Reviews & Analysis

Quantum computing has the potential to assist with myriad tasks in science. In this Perspective, the applicability and promising directions of quantum computing in computational biology, genetics and bioinformatics is evaluated and discussed.

There have been substantial developments in weather and climate prediction over the past few decades, attributable to advances in computational science. The rise of new technologies poses challenges to these developments, but also brings opportunities for new progress in the field.

The mechanisms facilitating evolutionary adaptation to future challenges are difficult to establish experimentally. Recent computational simulations of 200 cell populations indicate how evolution can hide useless genetic switches with capacity for later use.

The nature of biological networks still brings challenges related to computational complexity, interpretable results and statistical significance. Recent work proposes a new method that paves the way for addressing these issues when analyzing cancer genomic data.

Characterizing the aggregation of the peptide amyloid β is essential to better understand Alzheimer’s disease and to find potential targets for drug development. Deep neural networks make it possible to describe the kinetics of this peptide, opening the way for achieving this goal.

Computational approaches for drug repurposing can accelerate the identification of treatments during a pandemic. In this Review, the authors discuss this topic in the context of COVID-19 and propose a strategy to make computational drug repurposing more effective in future pandemics.

While estimating causality from observational data is challenging, quasi-experiments provide causal inference methods with plausible assumptions that can be practical to a range of real-world problems.