Traditional Chinese medicine (TCM) exhibits remarkable therapeutic efficacy in disease treatment and healthcare through patienti-specific formulas. However, current AI-based TCM formula recommendation models and methods mainly focus on data-based textual associations between symptoms and herbs, and have not fully utilized their features and relations at different scales, especially at the molecular scale. To address these limitations, we propose the Fusion of Multiscale Associations of Symptoms and Herbs (FMASH), an novel framework that effectively combines molecular-scale features and macroscopic properties of herbs with clinical symptoms, and provides the refined representation of their multiscale associations, enhancing the effectiveness of TCM formula recommendation. This framework can integrate molecular-scale chemical features and macroscopic properties of herbs, and capture complex local and global relations in the heterogeneous graph of symptoms and herbs, providing the effective embedding representation of their multiscale features and associations in a unified semantic space. Based on the refined feature representation, the framework is not only compatible with both traditional unordered formula recommendation task and the ordered herb sequence generation task, but also improves model's performance in both tasks. Comprehensive evaluations demonstrate FMASH's superior performance on the TCM formula recommendation over the state-of-the-art (SOTA) baseline, achieving relative improvements of 9.45\% in Precision@5, 12.11% in Recall@5, and 11.01% in F1@5 compared to the SOTA model on benchmark datasets. This work facilitates the practical application of AI-based TCM formula recommendation system.

翻译：中医药通过针对患者个体的方剂在疾病治疗与健康维护方面展现出卓越疗效。然而，当前基于人工智能的中医药方剂推荐模型与方法主要关注症状与草药之间基于数据的文本关联，未能充分利用其在多尺度（尤其是分子尺度）的特征与关系。为应对这些局限，本文提出症状与草药多尺度关联融合框架，该新颖框架有效结合了草药的分子尺度特征与宏观属性及临床症状，并提供了其多尺度关联的精细化表征，从而提升了中医药方剂推荐的有效性。该框架能够整合草药的分子尺度化学特征与宏观属性，并捕捉症状与草药异质图中复杂的局部与全局关系，在统一语义空间中提供其多尺度特征与关联的有效嵌入表示。基于精细化特征表示，该框架不仅兼容传统的无序方剂推荐任务和有序草药序列生成任务，同时提升了模型在这两项任务中的性能。综合评估表明，FMASH在中医药方剂推荐任务上优于现有最优基准模型，在基准数据集上相比最优模型在Precision@5、Recall@5和F1@5指标上分别实现了9.45%、12.11%和11.01%的相对提升。本工作推动了基于人工智能的中医药方剂推荐系统的实际应用。