Evidence map›Paper›PMID 38310576›Full record

ArticleCurrent computer-aided drug design2025

AI-based Virtual Screening of Traditional Chinese Medicine and the Discovery of Novel Inhibitors of TCTP.

Juxia Bai, Yangyang Ni, Yuqi Zhang, Junfeng Wan, Liqun Liang, Haoran Qiao, Yanyan Zhu, Qingjie Zhao, Huiyu Li

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Article in Current computer-aided drug design, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 42% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Juxia BaiCollege of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 201306, China.ORCID 0009-0005-4381-3559
Yangyang NiCollege of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 201306, China.
Yuqi ZhangCollege of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 201306, China.
Junfeng WanCollege of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 201306, China.
Liqun LiangCollege of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 201306, China.
Haoran QiaoCollege of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 201306, China.
Yanyan ZhuCollege of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 201306, China.
Qingjie ZhaoShanghai Frontiers Science Center for TCM Chemical Biology, Innovation Research Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China.
Huiyu LiCollege of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 201306, China.ORCID 0000-0002-0749-7107
Shanghai University of Electric Power · CNShanghai University of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTranslationally controlled tumour protein (TCTP) is associated with tumor diseases, such as breast cancer, and its inhibitor can reduce the growth of tumor cells. Unfortunately, there is currently no effective medication available for treating TCTP-related breast cancer.

objectivesThe objective of this study was to explore the inhibitor candidates among natural compounds for the treatment of breast cancer related to TCTP protein.

methodsTo explore the potential inhibitors of TCTP, we first screened out four potential inhibitors in the Traditional Chinese Medicine (TCM) for cancer based on AI virtual screening using the docking method, and then revealed the interaction mechanism of TCTP and four candidate inhibitors from TCM with molecular docking and molecular dynamics (MD) methods.

resultsBased on the conformational characteristics and the MD properties of the four leading compounds, we designed the new skeleton molecules with the AI method using MolAICal software. Our MD simulations have revealed that different small molecules bind to different sites of TCTP, but the flexible regions and the signaling pathways are almost the same, and the VDW and hydrophobic interactions are crucial in the interactions between TCTP and ligands.

conclusionWe have proposed the candidate inhibitor of TCTP. Our study has provided a potential new method for exploring inhibitors from Traditional Chinese Medicine (TCM).

Indexed as

Artificial IntelligenceBiomarkers, TumorBreast NeoplasmsDrugs, Chinese HerbalDrug DiscoveryFemaleHumansLigandsMedicine, Chinese TraditionalMolecular Docking SimulationMolecular Dynamics SimulationTumor Protein, Translationally-Controlled 1Biomarkers, TumorDrugs, Chinese HerbalLigandsTPT1 protein, humanTumor Protein, Translationally-Controlled 1AI-based virtual screeningAI drug design.molecular dockingmolecular dynamics simulationTCMTCTP

Identifiers

PMID38310576
OpenAlexW4391522278

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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.