Integrating CADD1 and UHPLC-ESI-HRMS2 to screen natural products for potential multi-target drugs against Alzheimer's disease

Alzheimer's disease (AD) is driven by multiple pathological pathways, making multi-target strategies highly attractive yet challenging to implement efficiently. Here, we present an integrated workflow combining ligand-based AI virtual screening, post-column UHPLC-ESI-HRMS ligand fishing, molecular dynamics simulations, cell-based assays, and network pharmacology to rapidly identify natural product scaffolds with desired activity profiles from large compound libraries. Using Aβ 1–42 and tau as model targets, a PyRMD deep-learning model was trained on curated ChEMBL data and applied to screen a 9.7-million-compound subset of ZINC15. Hits were matched against natural product databases, prominently enriching diarylheptanoids and pointing to turmeric ( Curcuma longa ) as a top candidate plant. Subsequent UHPLC-ESI-HRMS ligand fishing of turmeric extract confirmed non-covalent binding of eight curcuminoids to Aβ 1–42 . 100-ns MD simulations predicted favourable binding (ΔG < –10 kcal/mol) of C1–C4 to both Aβ 1–42 fibril cores and tau PHF (Paired Helical Filaments) cores. In SH-SY5Y cells, C1–C3 dose-dependently protected SH-SY5Y cells from Aβ 1–42 oligomer-induced toxicity and significantly reduced ThT fluorescence in Aβ 1–42 fibrillization and pre-formed fibril systems. Network pharmacology analysis of C1–C3 revealed 107 putative AD-related targets enriched in PI3K-Akt, EGFR, and PD-1/PD-L1 pathways. Although tau binding remains computationally predicted and requires further experimental validation, this study demonstrates a scalable, closed-loop platform capable of rediscovering and experimentally validating known bioactive natural scaffolds with multi-target potential from ultralarge libraries using curcuminoids as a successful proof-of-concept example. • Integrated AI virtual screening and UHPLC-ESI-HRMS rediscovers curcuminoids as multi-target Aβ/tau binders from 9.7 M compounds. • Turmeric curcuminoids bind Aβ₁₋₄₂ non-covalently; MD predicts strong dual binding to Aβ and tau cores (ΔG < −10 kcal/mol). • Curcuminoids protect SH-SY5Y cells from Aβ toxicity and reduce ThT fluorescence in fibril systems. • Network pharmacology identifies 107 CE targets enriched in PI3K-Akt, EGFR, and PD-1/PD-L1 pathways. • Scalable closed-loop platform validates potential natural multi-target scaffolds for Alzheimer's disease drug discovery.

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Publication Details

Journal
Results in Chemistry
Published
2026-02-21
DOI
https://doi.org/10.1016/j.rechem.2026.103162
Primary Topic
Curcumin's Biomedical Applications
Type
article
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article

Integrating CADD1 and UHPLC-ESI-HRMS2 to screen natural products for potential multi-target drugs against Alzheimer's disease

Yangyang Zhang, Shan Shu, Yuan Cao, Jinqi Yang et al.
Results in Chemistry
Curcumin's Biomedical Applications
article

Integrating CADD1 and UHPLC-ESI-HRMS2 to screen natural products for potential multi-target drugs against Alzheimer's disease

Yangyang Zhang, Shan Shu, Yuan Cao, Jinqi Yang, Chunlin Yue, Zihan Shen
article en

Abstract

Alzheimer's disease (AD) is driven by multiple pathological pathways, making multi-target strategies highly attractive yet challenging to implement efficiently. Here, we present an integrated workflow combining ligand-based AI virtual screening, post-column UHPLC-ESI-HRMS ligand fishing, molecular dynamics simulations, cell-based assays, and network pharmacology to rapidly identify natural product scaffolds with desired activity profiles from large compound libraries. Using Aβ 1–42 and tau as model targets, a PyRMD deep-learning model was trained on curated ChEMBL data and applied to screen a 9.7-million-compound subset of ZINC15. Hits were matched against natural product databases, prominently enriching diarylheptanoids and pointing to turmeric ( Curcuma longa ) as a top candidate plant. Subsequent UHPLC-ESI-HRMS ligand fishing of turmeric extract confirmed non-covalent binding of eight curcuminoids to Aβ 1–42 . 100-ns MD simulations predicted favourable binding (ΔG < –10 kcal/mol) of C1–C4 to both Aβ 1–42 fibril cores and tau PHF (Paired Helical Filaments) cores. In SH-SY5Y cells, C1–C3 dose-dependently protected SH-SY5Y cells from Aβ 1–42 oligomer-induced toxicity and significantly reduced ThT fluorescence in Aβ 1–42 fibrillization and pre-formed fibril systems. Network pharmacology analysis of C1–C3 revealed 107 putative AD-related targets enriched in PI3K-Akt, EGFR, and PD-1/PD-L1 pathways. Although tau binding remains computationally predicted and requires further experimental validation, this study demonstrates a scalable, closed-loop platform capable of rediscovering and experimentally validating known bioactive natural scaffolds with multi-target potential from ultralarge libraries using curcuminoids as a successful proof-of-concept example. • Integrated AI virtual screening and UHPLC-ESI-HRMS rediscovers curcuminoids as multi-target Aβ/tau binders from 9.7 M compounds. • Turmeric curcuminoids bind Aβ₁₋₄₂ non-covalently; MD predicts strong dual binding to Aβ and tau cores (ΔG < −10 kcal/mol). • Curcuminoids protect SH-SY5Y cells from Aβ toxicity and reduce ThT fluorescence in fibril systems. • Network pharmacology identifies 107 CE targets enriched in PI3K-Akt, EGFR, and PD-1/PD-L1 pathways. • Scalable closed-loop platform validates potential natural multi-target scaffolds for Alzheimer's disease drug discovery.

Results in ChemistryVol. 23
Chinese Academy of Sciences (CN), Beijing National Laboratory for Molecular Sciences (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 46%
Curcumin's Biomedical Applications
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