Scientists from the University of North Carolina at Chapel Hill Eshelman School of Pharmacy have designed an artificial intelligence based system to design new drug molecules from the scratch level.

This algorithm based computer program system is called Reinforcement Learning for Structural Evolution (ReLeaSE), which is composed of two neural networks as a teacher and a student. The teacher knows the syntax rules behind the chemical structures of about 1.7 million bioactive molecules. The student becomes an expert in proposing the molecules which can be made useful as novel medicines.

This invention was published in the journal Science Advances last week under Alexander Tropsha, Olexandr Isayev and Mariya Popova, who were the creators of ReLeaSE.

Alexander Tropsha said, "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; 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."

Alexander Tropsha further added, "The ability of the algorithm to design new, and therefore immediately patentable, chemical entities with specific biological activities and optimal safety profiles should be highly attractive to an industry that is constantly searching for new approaches to shorten the time it takes to bring a new drug candidate to clinical trials."