Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs)
are tremendous, the design and discovery of new candidates remain a time and cost …

B cell and T cell receptor repertoires compose the adaptive immune receptor repertoire
(AIRR) of an individual. The AIRR is a unique collection of antigen-specific receptors that …

We introduce a modern Hopfield network with continuous states and a corresponding
update rule. The new Hopfield network can store exponentially (with the dimension of the …

Generative machine learning (ML) has been postulated to become a major driver in the
computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to …

Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-
epitope interface. The predictability of antibody-antigen binding is a prerequisite for de novo …

Monitoring the T cell receptor (TCR) repertoire in health and disease can provide key
insights into adaptive immune responses, but the accuracy of current TCR sequencing …

Advances in single-cell technologies have enabled the investigation of T-cell phenotypes
and repertoires at unprecedented resolution and scale. Bioinformatic methods for the …

Many different solutions to predicting the cognate epitope target of a T-cell receptor (TCR)
have been proposed. However several questions on the advantages and disadvantages of …

Beyond potency, a good developability profile is a key attribute of a biological drug.
Selecting and screening for such attributes early in the drug development process can save …

A central mechanism in machine learning is to identify, store, and recognize patterns. How to
learn, access, and retrieve such patterns is crucial in Hopfield networks and the more recent …