Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and
structure data have radically transformed computational protein design. New methods …

Enzymes can be engineered at the level of their amino acid sequences to optimize key
properties such as expression, stability, substrate range, and catalytic efficiency─ or even to …

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

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

Accelerating catalyst discovery and development is of paramount importance in addressing
the global energy, sustainability and healthcare demands. The past decade has witnessed …

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

There has been considerable recent progress in protein structure prediction using deep
neural networks to predict inter-residue distances from amino acid sequences,–. Here we …

In the field of artificial intelligence, a combination of scale in data and model capacity
enabled by unsupervised learning has led to major advances in representation learning and …

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

Unsupervised contact prediction is central to uncovering physical, structural, and functional
constraints for protein structure determination and design. For decades, the predominant …