Application of Reinforcement Learning Techniques in De Novo Drug Design: A Systematic Literature Review

De novo drug design is the process of generating novel lead compounds that possess desirable pharmacological activities and optimal physicochemical properties for therapeutic development. In recent years, it has evolved into a key computational strategy for discovering and optimizing new therapeutic compounds. Reinforcement learning (RL), a branch of artificial intelligence, has emerged as a powerful tool to address the complex, sequential decision‐making processes involved in molecular generation. This study aims to review recent applications of RL in de novo drug design, highlight commonly used algorithms, identify major challenges, and discuss future research directions.