Computational prioritization of multi-target inhibitors: explainable QSAR and docking-based discovery of dual AChE/BACE1 chemotypes

MakaleAçık Erişim
  • SDG 3 · Sağlık ve Kaliteli Yaşam

Özet

The discovery of dual acetylcholinesterase (AChE) and β-secretase (BACE1) inhibitors remains a promising strategy against multifactorial Alzheimer’s disease. Here, rigorously curated ChEMBL-derived data were used to develop explainable QSAR (Quantitative structure–activity relationship) models for dual-inhibition prioritization. Molecules were standardized, near-duplicates were removed using a Tanimoto similarity threshold (≥ 0.80), and physicochemical outliers were filtered prior to modeling. Multiple classifiers (including Light Gradient-Boosting Machine, eXtreme Gradient Boosting, Random Forest, Support Vector Machine, k-Nearest Neighbors and Gradient Boosting Decision Trees) and fingerprints (e.g., RDKit fingerprints, Extended Connectivity Fingerprint) were benchmarked under scaffold-based nested cross-validation to prevent data leakage. Class imbalance was handled with SMOTETomek applied strictly within training folds. Model selection relied on F-Score, Area Under the Precision–Recall Curve, Matthews Correlation Coefficient (MCC), and Recall, and performance was accompanied by bootstrap confidence intervals, calibration curves, and Y-randomization controls. In classification, the top model (GBDT + ECFP6) achieved strong generalization (Recall ≈ 1.00, PR-AUC ≈ 0.84, MCC ≈ 0.81, F1 Score ≈ 0.84). Shapley Additive Explanations (SHAP) analysis highlighted aromatic and hydrogen-bonding substructures as key positive contributors. Prospective candidates (e.g., CHEMBL5082250, CHEMBL1651126, CHEMBL1651127) were evaluated by active-site-focused docking against AChE (PDB: 4EY7) and BACE1 (PDB: 2G94) with essential waters retained; docking scores (ΔG, kcal·mol⁻1) were used for relative ranking of the ligands. SwissADME/pkCSM profiling suggested CNS-relevant properties (e.g., MPO, logBB, P-gp liability) and acceptable oral drug-likeness. Collectively, the workflow provides a reproducible and transparent pipeline for prioritizing dual AChE/BACE1 chemotypes and nominates testable scaffolds for experimental validation.

