been used to provide quantitative insights into this field. This Review discusses how models are being designed for direct clinical integration to improve the success rate of immunotherapy.

A method for making large-scale nanophotonic simulations more computationally efficient is proposed, enabling a wide range of studies to be less time- and memory-intensive.

To understand whether or not the design of machine learning systems can integrate domain expertise, a recent work proposes methodologies to synthesize domain science with machine learning, which shows added benefits.

We used computational models built using neural networks to predict what brain areas process the new meaning that emerges when words are combined. The brain activity evoked by this composed meaning was detected only with some brain recording modalities, a finding that might have consequences for brain–computer interfaces.

The vulnerability of quantum machine learning is demonstrated on a superconducting quantum computer, together with a defense strategy based on noisy intermediate-scale quantum (NISQ) adversarial learning.

Recent work uses a language model to gain insight into how the human brain understands the combined meaning of words in a sentence, and uncovers parts of the brain that contribute to this understanding.

A universal interatomic potential for the periodic table has been developed by combining graph neural networks with three-body interactions. This M3GNet potential can perform structural relaxations, dynamic simulations and property predictions for materials across a diverse chemical space.

The surface energy cannot be assigned to each direction in low-symmetry crystals, making it impossible to predict their shapes by any known methods. Now, combining incomputable energies in an algebraic system, complemented by closure equations, it is possible to predict the equilibrium shape of any crystal.

We developed a computational method to reveal the drug-induced single-cell transcriptomic landscape. This algorithm enabled us to impute unknown drug-induced single-cell gene expression profiles using tensor imputation, predict cell type-specific drug efficacy, detect cell-type-specific marker genes, and identify the trajectories of regulated biological pathways while considering intercellular heterogeneity.

The modeling of non-linear morphological changes in biological systems is a challenging task. Motivated by the observation of exotic pattern formation processes on fruit surfaces, a chiral wrinkling topology is disclosed as a mechanical structural instability, which is then exploited for the design of enhanced adaptive graspers.

The simulation of relativistic flows that can transit from a fluid-like to a gas-like substance poses challenges for computational methods. A lattice kinetic scheme is proposed to simulate such flows, which allows a computational probe of both strongly and weakly interacting regimes.

In social interactions, individuals are often tempted to ‘free ride’ (benefit without paying) on other group members’ contributions. A new mathematical framework suggests a strategy based on cumulative reciprocity that can help to sustain mutual cooperation.

Designing efficient bike path networks requires balancing multiple opposing constraints such as cost and safety. An adaptive demand-driven inverse percolation approach is proposed to generate efficient network structures by explicitly taking into account the demands of cyclists and their route choice behavior based on safety preferences.

In a recent study a phenomenological model was used to study the effects of activity-dependent myelination (ADM) on network activity and information transmission in the brain. The model explores how the conduction velocity of an axon — and thus the overall transmission delay — varies as a function of neural activity.

A recent study proposes a metric to quantify how much information different types of epidemiological surveillance data, such as case counts and death counts, convey about the real-time transmission of an epidemic.

Integrating social mixing data into epidemic models can help policy makers better understand epidemic spread. However, empirical mixing data might not be immediately available in most populations. In a recent work, a network model methodology is proposed to construct micro-level social mixing structure when empirical data are not available.

Quantum machine learning has become an essential tool to process and analyze the increased amount of quantum data. Despite recent progress, there are still many challenges to be addressed and myriad future avenues of research.