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 …

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

A popular approach to protein design is to combine a generative model with a discriminative
model for conditional sampling. The generative model samples plausible sequences while …

We introduce AbDiffuser, an equivariant and physics-informed diffusion model for the joint
generation of antibody 3D structures and sequences. AbDiffuser is built on top of a new …

We study the problem of generating diverse candidates in the context of Multi-Objective
Optimization. In many applications of machine learning such as drug discovery and material …

Antibodies are multimeric proteins capable of highly specific molecular recognition. The
complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often …

We introduce GAUCHE, an open-source library for GAUssian processes in CHEmistry.
Gaussian processes have long been a cornerstone of probabilistic machine learning …

We propose fine-tuning large language models for generation of stable materials. While
unorthodox, fine-tuning large language models on text-encoded atomistic data is simple to …

Machine learning–based design has gained traction in the sciences, most notably in the
design of small molecules, materials, and proteins, with societal applications ranging from …

In the current landscape of deep learning research, there is a predominant emphasis on
achieving high predictive accuracy in supervised tasks involving large image and language …