Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …

The rapid advancements in machine learning, graphics processing technologies and the
availability of medical imaging data have led to a rapid increase in the use of deep learning …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Abstract Protein-Protein Interactions (PPIs) are fundamental means of functions and
signalings in biological systems. The massive growth in demand and cost associated with …

The rapid increase in the number of proteins in sequence databases and the diversity of
their functions challenge computational approaches for automated function prediction. Here …

In the space of only a few years, deep generative modeling has revolutionized how we think
of artificial creativity, yielding autonomous systems which produce original images, music …

Enzyme engineering plays a central role in developing efficient biocatalysts for
biotechnology, biomedicine, and life sciences. Apart from classical rational design and …

Abstract Machine learning provides researchers a unique opportunity to make metabolic
engineering more predictable. In this review, we offer an introduction to this discipline in …

Drug discovery based on artificial intelligence has been in the spotlight recently as it
significantly reduces the time and cost required for developing novel drugs. With the …

Nanozymes are nanomaterials that exhibit enzyme‐like biomimicry. In combination with
intrinsic characteristics of nanomaterials, nanozymes have broad applicability in materials …