Artificial intelligence (AI) de novo molecular generation is a highly promising strategy in the drug discovery, with deep reinforcement learning (RL) models emerging as powerful tools. This study introduces a fragment-by-fragment growth RL forward molecular generation and optimization strategy based on a low activity lead compound. This process integrates fragment growth-based reaction templates, while target docking and drug-likeness prediction were simultaneously performed. This comprehensive approach considers molecular similarity, internal diversity, synthesizability, and effectiveness, thereby enhancing the quality and efficiency of molecular generation. Finally, a series of tyrosinase inhibitors were generated and synthesized. Most compounds exhibited more improved activity than lead, with an optimal candidate compound surpassing the effects of kojic acid and demonstrating significant antipigmentation activity in a zebrafish model. Furthermore, metabolic stability studies indicated susceptibility to hepatic metabolism. The proposed AI structural optimization strategies will play a promising role in accelerating the drug discovery and improving traditional efficiency.