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

Modeling the effect of sequence variation on function is a fundamental problem for
understanding and designing proteins. Since evolution encodes information about function …

Computational biology and bioinformatics provide vast data gold-mines from protein
sequences, ideal for Language Models (LMs) taken from Natural Language Processing …

Transformers do not scale very well to long sequence lengths largely because of quadratic
self-attention complexity. In the recent months, a wide spectrum of efficient, fast Transformers …

Inspired by progress in unsupervised representation learning for natural language, we
examine whether similar models can learn useful representations for images. We train a …

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

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 …

Transformer architectures have proven to learn useful representations for protein
classification and generation tasks. However, these representations present challenges in …

Generative modeling for protein engineering is key to solving fundamental problems in
synthetic biology, medicine, and material science. We pose protein engineering as an …

Understanding the relationship between amino acid sequence and protein function is a long-
standing challenge with far-reaching scientific and translational implications. State-of-the-art …