CHAPEL HILL–A new artificial intelligence system can teach itself to design new drug molecules from scratch. Developed at the Eshelman School of Pharmacy at the University of North Carolina at Chapel Hill, it has the potential to dramatically accelerate the design of new drug candidates, the University reports.
Called Reinforcement Learning for Structural Evolution or ReLeaSE, the system is a computer program comprised of two neural networks that can be thought of as a teacher and student. The teacher knows the syntax and linguistic rules behind the vocabulary of chemical structures for about 1.7 million known bioactive molecules, the University says. “By working with the teacher, the student learns over time and becomes better at proposing molecules likely to be useful as new medicines.”
The University has applied for a patent on the technology developed by Alexander Tropsha, Olexandr Isayev, and Mariya Popova of the UNC Eshelman School of Pharmacy.
“If we compare this process to learning a language, then after the student learns the molecular alphabet and the rules of the language, they can create new words,” or in this case, molecules, Tropsha said in a statement.
ReLeasSE is an innovation in virtual screening, the computational method the pharmaceutical industry uses to identify drug candidates. Virtual screening only allows researchers to examine know chemical libraries. ReLeaSE can create and evaluate new molecules.
A scientist using virtual screening is like a customer ordering from a menu in a restaurant, according to Isayeev. “What can be ordered is usually limited by the menu. We want to give scientists a grocery store and a personal chef who can create any dish they want.”
