The development of bispecific antibodies that bind at least two different targets relies on
bringing together multiple binding domains with different binding properties and biophysical …

Antibodies are engineerable quantities in medicine. Learning antibody molecular
recognition would enable the in silico design of high affinity binders against nearly any …

Machine learning (ML) has demonstrated significant promise in accelerating drug design.
Active ML-guided optimization of therapeutic molecules typically relies on a surrogate model …

Protein language models trained on evolutionary data have emerged as powerful tools for
predictive problems involving protein sequence, structure, and function. However, these …

Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding
domain within the field of biologics. In silico tools that can streamline the process of antibody …

The optimal residue identity at each position in a protein is determined by its structural,
evolutionary, and functional context. We seek to learn the representation space of the …

T-cell receptor (TCR) structures are currently under-utilised in early-stage drug discovery
and repertoire-scale informatics. Here, we leverage a large dataset of solved TCR structures …

The successful application of machine learning in therapeutic antibody design relies heavily
on the ability of models to accurately represent the sequence-structure-function landscape …

There is currently considerable interest in the field of de novo antibody design, and deep
learning techniques are now regularly applied to optimise antibody properties such as …

Supervised machine learning models rely on training datasets with positive (target class)
and negative examples. Therefore, the composition of the training dataset has a direct …