Bayesian optimisation (BO) has been increasingly utilised to guide material discovery. While
BO is advantageous due to its sample efficiency, flexibility and versatility, it is constrained by …

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals
despite significant challenges and risks to their discovery and development. This review …

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 …

Optimizing discrete black-box functions is key in several domains, eg protein engineering
and drug design. Due to the lack of gradient information and the need for sample efficiency …

Antibody drugs have become a key part of biotherapeutics. Patients suffering from various
diseases have benefited from antibody therapies. However, its development process is …

Drug discovery deals with the search for initial hits and their optimization toward a targeted
clinical profile. Throughout the discovery pipeline, the candidate profile will evolve, but the …