Evidence map›Paper›PMID 41820321›Full record

ArticleCurrent pharmaceutical design2026

Multidimensional Data-Driven Mechanistic Insights into Anle Tablets for Depression Treatment through Molecular Docking and Dynamics.

Tengyu Chen, Miao Zhang, Rongxin Liu, Haijie Dong, Yusen Zhao, Mingyuan Luan, Anling Zhang, Hongzong Si

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Article in Current pharmaceutical design, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Tengyu ChenCollege of Life Sciences, Qingdao University, Qingdao, 266071, Shandong, China.
Miao ZhangSchool of Basic Medicine, Qingdao University, Qingdao, 266071, Shandong, China.
Rongxin LiuCollege of Life Sciences, Qingdao University, Qingdao, 266071, Shandong, China.
Haijie DongState Key Laboratory of Bio-fibers and Eco-textiles, Qingdao University, Qingdao, 266071, Shandong, China.
Yusen ZhaoSchool of Basic Medicine, Qingdao University, Qingdao, 266071, Shandong, China.
Mingyuan LuanDepartment of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, 72076, Tübingen, Germany.
Anling ZhangAcademic Affairs Office, Qingdao University, Qingdao, 266071, Shandong, China.
Hongzong SiState Key Laboratory of Bio-fibers and Eco-textiles, Qingdao University, Qingdao, 266071, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDepression is a serious mental health problem, leading to low mood, loss of interest, and even extreme behaviors. Anle tablets are commonly used in the clinical treatment of depression; however, their mechanism of action is still unclear.

methodsComponents and targets of Anle tablets were identified using Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), PubChem, and SwissTargetPrediction. Depression-related targets were searched from the Genecards, Online Mendelian Inheritance in Man (OMIM), and the Therapeutic Target Database (TTD). A network diagram was constructed to screen the core components. We conducted enrichment analysis to demonstrate the underlying molecular mechanisms. Feature targets were identified utilizing the Gene Expression Omnibus (GEO), machine learning, and a nomogram. We subsequently performed preliminary validation using molecular docking and another GEO dataset. Finally, we performed a dynamic analysis of drug-target protein binding using molecular dynamics simulations.

resultsIGF1R, HSD11B1, GABRA1, and OPRK1 were screened as the feature targets. The core components were screened, such as 3,22-Dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid (MOL004905), (+)-Anomalin, and 7-Acetoxy-2-methylisoflavone. The primary mechanisms of action were associated with synaptic function and neurotransmitter transmission. The drug component MOL004905 and the target protein HSD11B1 emerged as the optimal docking pair. Their binding sites and the forces of interaction between them revealed strong stability. DISCUSSION: This study employed a multifaceted approach, integrating network pharmacology, bioinformatics, molecular docking, and molecular dynamics simulations, to analyze the components, targets, and mechanisms of Anle tablets in the treatment of depression. It also simulated the combination of drug molecules and target proteins and conducted a comprehensive evaluation.

conclusionAnle tablets may exert their therapeutic effects by targeting feature targets through their core active components, thereby modulating synaptic function and neurotransmitter transmission.

Indexed as

Antidepressive AgentsDepressionDrugs, Chinese HerbalMolecular Docking SimulationMolecular Dynamics SimulationHumansMedicine, Chinese TraditionalTabletsAntidepressive AgentsDrugs, Chinese HerbalTabletsAnle tabletsbioinformaticsdepressionmolecular dockingmolecular dynamicsnetwork pharmacologynomogram

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.