Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

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

Predicting the effects of coding variants is a major challenge. While recent deep-learning
models have improved variant effect prediction accuracy, they cannot analyze all coding …

Attention-based models trained on protein sequences have demonstrated incredible
success at classification and generation tasks relevant for artificial-intelligence-driven …

The genome is a sequence that encodes the DNA, RNA, and proteins that orchestrate an
organism's function. We present Evo, a long-context genomic foundation model with a …

Adapting language models to protein sequences spawned the development of powerful
protein language models (pLMs). Concurrently, AlphaFold2 broke through in protein …

Language models have contributed to breakthroughs in interdisciplinary research, such as
protein design and molecular dynamics understanding. In this study, we reveal that beyond …

Due to their high target specificity and binding affinity, therapeutic antibodies are currently
the largest class of biotherapeutics. The traditional largely empirical antibody development …

Designing protein sequences towards desired properties is a fundamental goal of protein
engineering, with applications in drug discovery and enzymatic engineering. Machine …

Large language models trained on sequence information alone can learn high-level
principles of protein design. However, beyond sequence, the three-dimensional structures of …