Evidence map›Paper›PMID 42032010›Full record

Reviewnpj biomedical innovations2025

Application of Artificial Intelligence In Drug-target Interactions Prediction: A Review.

Qian Liao, Yu Zhang, Ying Chu, Yi Ding, Zhen Liu, Xianyi Zhao, Yizheng Wang, Jie Wan, Yijie Ding, Prayag Tiwari and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in npj biomedical innovations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
–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

28 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. A Scalable and Robust Ensemble Deep Learning Method for Predicting Drug-Target Interactions.Interdisciplinary sciences, computational life sciences · 2026
    Article
  4. Article
  5. Review
  6. Review
  7. Artificial Intelligence Across the Drug Development Lifecycle.Medical sciences (Basel, Switzerland) · 2026
    Review
  8. Article
  9. 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
    Review
  10. Review
  11. Article
  12. Review
  13. Review
  14. Review
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. 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

12 authors.

Qian Liao *School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China.
Yu Zhang *School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China.
Ying ChuSchool of Computer and Information Engineering, Harbin University of Commerce, Harbin, China.
Yi DingSchool of Computer and Information Engineering, Harbin University of Commerce, Harbin, China.
Zhen LiuSchool of Computer and Information Engineering, Harbin University of Commerce, Harbin, China.
Xianyi ZhaoSchool of Computer and Information Engineering, Harbin University of Commerce, Harbin, China.
Yizheng WangYangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
Jie Wanthe Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin, China.
Yijie DingYangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China. wuxi_dyj@csj.uestc.edu.cn.
Prayag TiwariSchool of Information Technology, Halmstad University, Halmstad, Sweden. prayag.tiwari@hh.se.
Quan ZouYangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China. zouquan@nclab.net.
Ke HanSchool of Computer and Information Engineering, Harbin University of Commerce, Harbin, China. thruster@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting drug-target interactions (DTI) is a complex task. With the introduction of artificial intelligence (AI) methods such as machine learning and deep learning, AI-based DTI prediction can significantly enhance speed, reduce costs, and screen potential drug design options before conducting actual experiments. However, the application of AI methods also faces several challenges that need to be addressed. This article reviews various AI-based approaches and suggests possible future directions.

Identifiers

PMID42032010
PMCPMC13055101

What OpenQuestion holds

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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 OpenQuestion graph.