Artificial Intelligence-Assisted Optimization of Antipigmentation Tyrosinase Inhibitors: De Novo Molecular Generation Based on a Low Activity Lead Compound

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.人工智能（AI）从头分子生成（de novo molecular generation）是药物研发领域极具前景的策略，深度强化学习（deep reinforcement learning, RL）正逐渐成为该领域的强力工具。本研究提出了一种基于低活性先导化合物的逐片段生长式深度强化学习正向分子生成与优化策略。该方法整合了基于片段生长的反应模板，同时开展了靶点对接（target docking）与类药性预测（drug-likeness prediction）。该综合策略兼顾分子相似度、内部多样性、可合成性（synthesizability）与药效有效性，从而提升分子生成的质量与效率。最终，本研究生成并合成了一系列酪氨酸酶抑制剂（tyrosinase inhibitors）。多数化合物的活性较先导化合物有所提升，其中最优候选化合物的活性优于曲酸（kojic acid），并在斑马鱼模型（zebrafish model）中展现出显著的抗色素沉着活性（antipigmentation activity）。此外，代谢稳定性研究表明该类化合物易受肝脏代谢（hepatic metabolism）影响。本研究提出的AI分子结构优化策略，有望在加速药物研发进程、提升传统研发效率方面发挥重要作用。