Antibodies are versatile molecular binders with an established and growing role as
therapeutics. Computational approaches to develo** and designing these molecules are …

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 …

Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous
optimization. However, its adoption for drug design has been hindered by the discrete, high …

Therapeutic antibodies are an important and rapidly growing drug modality. However, the
design and discovery of early-stage antibody therapeutics remain a time and cost-intensive …

Despite tremendous progress of generative models in the natural sciences, their
controllability remains challenging. One fundamentally missing aspect of molecular or …

Oligoclonal mixtures of broadly-neutralizing antibodies can neutralize complex compositions
of similar and dissimilar antigens, making them versatile tools for the treatment of eg …

High-affinity antibodies are often identified through directed evolution, which may require
many iterations of mutagenesis and selection to find an optimal candidate. Deep learning …

Meta-Bayesian optimisation (meta-BO) aims to improve the sample efficiency of Bayesian
optimisation by leveraging data from related tasks. While previous methods successfully …

Traditional antibody optimization approaches involve screening a small subset of the
available sequence space, often resulting in drug candidates with suboptimal binding …

Therapeutic antibody engineering seeks to identify antibody sequences with specific binding
to a target and optimized drug-like properties. When guided by deep learning, antibody …