  The size and quality of chemical libraries to the drug discovery pipeline are
crucial for developing new drugs or repurposing existing drugs. Existing
techniques such as combinatorial organic synthesis and High-Throughput
Screening usually make the process extraordinarily tough and complicated since
the search space of synthetically feasible drugs is exorbitantly huge. While
reinforcement learning has been mostly exploited in the literature for
generating novel compounds, the requirement of designing a reward function that
succinctly represents the learning objective could prove daunting in certain
complex domains. Generative Adversarial Network-based methods also mostly
discard the discriminator after training and could be hard to train. In this
study, we propose a framework for training a compound generator and learning a
transferable reward function based on the entropy maximization inverse
reinforcement learning paradigm. We show from our experiments that the inverse
reinforcement learning route offers a rational alternative for generating
chemical compounds in domains where reward function engineering may be less
appealing or impossible while data exhibiting the desired objective is readily
available.
