Predicting Therapeutic Antibody Properties with Machine Learning
Once trained, machine learning algorithms can predict the properties of new antibody candidates, greatly speeding up the drug discovery process.
Once trained, machine learning algorithms can predict the properties of new antibody candidates, greatly speeding up the drug discovery process.
Generative AI models are a type of artificial intelligence that generate new data that resemble training data by understanding the underlying...
Phages are viruses that infect bacteria. Helper phage plays a crucial role in certain laboratory techniques such as phage display.
Genetic libraries serve as valuable tools for understanding gene functions, studying genetic diseases, and developing new drugs.
Phage display is a powerful technique for the study of protein-protein interactions and the discovery of therapeutic antibodies.
Nanobody characteristics such as high stability, and specificity have led to their fusion with other proteins for therapeutic and diagnostic purposes.
Camelid antibodies exhibit unusual characteristics that provide a myriad of potential applications in research and therapeutics.
Next-generation sequencing (NGS) plays a pivotal role in the discovery and development of therapeutic T cell receptors (TCRs).
The key to the production of monoclonal antibodies (mAbs) lies in a specialized cell type known as a hybridoma.
Bulk B Cell analysis via NGS is a method used to explore the repertoire of B cell receptors (BCRs) in a sample that contains heterogeneous B cell...