PacMan‐RL

Drug development, an intricate labyrinth of experimentation, often resembles a high‐stakes gamble. This study presents PacMan‐RL, a reinforcement learning method inspired by the game Pac‐Man, for drug development. PacMan‐RL navigates the chemical universe to find target proteins while avoiding undesired traits like toxicity. This dynamic approach promises more effective and safer medication candidates. This document provides the conceptual framework of PacMan‐RL, including the definition of the agent, the action space, the state representation, and the reward mechanisms. This potent technique can transform medication design, resulting in expedited and more effective creation of efficacious drugs without adverse effects.