Machine learning-guided discovery of NEU1-targeting natural compounds with relevance to mitochondrial dysfunction-linked, fatigue-associated neurodegeneration in Alzheimer’s disease

Adnan, Mohd, Patel, Mitesh, Surti, Malvi and Deshpande, Sumukh 2026. Machine learning-guided discovery of NEU1-targeting natural compounds with relevance to mitochondrial dysfunction-linked, fatigue-associated neurodegeneration in Alzheimer’s disease. Journal of Disability Research 5 , e20260851. 10.57197/jdr-2026-0851

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Abstract

Alzheimer’s disease (AD), the most common form of dementia, is characterized not only by amyloid-beta (Aβ) accumulation and tau pathology but also by mitochondrial dysfunction, reduced energy metabolism, oxidative stress, and fatigue-associated neuronal damage. Neuraminidase-1 (NEU1), a lysosomal sialidase involved in autophagy, neuroinflammation, and mitochondrial regulation, has recently emerged as a promising therapeutic target in AD. This study aimed to identify novel NEU1 inhibitors targeting mitochondrial dysfunction and fatigue-related pathways using an integrated computational drug discovery approach involving machine learning (ML), quantitative structure–activity relationship modeling, molecular docking, and molecular dynamics simulations. A curated dataset of 12,239 bioactive natural compounds was pre-processed and screened based on drug-likeness properties. Molecular descriptors were generated using PaDEL software (RRID:SCR_014272), and predictive models were developed using Support Vector Machine, Random Forest (RF), and Extreme Gradient Boosting algorithms. Among these, RF demonstrated the best predictive performance with a test accuracy of 82.9%, R2 score of 0.6312, and root mean square error of 0.3221. Virtual screening against the NEU1 active site (PDB ID: 8DU5) identified several promising compounds, with 3-[1-(2,3-dihydro-1,4-benzodioxin-6-yl)-5-oxopyrrolidin-3-yl]-1-(3,4-dimethylphenyl) urea showing the highest binding affinity (−10.2 kcal/mol). Mitochondrial dysfunction and fatigue-associated neurodegeneration are discussed as literature-supported downstream consequences of NEU1-driven neuroinflammatory and desialylation pathways and direct mitochondrial or fatigue-related assays. Molecular dynamics simulations confirmed stable protein–ligand interactions with favorable RMSD, radius of gyration, and solvent accessible surface area profiles. Overall, this study highlights the potential of ML-guided screening in discovering NEU1 inhibitors targeting mitochondrial dysfunction and fatigue-associated neurodegenerative mechanisms in AD.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Medicine
Publisher:	King Salman Center for Disability Research
ISSN:	1658-9912
Date of First Compliant Deposit:	1 September 2026
Date of Acceptance:	4 August 2026
Last Modified:	01 Sep 2026 10:45
URI:	https://orca.cardiff.ac.uk/id/eprint/189274

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