  Naloxone, an opioid antagonist, has been widely used to save lives from
opioid overdose, a leading cause for death in the opioid epidemic. However,
naloxone has short brain retention ability, which limits its therapeutic
efficacy. Developing better opioid antagonists is critical in combating the
opioid epidemic.Instead of exhaustively searching in a huge chemical space for
better opioid antagonists, we adopt reinforcement learning which allows
efficient gradient-based search towards molecules with desired physicochemical
and/or biological properties. Specifically, we implement a deep reinforcement
learning framework to discover potential lead compounds as better opioid
antagonists with enhanced brain retention ability. A customized multi-objective
reward function is designed to bias the generation towards molecules with both
sufficient opioid antagonistic effect and enhanced brain retention ability.
Thorough evaluation demonstrates that with this framework, we are able to
identify valid, novel and feasible molecules with multiple desired properties,
which has high potential in drug discovery.
