Unsupervised protein language models trained across millions of diverse sequences learn
structure and function of proteins. Protein language models studied to date have been …

Optimization is a ubiquitous modeling tool and is often deployed in settings which
repeatedly solve similar instances of the same problem. Amortized optimization methods …

Protein is linked to almost every life process. Therefore, analyzing the biological structure
and property of protein sequences is critical to the exploration of life, as well as disease …

Geometric deep learning has recently achieved great success in non-Euclidean domains,
and learning on 3D structures of large biomolecules is emerging as a distinct research area …

Data-driven predictive methods that can efficiently and accurately transform protein
sequences into biologically active structures are highly valuable for scientific research and …

Proteins are macromolecules composed of 20 types of amino acids in a specific order.
Understanding how proteins fold is vital because its 3-dimensional structure determines the …

Inverse design of short single-stranded RNA and DNA sequences (aptamers) is the task of
finding sequences that satisfy a set of desired criteria. Relevant criteria may be, for example …

Recently a number of works have demonstrated successful applications of a fully data-
driven approach to protein design, based on learning generative models of the distribution …

Proteins are essential components of cellular processes, playing pivotal roles in various
biological functions. Their interactions with other proteins are fundamental for maintaining …

Polypeptides with multiple enzyme domains, such as type I polyketide synthases, produce
chemically complex compounds that are difficult to produce via conventional chemical …