Evidence map›Paper›PMID 39792782›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Machine Learning-Driven Prediction, Preparation, and Evaluation of Functional Nanomedicines Via Drug-Drug Self-Assembly.

Chengyuan Zhang, Yuchuan Yuan, Qiong Xia, Junjie Wang, Kangkang Xu, Zhiwei Gong, Jie Lou, Gen Li, Lu Wang, Li Zhou and 3 more

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Review
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  15. Machine Learning-Enhanced Nanoparticle Design for Precision Cancer Drug Delivery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  16. Article
  17. Review
  18. Review
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.

Chengyuan ZhangYunnan Key Laboratory of Stem Cell and Regenerative Medicine, Kunming Medical University, Kunming, 650500, China.ORCID https://orcid.org/0000-0001-7625-411X
Yuchuan YuanSchool of Medicine, Northwest University, Xi'an, 710068, China.ORCID https://orcid.org/0000-0002-8289-7813
Qiong XiaDepartment of Pharmacy, School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China.ORCID https://orcid.org/0009-0005-7027-3316
Junjie WangDepartment of Pharmacy, School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China.ORCID https://orcid.org/0009-0003-6742-1758
Kangkang XuDepartment of Pharmacy, School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China.
Zhiwei GongDepartment of Pharmacy, School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China.
Jie LouDepartment of Pharmacy, School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China.ORCID https://orcid.org/0000-0001-6639-9913
Gen LiDepartment of Pharmacy, School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China.
Lu WangDepartment of Pharmacy, School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China.
Li ZhouDepartment of Biomedical Engineering, School of Engineering, China Pharmaceutical University, Nanjing, 210009, China.
Zhirui LiuDepartment of Pharmacy, Xinan Hospital, Army Medical University, Chongqing, 400038, China.
Kui LuoDepartment of Radiology, and Department of Geriatrics, Huaxi MR Research Center (HMRRC), National Clinical Research Center for Geriatrics Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
Xing ZhouYunnan Key Laboratory of Stem Cell and Regenerative Medicine, Kunming Medical University, Kunming, 650500, China.ORCID https://orcid.org/0000-0003-0852-0687

Funding

Chongqing medical scientific research project 2022MSXM154Chongqing Municipal Technology Innovation and Application Development Special Key Project CSTB2021TIAD-KPX0051National Natural Science Foundation 32271445National Natural Science Foundation 81501583National Natural Science Foundation 82460703Postdoctoral Research Foundation of China 2021M700625
6 · The paper itself

Abstract

Small molecules as nanomedicine carriers offer advantages in drug loading and preparation. Selecting effective small molecules for stable nanomedicines is challenging. This study used artificial intelligence (AI) to screen drug combinations for self-assembling nanomedicines, employing physiochemical parameters to predict formation via machine learning. Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) are identified as effective carriers for antineoplastic drugs, with high drug loading. Nanomedicines, PEG-coated indomethacin/paclitaxel nanomedicine (PiPTX), and laminarin-modified indomethacin/paclitaxel nanomedicine (LiDOX), are developed with extended circulation and active targeting functions. Indomethacin/paclitaxel nanomedicine iDOX exhibits pH-responsive drug release in the tumor microenvironment. These nanomedicines enhance anti-tumor effects and reduce side effects, offering a rapid approach to clinical nanomedicine development.

Indexed as

Machine LearningNanomedicineAnimalsAnti-Inflammatory Agents, Non-SteroidalAntineoplastic AgentsDrug CarriersDrug Delivery SystemsHumansIndomethacinPaclitaxelAnti-Inflammatory Agents, Non-SteroidalAntineoplastic AgentsDrug CarriersIndomethacinPaclitaxelmachine learningnanomedicineself‐assemblysmall‐molecular drug carrier

Identifiers

PMID39792782
PMCPMC11884566

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.