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Here, rigorously curated ChEMBL-derived data were used to develop explainable QSAR (Quantitative structure\u2013activity relationship) models for dual-inhibition prioritization. Molecules were standardized, near-duplicates were removed using a Tanimoto similarity threshold (\u2265\u20090.80), 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\u2013Recall 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\u2009+\u2009ECFP6) achieved strong generalization (Recall \u2248 1.00, PR-AUC \u2248 0.84, MCC \u2248 0.81, F1 Score \u2248 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 (\u0394G, kcal\u00b7mol\u207b\n                    <jats:sup>1<\/jats:sup>\n                    ) 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.\n                  <\/jats:p>","DOI":"10.1007\/s10822-025-00757-3","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T07:03:22Z","timestamp":1769583802000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Computational prioritization of multi-target inhibitors: explainable QSAR and docking-based discovery of dual AChE\/BACE1 chemotypes"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4784-4668","authenticated-orcid":false,"given":"\u0130sa","family":"Bozk\u0131r","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9237-4357","authenticated-orcid":false,"given":"Merve Seda","family":"\u0130bi\u015fo\u011flu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3991-5676","authenticated-orcid":false,"given":"\u0130lknur","family":"Kay\u0131k\u00e7\u0131o\u011flu Bozk\u0131r","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7261-6790","authenticated-orcid":false,"given":"Halil \u0130brahim","family":"G\u00fcler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"issue":"Chemistry","key":"757_CR1","doi-asserted-by":"publisher","first-page":"100","DOI":"10.21608\/rpbs.2021.55758.1087","volume":"5","author":"AM Atta","year":"2021","unstructured":"Atta AM, Elgawish MS, Moustafa SM (2021) Recent insight into BACE1 as a potential target for treatment of Alzheimer\u2019s disease. 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