  Computer-aided design of molecules has the potential to disrupt the field of
drug and material discovery. Machine learning, and deep learning, in
particular, have been topics where the field has been developing at a rapid
pace. Reinforcement learning is a particularly promising approach since it
allows for molecular design without prior knowledge. However, the search space
is vast and efficient exploration is desirable when using reinforcement
learning agents. In this study, we propose an algorithm to aid efficient
exploration. The algorithm is inspired by a concept known in the literature as
curiosity. We show on three benchmarks that a curious agent finds better
performing molecules. This indicates an exciting new research direction for
reinforcement learning agents that can explore the chemical space out of their
own motivation. This has the potential to eventually lead to unexpected new
molecules that no human has thought about so far.
