Curiosity in exploring chemical spaces: intrinsic rewards for molecular reinforcement learning

Computer aided design of molecules has the potential to disrupt the field of drug and material discovery. Machine learning and deep learning in particular, made big strides in recent years and promises to greatly benefit computer aided methods. Reinforcement learning is a particularly promising approach since it enables de novo molecule design, that is molecular design, without providing any prior knowledge. However, the search space is vast, and therefore any reinforcement learning agent needs to perform efficient exploration. In this study, we examine three versions of intrinsic motivation to aid efficient exploration. The algorithms are adapted from intrinsic motivation in the literature that were developed in other settings, predominantly video games. We show that the curious agents finds better performing molecules on two of three benchmarks. 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 molecular designs no human has thought about so far.