Evidence map›Paper›PMID 41909737›Full record

ArticleActa pharmaceutica Sinica. B2026

TeroACT: A terpenoid bioactivity landscape and discovery platform.

XiaoJuan Shen, Shijia Yan, Xu Kang, Kangwei Xu, Yongxing Jian, Tao Zeng, Guohui Wan, Ruibo Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Acta pharmaceutica Sinica. B, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

8 authors.

XiaoJuan ShenState Key Laboratory of Anti-Infective Drug Discovery and Development, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.
Shijia YanState Key Laboratory of Anti-Infective Drug Discovery and Development, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.
Xu KangState Key Laboratory of Anti-Infective Drug Discovery and Development, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.
Kangwei XuState Key Laboratory of Anti-Infective Drug Discovery and Development, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.
Yongxing JianState Key Laboratory of Anti-Infective Drug Discovery and Development, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.
Tao ZengState Key Laboratory of Anti-Infective Drug Discovery and Development, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.
Guohui WanState Key Laboratory of Anti-Infective Drug Discovery and Development, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.
Ruibo WuState Key Laboratory of Anti-Infective Drug Discovery and Development, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Terpenoids exhibit diverse biological activities and thus have a wide range of pharmacological applications. In modern drug discovery, data-driven deep models play a crucial role in facilitating efficient feature representation and knowledge inference. To explore the uncharted bioactivity space of terpenoids, the construction of a multi-dimensional relational terpenoid database is essential for mapping terpenoid-bioactivity profiles. In this study, we first constructed a large-scale biological knowledge graph by integrating various data types, including terpenoid compounds, protein targets, cellular targets, genes, diseases, and their interrelationships. Subsequently, we developed a network-based disease prediction model, as well as optimized multiple compound-protein interaction prediction tools to extend the framework for activity research. These resources have been deployed on a user-friendly web platform (TeroACT) accessible at: http://terokit.qmclab.com/teroact/. Using

Indexed as

Anti-inflammatoryAnti-melanomaDeep learningDeep neural networkDisease predictionDrug repositionKnowledge graphTerpenoids

Identifiers

PMID41909737
PMCPMC13031156

What OpenQuestion holds

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Registered trials

None linked

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.