Scientists have developed an artificial intelligence (AI) system that can learn to design drug molecules from scratch, potentially accelerating the development of new medicines.
The system, called Reinforcement Learning for Structural Evolution (ReLeaSE), 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.
ReLeaSE is a powerful innovation to virtual screening, the computational method widely used by the pharmaceutical industry to identify viable drug candidates.
Virtual screening allows scientists to evaluate existing large chemical libraries, but the method only works for known chemicals. ReLeASE has the unique ability to create and evaluate new molecules.
The team has used ReLeaSE to generate molecules with properties that they specified, such as desired bioactivity and safety profiles.
They also used the ReLeaSE method to design molecules with customized physical properties, such as melting point and solubility in water and to design new compounds with inhibitory activity against an enzyme that is associated with leukemia.