Protein Language Models Advance Antibody Design and Therapeutic Development
Recent advancements in protein language models, particularly those trained on antibody sequences, are revolutionizing the design and refinement of therapeutic antibodies. Researchers are leveraging artificial intelligence to predict antibody structures, generate new antibody candidates, and optimize existing ones with unprecedented efficiency. For instance, DeepAb and IgFold models can predict antibody structures rapidly and accurately, even for complex variable loops. Specialized language models like AbLang, trained specifically on antibody sequences, have proven more effective at restoring missing residues in antibody sequences than general protein language models. Furthermore, generative models like IgLM can create synthetic antibody libraries and redesign specific regions of antibodies, while general protein language models can guide the evolution of human antibodies, improving binding affinities significantly with minimal experimental screening.
