Evidence map›Paper›PMID 40846852›Full record

ArticleNature communications2025

Harnessing artificial intelligence to identify Bufalin as a molecular glue degrader of estrogen receptor alpha.

Shilong Jiang, Keyi Liu, Ting Jiang, Hui Li, Xiao Wei, Xiaoya Wan, Changxin Zhong, Rong Gong, Zonglin Chen, Chan Zou and 3 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Review
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  7. Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026
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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

13 authors.

Shilong Jiang *Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China.
Keyi Liu *Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.ORCID http://orcid.org/0000-0002-2188-0110
Ting Jiang *Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China.
Hui LiXiangya School of Pharmaceutical Sciences, Central South University, Changsha, China.
Xiao WeiXiangya School of Pharmaceutical Sciences, Central South University, Changsha, China.
Xiaoya WanDepartment of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China.
Changxin ZhongDepartment of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China.
Rong GongDepartment of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China.
Zonglin ChenDepartment of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China.
Chan ZouCenter for Clinical Pharmacology, the Third Xiangya Hospital, Central South University, Changsha, China.
Qing ZhangDepartment of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
Yan ChengDepartment of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China. yancheng@csu.edu.cn.ORCID http://orcid.org/0000-0002-7905-0443
Dongsheng CaoXiangya School of Pharmaceutical Sciences, Central South University, Changsha, China. oriental-cds@163.com.ORCID http://orcid.org/0000-0003-3604-3785

Funding

National Natural Science Foundation of China (National Science Foundation of China) 22173118
6 · The paper itself

Abstract

Target identification in natural products plays a critical role in the development of innovative drugs. Bufalin, a compound derived from traditional medicines, has shown promising anti-cancer activity; however, its precise molecular mechanism of action remains unclear. Here, we employ artificial intelligence, molecular docking, and molecular dynamics simulations to elucidate the molecular mechanism of Bufalin. Using an integrated multi-predictive strategy, we identify CYP17A1, ESR1, mTOR, AR, and PRKCD as the potential targets of Bufalin. Subsequent validation via surface plasmon resonance, biotin pulldown, and thermal shift assays confirms Bufalin's direct binding to ESR1, which encodes estrogen receptor alpha (ERα). Molecular docking analyses pinpoint Bufalin's selective interaction with Arg394 on ERα. Molecular dynamic simulations further show that Bufalin acts as a molecular glue, enhancing the interaction between ERα and the E3 ligase STUB1, thereby promoting proteasomal degradation of ERα. Given the therapeutic potential of ERα degradation in overcoming endocrine resistance, we investigate the inhibitory effect of Bufalin on endocrine-resistant models and prove Bufalin reverses Tamoxifen resistance in vitro, in vivo, and in patient-derived breast cancer organoids from tamoxifen-relapsed cases. Collectively, our findings indicate that Bufalin functions as a molecular glue to degrade ERα, offering a potential therapeutic strategy for reversing Tamoxifen resistance.

Indexed as

Artificial IntelligenceBreast NeoplasmsBufanolidesEstrogen Receptor alphaAnimalsAntineoplastic AgentsCell Line, TumorDrug Resistance, NeoplasmFemaleHumansMCF-7 CellsMiceMolecular Docking SimulationMolecular Dynamics SimulationProtein BindingTamoxifenAntineoplastic AgentsbufalinBufanolidesESR1 protein, humanEstrogen Receptor alphaTamoxifen

Identifiers

PMID40846852
PMCPMC12373995

What OpenQuestion holds

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Registered trials

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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.