Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

Deep neural-network-based language models (LMs) are increasingly applied to large-scale
protein sequence data to predict protein function. However, being largely black-box models …

Learning effective protein representations is critical in a variety of tasks in biology such as
predicting protein function or structure. Existing approaches usually pretrain protein …

Pretrained protein sequence language models have been shown to improve the
performance of many prediction tasks and are now routinely integrated into bioinformatics …

Self-supervised pretraining on protein sequences has led to state-of-the art performance on
protein function and fitness prediction. However, sequence-only methods ignore the rich …

In offline model-based optimization, we strive to maximize a black-box objective function by
only leveraging a static dataset of designs and their scores. This problem setting arises in …

Offline model-based optimization aims to find a design that maximizes a property of interest
using only an offline dataset, with applications in robot, protein, and molecule design …

Self-supervised pre-training methods on proteins have recently gained attention, with most
approaches focusing on either protein sequences or structures, neglecting the exploration of …

The structure of protein-protein complexes is critical for understanding binding dynamics,
biological mechanisms, and intervention strategies. Rigid protein docking, a fundamental …

We study offline model-based optimization to maximize a black-box objective function with a
static dataset of designs and scores. These designs encompass a variety of domains …