Evidence map›Paper›PMID 41096558›Full record

ArticleInternational journal of molecular sciences2025

HazChemNet: A Deep Learning Model for Hazardous Chemical Prediction.

Nan Zhang, Hexiang Qiu, Hongxia Cai, Zhiru Li, Yutong Li, Zinan Li, Lijuan Qi, Hongju Du, Yan Pan, Haiming Jing and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Nan ZhangBeijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Hexiang QiuLaboratory of Aging Research, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610056, China.
Hongxia CaiLaboratory of Aging Research, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610056, China.
Zhiru LiLaboratory of Aging Research, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610056, China.
Yutong LiLaboratory of Aging Research, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610056, China.
Zinan LiBeijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Lijuan QiBeijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Hongju DuBeijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Yan PanLaboratory of Aging Research, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610056, China.ORCID 0009-0003-3713-8329
Haiming JingBeijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Junyu NingBeijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing 100013, China.
Bo XianLaboratory of Aging Research, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610056, China.ORCID 0009-0006-1873-8435
Shan GaoBeijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing 100013, China.

Funding

2021 Research Start-up Fund-Fresh Wave Y030212059003033Construction Project - Health Toxicology Discipline "Academic Leader" Project 02-08Science and Technology Program of Beijing, China Z231100004523001
6 · The paper itself

Abstract

The identification of hazardous chemicals is critical for mitigating environmental and health risks, yet existing methods often lack efficiency and accuracy. This study presents HazChemNet, a deep learning model integrating attention-based autoencoders and mixture-of-experts architectures, designed to predict chemical hazardousness from molecular structures. The study utilized a dataset of 2428 hazardous compounds from China's 2015 hazardous chemical list. Features were derived from molecular fingerprints and physicochemical descriptors, with external validation on 52 unseen chemicals achieving 92.3% accuracy for hazardous and 84.6% for non-hazardous classifications. Experimental validation using

Indexed as

Deep LearningHazardous SubstancesAnimalsCaenorhabditis elegansChinaHazardous Substancesdecision support systemsdeep learningmolecular fingerprintspublic safety

Identifiers

PMID41096558
PMCPMC12524297

What OpenQuestion holds

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LicenceCC BY
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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.