Concerns over climate change have necessitated a rethinking of our transportation
infrastructure. One possible alternative to carbon-polluting fossil fuels is biofuels produced …

Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …

Enzyme function annotation is a fundamental challenge, and numerous computational tools
have been developed. However, most of these tools cannot accurately predict functional …

Enzymatic deconstruction of poly (ethylene terephthalate)(PET) is under intense
investigation, given the ability of hydrolase enzymes to depolymerize PET to its constituent …

Enzyme turnover numbers (k cat) are key to understanding cellular metabolism, proteome
allocation and physiological diversity, but experimentally measured k cat data are sparse …

High-throughput next-generation sequencing now makes it possible to generate a vast
amount of multi-omics data for various applications. These data have revolutionized …

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

For most proteins annotated as enzymes, it is unknown which primary and/or secondary
reactions they catalyze. Experimental characterizations of potential substrates are time …

Retrobiosynthesis provides an effective and sustainable approach to producing functional
molecules. The past few decades have witnessed a rapid expansion of biosynthetic …

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 …