Tokyo (SCCIJ)—The use of generative artificial intelligence in protein design could revolutionize new drug development. The leading Swiss university EPFL is forming a consortium to explore this avenue further.
Designing new proteins
Recent advancements in protein design are opening new avenues for drug research. At the forefront of this revolution is generative artificial intelligence (GenAI), capable of designing entirely new kinds of proteins. New imaging methods such as X-ray crystallography and cryogenic electron microscopy also play a key role, as they let scientists observe the composition of real-world proteins with unprecedented precision. Combining these new technologies could pave the way to novel processes, allowing researchers to develop – among other things – innovative biologic medications, often called biologics.
Various complementary research projects are underway at schools and universities across Switzerland. At the Swiss Federal Institute of Technology in Lausanne (EPFL), Bruno Correia and Beat Fierz are building a consortium to usher in a new era of drug research—one powered by machine learning. Bringing this under one roof would not only cement the country’s position as a center of excellence in this field but also encourage the rapid emergence of effective new proteins for clinical applications.
A close-up view of biomolecules
The idea is to promote the development of AI-enabled molecule design technology, explore new types of drug-cell interactions, create new databases to improve the performance of design software further and prepare early-career scientists to seize new research and technology transfer opportunities. The ambitious endeavor will captivate scientists for generations to come.
EPFL is already highly active in protein design. For more than five years now, the School’s Laboratory of Protein Design & Immunoengineering, which Correia heads, has been using machine learning to predict the interactive potential between proteins and their receptors. “The use of deep learning in biological engineering is opening up exciting new opportunities,” says Correia.
This groundbreaking work also marks the starting point for a nascent research revolution. After training GenAI programs such as ChatGPT on protein and molecular-interaction data generated by researchers and models such as AlphaFold, the programs can design and model entirely new types of molecules in countless forms and simulate their interactions with cells. They can perform billions of such calculations per second until they find molecules with theoretical relevance for drug development. “This new approach will be nothing short of a paradigm shift for the entire field of biotechnology,” states Correia.
Text: Emmanuel Barraud/EPFL (Editing by SCCIJ)
