development, combining different levels of theory to increase accuracy, aiming to connect chemical and molecular changes to macroscopic observables. In this review, we outline biomolecular simulation methods and highlight examples of its application to investigate questions in biology. enzyme, membrane, molecular dynamics, multiscale, protein, QM/MM 1 | INTRODUCTION Biomolecular simulations are now making significant contributions to a wide variety of problems in drug discovery, drug development, biocatalysis, biotechnology, nanotechnology, chemical biology, and medicine. Biomolecular simulation is a rapidly growing field in scale and impact, increasingly demonstrating its worth in understanding mechanisms and analyzing activities, and contributing to the design of drugs and biocatalysts. Physics-based simulations complement experiments in building a molecular-level understanding of biology: They can test hypotheses and interpret and analyze experimental data in terms of interactions at the atomic level. Different types of simulation techniques have been developed, which are applicable to a range of different problems in biomolecular science. Simulations have already shown their worth in helping to analyze how enzymes catalyze biochemical reactions, and how proteins adopt their functional structures, for example, within cell membranes. They contribute to the design of drugs and catalysts, and in understanding the molecular basis of disease. Simulations have played a key role in developing the conceptual framework now at the heart of biomolecular science: that the dynamics of biological molecules is central to their function. Developing methods from chemical physics and computational science will open exciting new opportunities in biomolecular science, including in drug design and development, biotechnology, and biocatalysis. With high-performance computing resources, large-scale atomistic simulations of biological machines such as the ribosome, proton pumps and motors, membrane receptor complexes, and even whole viruses have become possible. Useful simulations of smaller systems can be carried out with desktop resources, thanks to developments allowing, for example, graphics processing units (GPUs) to be used. A particular challenge across the field is the integration of simulations crossing the span of length-and timescales as different types of simulation method are required for different types of problems. 1 Biomolecular systems pose fundamental scientific challenges (e.g., protein folding, enzyme catalysis, gene regulation, disease mechanisms, and antimicrobial resistance) and are at the heart of many advanced technological developments (drug discovery, biotechnology, biocatalysis, biomaterials, and genetic engineering). Biomolecular systems are inherently complex and pose significant challenges in modeling. An essential underlying paradigm is the need to consider biomolecular ensembles and their dynamics, rather than simply static biomolecular structures to understand and predict their behavior and propertie...