An artificial-intelligence approach created at the University of North Carolina at Chapel Hill Eshelman School of Pharmacy can teach itself to design new drug molecules from scratch and has the potential to dramatically accelerate the design of new drug candidates. The system is called Reinforcement Learning for Structural Evolution, known as ReLeaSE, and is an algorithm and computer program that comprises two neural networks which can be thought of as a teacher and a student.
The teacher knows the syntax and linguistic rules behind the vocabulary of chemical structures for about 1.7 million known biologically active molecules. By working with the teacher, the student learns over time and becomes better at proposing molecules that are likely to be useful as new medicines.
Alexander Tropsha, Olexandr Isayev and Mariya Popova, all of the UNC Eshelman School of Pharmacy, are the creators of ReLeaSE. The University has applied for a patent for the technology, and the team published a proof-of-concept study in the journal Science Advances last week.
“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 molecules,” said Tropsha. “If the new molecule is realistic and has the desired effect, the teacher approves.
If not, the teacher disapproves, forcing the student to avoid bad molecules and create good ones.” ReLeaSE is a powerful innovation to virtual screening, the computational method widely used by the pharmaceutical industry to identify viable drug candidates. Read more from phys.org…
thumbnail courtesy of phys.org