Evidence map›Paper›PMID 41502979›Full record

ArticleBioactive materials2026

AI-guided precise design of antimicrobial polymers through high-throughput screening technology on an automated platform.

Tianyi Zhang, Yuhui Wu, Ye Tian, Youxiang Wang, Peng Zhang, Qiannuan Shi, Qun Fang, Jianzhang Pan, Qiao Jin, Jian Ji

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioactive materials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

10 authors.

Tianyi ZhangMOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, 310058, PR China.
Yuhui WuMOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, 310058, PR China.
Ye TianMOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, 310058, PR China.
Youxiang WangMOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, 310058, PR China.
Peng ZhangMOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, 310058, PR China.
Qiannuan ShiInstitute of Intelligent Chemical Manufacturing and iChemFoundry Platform, Engineering Research Center of Functional Materials Intelligent Manufacturing of Zhejiang Province, ZJU-Hangzhou Global Scientific and Technological Innovation Center, 311200, PR China.
Qun FangInstitute of Intelligent Chemical Manufacturing and iChemFoundry Platform, Engineering Research Center of Functional Materials Intelligent Manufacturing of Zhejiang Province, ZJU-Hangzhou Global Scientific and Technological Innovation Center, 311200, PR China.
Jianzhang PanInstitute of Intelligent Chemical Manufacturing and iChemFoundry Platform, Engineering Research Center of Functional Materials Intelligent Manufacturing of Zhejiang Province, ZJU-Hangzhou Global Scientific and Technological Innovation Center, 311200, PR China.
Qiao JinMOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, 310058, PR China.
Jian JiMOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, 310058, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antimicrobial peptides (AMPs)-mimicking antimicrobial polymers show great potential as therapeutic alternatives to antibiotics in the looming "post-antibiotic era". However, the discovery of new AMP-mimicking antimicrobial polymers is challenging due to the vast chemical space of side-chain combinations. The advancement of AI-guided high-throughput screening enables more efficient, precise, and intelligent material design. Herein, we integrate combinatorial chemistry, machine learning, and automated high-throughput synthesis and characterization platforms to establish a new paradigm for the design of antimicrobial polymers with excellent biocompatibility. Starting with a library of 13,728 combinations, a seed dataset of 400 structures is generated, followed by four Design-Build-Test-Learn iterations using a new machine learning model. 7 top-performing candidates are screened with a minimum inhibitory concentration (MIC) ≤ 8 μg/mL and an inhibitory concentration causing 20 % cell death (IC

Indexed as

Antimicrobial polymersAutomated platformCombinatorial chemistryHigh-throughput screeningMachine learning

Identifiers

PMID41502979
PMCPMC12769852

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.