Machine learning and free energy clustering reveal PAH protein binding linked to AD risk.

Chao Chen, Yuxi He, Ying Ni, Dongyan Song, Maoping Chu, Wensheng Zhang

Abstract read

In one paragraph

Article in iScience, 2026. 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

6 authors.

Chao ChenInstrumentation and Service Center for Science and Technology, Beijing Normal University at Zhuhai, Zhuhai, China.

Yuxi HePediatric Research Institute, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.

Ying NiEngineering Research Center of Natural Medicine, Ministry of Education, Beijing Normal University at Zhuhai, Zhuhai, China.

Dongyan SongPediatric Research Institute, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.

Maoping ChuPediatric Research Institute, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.

Wensheng ZhangEngineering Research Center of Natural Medicine, Ministry of Education, Beijing Normal University at Zhuhai, Zhuhai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study develops a computational framework integrating bioinformatics, machine learning, and ΔG clustering to prioritize polycyclic aromatic hydrocarbons (PAHs) for Alzheimer's disease (AD)-associated neurotoxicity. PAH targets were predicted from ChEMBL/STITCH databases; AD-related differentially expressed genes (DEGs) were identified via WGCNA and differential expression analysis of GEO datasets. Protein-protein interaction (PPI) networks, GO/KEGG enrichment, and XGBoost feature selection identified PARP1, PTPN1, and ITGA4 as candidate core PAH targets enriched in neuroinflammation, microglial activation, lipid metabolism, and atherosclerosis pathways. Molecular docking produced ΔG heatmaps for clustering 16 PAHs into eight toxicity-similarity categories. Category-average ΔG values correlated linearly with literature LD

Indexed as

bioinformaticsmedicineneuroscience

Identifiers

PMID41953002

DOI10.1016/j.isci.2026.115311

PMCPMC13053772

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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.