Computational prioritization of multi-target inhibitors: explainable QSAR and docking-based discovery of dual AChE/BACE1 chemotypes
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
Tam Kayıt
| Alan | Değer | Dil |
|---|---|---|
| dc.contributor.author | Bozkır, İsa | |
| dc.contributor.author | İbişoğlu, Merve Seda | |
| dc.contributor.author | Kayıkçıoğlu Bozkır, İlknur | |
| dc.contributor.author | Güler, Halil İbrahim | |
| dc.date.accessioned | 2026-10-22T10:19:46.902426724Z | |
| dc.date.available | 2026-10-22T10:19:46.902426724Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | 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. | |
| dc.description.sponsorship | Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TUBITAK | |
| dc.identifier.articlenumber | 54 | |
| dc.identifier.doi | 10.1007/s10822-025-00757-3 | |
| dc.identifier.eissn | 1573-4951 | |
| dc.identifier.issn | 0920-654X | |
| dc.identifier.issue | 1 | |
| dc.identifier.pmid | 41603986 | |
| dc.identifier.scopus | 2-s2.0-105028818962 | |
| dc.identifier.startpage | 54 | |
| dc.identifier.uri | https://rdlopen.rawdatalibrary.net/r/gumushane/12191 | |
| dc.identifier.volume | 40 | |
| dc.identifier.wos | WOS:001673223500001 | |
| dc.language.iso | en | |
| dc.publisher | Springer Science and Business Media Deutschland GmbH | |
| dc.relation.ispartof | Journal of Computer-Aided Molecular Design | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | AChE | |
| dc.subject | Alzheimer’s disease | |
| dc.subject | BACE1 | |
| dc.subject | Docking/ADMET | |
| dc.subject | Dual inhibitors | |
| dc.subject | Explainable QSAR | |
| dc.subject.classification | Biochemistry & Molecular Biology | |
| dc.subject.classification | Biophysics | |
| dc.subject.classification | Computer Science, Interdisciplinary Applications | |
| dc.subject.classification | Computer Science | |
| dc.subject.other | Acetylcholinesterase | |
| dc.subject.other | Amyloid Precursor Protein Secretases | |
| dc.subject.other | Aspartic Acid Endopeptidases | |
| dc.subject.other | Cholinesterase Inhibitors | |
| dc.subject.other | Drug Discovery | |
| dc.subject.other | Humans | |
| dc.subject.other | Molecular Docking Simulation | |
| dc.subject.other | Quantitative Structure-Activity Relationship | |
| dc.subject.other | Adaptive boosting | |
| dc.subject.other | Classification (of information) | |
| dc.subject.other | Decision trees | |
| dc.subject.other | Ligands | |
| dc.subject.other | Molecules | |
| dc.subject.other | Nearest neighbor search | |
| dc.subject.other | Neurodegenerative diseases | |
| dc.subject.other | Random forests | |
| dc.subject.other | Scaffolds | |
| dc.subject.other | Support vector machines | |
| dc.subject.other | beta secretase 1 | |
| dc.subject.other | beta secretase 1 inhibitor | |
| dc.subject.other | beta secretase inhibitor | |
| dc.subject.other | cholinesterase inhibitor | |
| dc.subject.other | donepezil | |
| dc.subject.other | unclassified drug | |
| dc.subject.other | aspartic proteinase | |
| dc.subject.other | BACE1 protein, human | |
| dc.subject.other | secretase | |
| dc.subject.other | Alzheimer | |
| dc.subject.other | Alzheimer’s disease | |
| dc.subject.other | BACE1 | |
| dc.subject.other | Docking/ADMET | |
| dc.subject.other | Dual inhibitors | |
| dc.subject.other | Explainable quantitative structure–activity relationship | |
| dc.subject.other | Gradient boosting | |
| dc.subject.other | Prioritization | |
| dc.subject.other | Quantitative structure activity relationship | |
| dc.subject.other | Article | |
| dc.subject.other | cheminformatics | |
| dc.subject.other | chemotype | |
| dc.subject.other | confidence interval | |
| dc.subject.other | controlled study | |
| dc.subject.other | correlation coefficient | |
| dc.subject.other | enzyme inhibition | |
| dc.subject.other | hydrogen bond | |
| dc.subject.other | k nearest neighbor | |
| dc.subject.other | molecular docking | |
| dc.subject.other | molecular model | |
| dc.subject.other | nested cross validation | |
| dc.subject.other | Protein Data Bank | |
| dc.subject.other | quantitative structure activity relation | |
| dc.subject.other | random forest | |
| dc.subject.other | regression model | |
| dc.subject.other | Shapley additive explanation | |
| dc.subject.other | support vector machine | |
| dc.subject.other | chemistry | |
| dc.subject.other | drug development | |
| dc.subject.other | human | |
| dc.subject.other | metabolism | |
| dc.subject.other | Docking | |
| dc.subject.other | Hydrogen bonds | |
| dc.title | Computational prioritization of multi-target inhibitors: explainable QSAR and docking-based discovery of dual AChE/BACE1 chemotypes | |
| dc.type | Article | |
| dspace.entity.type | Publication | |
| rdl.citation.openalex | 5 | |
| rdl.citation.scopus | 2 | |
| rdl.citation.wos | 2 | |
| rdl.department | Department of Medical Services and Techniques, Gumushane Health Services Vocational School, Gumushane University, Gümüşhane, Turkey | |
| rdl.department | Department of Software Engineering, Faculty of Engineering and Natural Sciences, Gumushane University, Gümüşhane, Turkey | |
| rdl.oa.status | hybrid | |
| rdl.oa.url | https://link.springer.com/content/pdf/10.1007/s10822-025-00757-3.pdf | |
| rdl.sdg | SDG03 | |
| rdl.source.index | SCOPUS | |
| rdl.source.index | WOS | |
| rdl.source.index | PUBMED | |
| rdl.wos.index | Science Citation Index Expanded (SCI-EXPANDED) |

