Artificial intelligence-driven identification and mechanistic exploration of synergistic anti-aging compounds from Dengzhan Shengmai formulation
4 affiliations
- 1Hebei Province Key Laboratory of Study and Exploitation of Chinese Medicine, Chengde Medical University, Chengde, China
- 2Chengde Medical University, Chengde, China
- 3Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing, China
- 4Hebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, Hebei, China
Abstract
Aging is a complex biological process involving multiple dysregulated pathways, and synergistic compound combinations offer distinct therapeutic advantages through multi-target and multi-pathway interactions. Traditional Chinese Medicine (TCM) formulations are inherently synergistic, yet systematically identifying their active anti-aging combinations remains a major challenge. Here, we developed DeepSMCA, a deep learning-based framework integrating molecular descriptors, ADMET parameters, and protein-protein interaction (PPI) network embeddings learned via a variational graph auto-encoder (VGAE), combined with a ResNetDNN classifier and the Bliss independence model, to identify synergistic combinations of anti-aging compound from the Dengzhan Shengmai (DZSM) formulation. Trained on 914 curated compounds, DeepSMCA achieved an area under the curve (AUC) of 0.9849 on the validation set, outperforming conventional machine learning and deep learning baselines, and interpretability analysis revealed that PPI network features contributed most (59.5%) to model predictions. Chemical profiling identified 30 constituents in DZSM, from which the three top-ranked synergistic combinations (Com1–3) were validated in D-galactose (D-Gal)-induced senescent PC12 cells. All three combinations enhanced cell viability, alleviated oxidative stress, attenuated intracellular reactive oxygen species accumulation, and decreased senescence-associated β-galactosidase-positive cells by up to 54.79%. Transcriptomic analysis showed that the combinations reversed 1,001–1,037 D-Gal-induced differentially expressed genes (DEGs), which were enriched in 18 shared aging-related pathways centered on longevity regulation, FoxO, p53, and autophagy signaling. Compound-target–aging-pathway network analysis further revealed complementary target engagement among constituents. This study establishes an interpretable, proof-of-concept computational–experimental pipeline for dissecting multi-component synergy in complex formulations, providing a generalizable strategy for anti-aging drug discovery from TCM.
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BibTeX
@article{hou2026artificial,
title = {{Artificial intelligence-driven identification and mechanistic exploration of synergistic anti-aging compounds from Dengzhan Shengmai formulation}},
author = {Jingyi Hou and Xueli Li and Miao Gu and Kaikai Ding and Kailan Yang and Bowen Xu},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0356888},
url = {https://doi.org/10.1371/journal.pone.0356888},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial intelligence-driven identification and mechanistic exploration of synergistic anti-aging compounds from Dengzhan Shengmai formulation
AU - Jingyi Hou
AU - Xueli Li
AU - Miao Gu
AU - Kaikai Ding
AU - Kailan Yang
AU - Bowen Xu
PY - 2026
DA - 2026/09/08
JO - PLOS One
DO - 10.1371/journal.pone.0356888
UR - https://doi.org/10.1371/journal.pone.0356888
AB - Aging is a complex biological process involving multiple dysregulated pathways, and synergistic compound combinations offer distinct therapeutic advantages through multi-target and multi-pathway interactions. Traditional Chinese Medicine (TCM) formulations are inherently synergistic, yet systematically identifying their active anti-aging combinations remains a major challenge. Here, we developed DeepSMCA, a deep learning-based framework integrating molecular descriptors, ADMET parameters, and protein-protein interaction (PPI) network embeddings learned via a variational graph auto-encoder (VGAE), combined with a ResNetDNN classifier and the Bliss independence model, to identify synergistic combinations of anti-aging compound from the Dengzhan Shengmai (DZSM) formulation. Trained on 914 curated compounds, DeepSMCA achieved an area under the curve (AUC) of 0.9849 on the validation set, outperforming conventional machine learning and deep learning baselines, and interpretability analysis revealed that PPI network features contributed most (59.5%) to model predictions. Chemical profiling identified 30 constituents in DZSM, from which the three top-ranked synergistic combinations (Com1–3) were validated in D-galactose (D-Gal)-induced senescent PC12 cells. All three combinations enhanced cell viability, alleviated oxidative stress, attenuated intracellular reactive oxygen species accumulation, and decreased senescence-associated β-galactosidase-positive cells by up to 54.79%. Transcriptomic analysis showed that the combinations reversed 1,001–1,037 D-Gal-induced differentially expressed genes (DEGs), which were enriched in 18 shared aging-related pathways centered on longevity regulation, FoxO, p53, and autophagy signaling. Compound-target–aging-pathway network analysis further revealed complementary target engagement among constituents. This study establishes an interpretable, proof-of-concept computational–experimental pipeline for dissecting multi-component synergy in complex formulations, providing a generalizable strategy for anti-aging drug discovery from TCM.
SN - 1932-6203
ER -