AI Tools Like RENAISSANCE Revolutionize Antibody Discovery and Metabolism Modeling

Sep 2, 2024, 07:48 AM

Recent advancements in artificial intelligence (AI) are significantly enhancing the field of drug discovery and cellular metabolism modeling. Researchers at the University of Michigan, led by Peter Tessier, PhD, are utilizing AI to optimize antibody discovery by balancing multiple attributes such as affinity and manufacturability. This approach aims to expedite the development of antibody therapeutics for clinical use. Additionally, a new AI tool is being developed to predict protein functions within various cellular environments, potentially improving the efficiency of drug discovery. At EPFL, researchers have created an AI tool to generate precise models of cellular metabolism, simplifying the understanding of cell functions. The generative machine learning framework, RENAISSANCE, has been introduced to efficiently parameterize large-scale kinetic models of metabolism, offering accurate characterization of intracellular metabolic states. A study compares six state-of-the-art methods, including AlphaFold-Multimer, for predicting antibody–antigen complex structures from sequences, highlighting systematic biases.