Emerging computational tools promise to revolutionize protein engineering for biocatalytic
applications and accelerate the development timelines previously needed to optimize an …

Diffusion models have emerged as powerful tools for molecular generation, particularly in
the context of 3D molecular structures. Inspired by nonequilibrium statistical physics, these …

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics
simulations using machine learning potentials known as Deep Potential (DP) models. This …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Despite great advances in protein structure analysis, label-free and ultrasensitive methods
to obtain the natural and dynamic three-dimensional (3D) structures are still urgently …

This work brings the leading accuracy, sample efficiency, and robustness of deep
equivariant neural networks to the extreme computational scale. This is achieved through a …

Machine learning plays an important and growing role in molecular simulation. The newest
version of the OpenMM molecular dynamics toolkit introduces new features to support the …

Classical empirical force fields have dominated biomolecular simulation for over 50 years.
Although widely used in drug discovery, crystal structure prediction, and biomolecular …

We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM)
dataset that contains the structural and electronic information of 59,783 low-and high-energy …

Fast and accurate calculation of intermolecular interaction energies is desirable for
understanding many chemical and biological processes, including the binding of small …