Neural-network architectures have been increasingly used to represent quantum many-body
wave functions. These networks require a large number of variational parameters and are …

Protein language models (pLMs) have emerged as potent tools for predicting and designing
protein structure and function, and the degree to which these models fundamentally …

Recent advancements in immune sequencing and experimental techniques are generating
extensive T cell receptor (TCR) repertoire data, enabling the development of models to …

From AlphaGO over StableDiffusion to ChatGPT, the recent decade of exponential advances
in artificial intelligence (AI) has been altering life. In parallel, advances in computational …

Abstract Motivation Multiple sequence alignments (MSAs) of homologous sequences
contain information on structural and functional constraints and their evolutionary histories …

Computational models starting from large ensembles of evolutionarily related protein
sequences capture a representation of protein families and learn constraints associated to …

The remarkable capability of over-parameterised neural networks to generalise effectively
has been explained by invoking a``simplicity bias'': neural networks prevent overfitting by …

The dot product attention mechanism, originally designed for natural language processing
tasks, is a cornerstone of modern Transformers. It adeptly captures semantic relationships …

Self-supervised neural language models with attention have recently been applied to
biological sequence data, advancing structure, function and mutational effect prediction …

Drug resistance in HIV type 1 (HIV-1) is a pervasive problem that affects the lives of millions
of people worldwide. Although records of drug-resistant mutations (DRMs) have been …