Anahtar Kelimeler

RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/gumushane/12191Resmi atıflarda kullanılan kalıcı adres
DOI
https://doi.org/10.1007/s10822-025-00757-3Yayıncı resmi DOI adresi
Tam Kayıt
AlanDeğerDil
dc.contributor.authorBozkır, İsa
dc.contributor.authorİbişoğlu, Merve Seda
dc.contributor.authorKayıkçıoğlu Bozkır, İlknur
dc.contributor.authorGüler, Halil İbrahim
dc.date.accessioned2026-10-22T10:19:46.902426724Z
dc.date.available2026-10-22T10:19:46.902426724Z
dc.date.issued2026
dc.description.abstractThe discovery of dual acetylcholinesterase (AChE) and β-secretase (BACE1) inhibitors remains a promising strategy against multifactorial Alzheimer’s disease. Here, rigorously curated ChEMBL-derived data were used to develop explainable QSAR (Quantitative structure–activity relationship) models for dual-inhibition prioritization. Molecules were standardized, near-duplicates were removed using a Tanimoto similarity threshold (≥ 0.80), and physicochemical outliers were filtered prior to modeling. Multiple classifiers (including Light Gradient-Boosting Machine, eXtreme Gradient Boosting, Random Forest, Support Vector Machine, k-Nearest Neighbors and Gradient Boosting Decision Trees) and fingerprints (e.g., RDKit fingerprints, Extended Connectivity Fingerprint) were benchmarked under scaffold-based nested cross-validation to prevent data leakage. Class imbalance was handled with SMOTETomek applied strictly within training folds. Model selection relied on F-Score, Area Under the Precision–Recall Curve, Matthews Correlation Coefficient (MCC), and Recall, and performance was accompanied by bootstrap confidence intervals, calibration curves, and Y-randomization controls. In classification, the top model (GBDT + ECFP6) achieved strong generalization (Recall ≈ 1.00, PR-AUC ≈ 0.84, MCC ≈ 0.81, F1 Score ≈ 0.84). Shapley Additive Explanations (SHAP) analysis highlighted aromatic and hydrogen-bonding substructures as key positive contributors. Prospective candidates (e.g., CHEMBL5082250, CHEMBL1651126, CHEMBL1651127) were evaluated by active-site-focused docking against AChE (PDB: 4EY7) and BACE1 (PDB: 2G94) with essential waters retained; docking scores (ΔG, kcal·mol⁻1) were used for relative ranking of the ligands. SwissADME/pkCSM profiling suggested CNS-relevant properties (e.g., MPO, logBB, P-gp liability) and acceptable oral drug-likeness. Collectively, the workflow provides a reproducible and transparent pipeline for prioritizing dual AChE/BACE1 chemotypes and nominates testable scaffolds for experimental validation.
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumu, TUBITAK
dc.identifier.articlenumber54
dc.identifier.doi10.1007/s10822-025-00757-3
dc.identifier.eissn1573-4951
dc.identifier.issn0920-654X
dc.identifier.issue1
dc.identifier.pmid41603986
dc.identifier.scopus2-s2.0-105028818962
dc.identifier.startpage54
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/gumushane/12191
dc.identifier.volume40
dc.identifier.wosWOS:001673223500001
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofJournal of Computer-Aided Molecular Design
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectAChE
dc.subjectAlzheimer’s disease
dc.subjectBACE1
dc.subjectDocking/ADMET
dc.subjectDual inhibitors
dc.subjectExplainable QSAR
dc.subject.classificationBiochemistry & Molecular Biology
dc.subject.classificationBiophysics
dc.subject.classificationComputer Science, Interdisciplinary Applications
dc.subject.classificationComputer Science
dc.subject.otherAcetylcholinesterase
dc.subject.otherAmyloid Precursor Protein Secretases
dc.subject.otherAspartic Acid Endopeptidases
dc.subject.otherCholinesterase Inhibitors
dc.subject.otherDrug Discovery
dc.subject.otherHumans
dc.subject.otherMolecular Docking Simulation
dc.subject.otherQuantitative Structure-Activity Relationship
dc.subject.otherAdaptive boosting
dc.subject.otherClassification (of information)
dc.subject.otherDecision trees
dc.subject.otherLigands
dc.subject.otherMolecules
dc.subject.otherNearest neighbor search
dc.subject.otherNeurodegenerative diseases
dc.subject.otherRandom forests
dc.subject.otherScaffolds
dc.subject.otherSupport vector machines
dc.subject.otherbeta secretase 1
dc.subject.otherbeta secretase 1 inhibitor
dc.subject.otherbeta secretase inhibitor
dc.subject.othercholinesterase inhibitor
dc.subject.otherdonepezil
dc.subject.otherunclassified drug
dc.subject.otheraspartic proteinase
dc.subject.otherBACE1 protein, human
dc.subject.othersecretase
dc.subject.otherAlzheimer
dc.subject.otherAlzheimer’s disease
dc.subject.otherBACE1
dc.subject.otherDocking/ADMET
dc.subject.otherDual inhibitors
dc.subject.otherExplainable quantitative structure–activity relationship
dc.subject.otherGradient boosting
dc.subject.otherPrioritization
dc.subject.otherQuantitative structure activity relationship
dc.subject.otherArticle
dc.subject.othercheminformatics
dc.subject.otherchemotype
dc.subject.otherconfidence interval
dc.subject.othercontrolled study
dc.subject.othercorrelation coefficient
dc.subject.otherenzyme inhibition
dc.subject.otherhydrogen bond
dc.subject.otherk nearest neighbor
dc.subject.othermolecular docking
dc.subject.othermolecular model
dc.subject.othernested cross validation
dc.subject.otherProtein Data Bank
dc.subject.otherquantitative structure activity relation
dc.subject.otherrandom forest
dc.subject.otherregression model
dc.subject.otherShapley additive explanation
dc.subject.othersupport vector machine
dc.subject.otherchemistry
dc.subject.otherdrug development
dc.subject.otherhuman
dc.subject.othermetabolism
dc.subject.otherDocking
dc.subject.otherHydrogen bonds
dc.titleComputational prioritization of multi-target inhibitors: explainable QSAR and docking-based discovery of dual AChE/BACE1 chemotypes
dc.typeArticle
dspace.entity.typePublication
rdl.citation.openalex5
rdl.citation.scopus2
rdl.citation.wos2
rdl.departmentDepartment of Medical Services and Techniques, Gumushane Health Services Vocational School, Gumushane University, Gümüşhane, Turkey
rdl.departmentDepartment of Software Engineering, Faculty of Engineering and Natural Sciences, Gumushane University, Gümüşhane, Turkey
rdl.oa.statushybrid
rdl.oa.urlhttps://link.springer.com/content/pdf/10.1007/s10822-025-00757-3.pdf
rdl.sdgSDG03
rdl.source.indexSCOPUS
rdl.source.indexWOS
rdl.source.indexPUBMED
rdl.wos.indexScience Citation Index Expanded (SCI-EXPANDED)