A transformer is the foundational architecture behind large language models designed to
handle sequential data by using mechanisms of self‐attention to weigh the importance of …

Highlights•Generative AI accelerates discovery, improves targets, and boosts R&D
productivity.•Smarter assistants and generative biology in Horizon 1 revolutionize R&D …

Antibody generation requires the use of one or more time-consuming methods, namely
animal immunization, and in vitro display technologies. However, the recent availability of …

Therapeutic antibodies are an important class of biopharmaceuticals. With the rapid
development of deep learning methods and the increasing amount of antibody data …

Motivation Macrocyclic peptides hold great promise as therapeutics targeting intracellular
proteins. This stems from their remarkable ability to bind flat protein surfaces with high …

High affinity is crucial for the efficacy and specificity of antibody. Due to involving high-
throughput screens, biological experiments for antibody affinity maturation are time …

Generative adversarial networks (GANs) have successfully generated functional protein
sequences. However, traditional GANs often suffer from inherent randomness, resulting in a …

Monoclonal antibodies (mAbs) represent one of the most prevalent FDA-approved
modalities for treating autoimmune diseases, infectious diseases, and cancers. However …

The overarching goal of protein engineering is the design and optimization of proteins
customized for specific purposes. Generative protein language models (PLMs) allow …