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Sürdürülebilir Kalkınma Amaçları

• 2

• MakaleAçık Erişim2026Scopus SJRQ2/r/gumushane/12191
  Computational prioritization of multi-target inhibitors: explainable QSAR and docking-based discovery of dual AChE/BACE1 chemotypes

  Bozkır, İsa; İbişoğlu, Merve Seda; Kayıkçıoğlu Bozkır, İlknur; Güler, Halil İbrahim

  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.

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  AtıfScopus2·WoS2·OpenAlex5

  İncele
• BildiriYalnız künye2025/r/gumushane/13955
  Tumor Classification via Subregion Combinations of Cortical L5PC6 Pyramidal Neurons

  Ozcan, Zubeyir; Bozkir, Ilknur Kayikcioglu; Kayikcioglu, Temel

  In this study, it was aimed to classify benign and malignant tumor samples by utilizing different subregions of a cortical L5PC6 pyramidal neuron. A spike-based learning method was applied to a real dataset, and a decision mechanism based on action potential generation was established by training a separate neuron for each class. During the training process, various types of synaptic connections and spike encoding schemes were evaluated and intensity coding yielded the highest performance for the pyramidal neuron. Regional analyses revealed that the basilar region particularly stood out in terms of classification success. The results demonstrate that the morphological and biophysical diversity of pyramidal neurons provides a significant advantage in classification tasks.

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  İncele
• BildiriYalnız künye2018/r/gumushane/16223
  Cell Master: A Versatile and User-Friendly Educational Software for Simulation of Neuronal Dynamics

  Özcan, Zubeyir; Kayikçioglu, Ilknur; Yildirim, Omer; Köse, Cemal; Kayikçioglu, Temel

  In this study, A user-friendly, versatile and easily accessible educational software design was done by using NEURON hoc programming in order to investigate the dynamics, biophysical properties of nerve cells and their responses to various stimulation. With the help of interfaces which prepared for users to be assisted visually, it is aimed to be able to easily perform almost any application desired. This software allows users to perform their simulations without the need to write any code. Considering the features given in detail in the study, it has been suggested to use the software in neuroscience education.

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