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 investigate the effect of cold pressed Camelina Sativa oil (CPCO) on growth performance and lipid metabolism of hyperlipemia rats. Method The rats were divided into control group (Cont),high fat diet group (HFD),fish oil group (HFD+FO),low dose CPCO group (HFD+LCPCO) and high dose CPCO group (HFD+HCPCO). The latter 4 groups were fed high fat feed. Fo and CPCO were given by gavage. Total cholesterol (TC),triglyceride (TG),high density lipoprotein cholesterol (HDL-C) and HDL-C subtype levels in serum were determined at the end of 6w. The distribution of HDL-C particles was studied by agarose gel electrophoresis. Results HFD resulted typical hyperlipemia. Body weight,TC and TG level were significantly decreased,and HDL-C particles tended to be miniaturized and the HDL3 level increased by feeding Camelina Sativa oil as compared with HFD group. TC content in HFD+FO and HFD+HCPCO group was decreased 46.06% and 30.60% respectively. TG content in these two groups was decreased 25.81% and 22.72% respectively as compared with HFD group. Conclusion Chinese Camelina Sativa oil which contained high level of ?-linolenic acid(ALA)could prevent and cure hyperlipidemia by regulating blood lipid metabolism and protecting hepatic function.