The research comes from the University of North Carolina at Chapel Hill, and it demonstrates how an artificial-intelligence design can teach itself how to design new drug molecules from scratch. Such a system could accelerate the design of new drug candidates for use across pharmaceuticals and healthcare.
The new device is named “Reinforcement Learning for Structural Evolution” (abbreviated to ReLeaSE). The artificial intelligence is in the form of an algorithm which has been configured to work with a computer program, based on two neural networks. The networks are described by the researchers as being akin to a teacher and a student. In this sense, the teacher network understands the syntax and linguistic rules required to decipher the chemical structures for about 1.7 million known biologically active molecules. The student, working alongside the teacher network, begins to learn, across time, and becomes better at proposing new molecules that could form the basis of new medicines.
According to lead researcher Alexander 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.”
The new approach is discussed in the journal Science Advances. The research paper is titled “Deep reinforcement learning for de novo drug design.”
In related news, computer scientists from Stanford University have worked out out how to predict side effects of combination medicines using artificial intelligence. The new system is called Decagon, as profiled in the Digital Journal article “Artificial intelligence to predict drug side effects.”
