Natural product-based drug combinations (NPDCs) present distinctive advantages in treating complex diseases. While high-throughput screening (HTS) and conventional computational methods have partially accelerated synergistic drug combination discovery, their applications remain constrained by experimental data fragmentation, high costs, and extensive combinatorial space. Recent developments in artificial intelligence (AI), encompassing traditional machine learning and deep learning algorithms, have been extensively applied in NPDC identification. Through the integration of multi-source heterogeneous data and autonomous feature extraction, prediction accuracy has markedly improved, offering a robust technical approach for novel NPDC discovery. This review comprehensively examines recent advances in AI-driven NPDC prediction, presents relevant data resources and algorithmic frameworks, and evaluates current limitations and future prospects. AI methodologies are anticipated to substantially expedite NPDC discovery and inform experimental validation.
Artificial Intelligence ; Biological Products/chemistry* ; Humans ; Drug Combinations ; Drug Discovery/methods* ; Machine Learning ; Algorithms

Objective To discuss the method of preventing injury of recurrent laryngeal nerve in thyroid surgery. Methods 362 cases of thyroid operation in our hospital were retrospectively studied, which consists of 188 cases with single lateral partial or total thyroidectomy, 127 with double lateral partial thyroidectomy,31 with single lateral total thyroidectomy plus isthmectomy, 16 with thyroid carcinoma radical correction. Results There was no injury,except for 3 cases damage to validate the recurrent laryngeal nerve in thyroid surgery,which were fully recov- ered after treatment. Conclusion To dissect recurrent laryngeal nerve routinely in inthyroid surgery can prevent and reduce the injury.