  Rising costs in recent years of developing new drugs and treatments have led
to extensive research in optimization techniques in biomolecular design.
Currently, the most widely used approach in biomolecular design is directed
evolution, which is a greedy hill-climbing algorithm that simulates biological
evolution. In this paper, we propose a new benchmark of applying reinforcement
learning to RNA sequence design, in which the objective function is defined to
be the free energy in the sequence's secondary structure. In addition to
experimenting with the vanilla implementations of each reinforcement learning
algorithm from standard libraries, we analyze variants of each algorithm in
which we modify the algorithm's reward function and tune the model's
hyperparameters. We show results of the ablation analysis that we do for these
algorithms, as well as graphs indicating the algorithm's performance across
batches and its ability to search the possible space of RNA sequences. We find
that our DQN algorithm performs by far the best in this setting, contrasting
with, in which PPO performs the best among all tested algorithms. Our results
should be of interest to those in the biomolecular design community and should
serve as a baseline for future experiments involving machine learning in
molecule design.
