Dr. Öğr. Üyesi SADETTİN YAVUZ UĞURLU

1. μOR-ligand: target-aware view-based hybrid feature selection for μ-opioid receptor ligand functional classification

Uğurlu S. Y.

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN , cilt.39, sa.2, ss.1-53, 2025 (SCI-Expanded)

2. MEGA PROTAC, MEGA DOCK-based PROTAC mediated ternary complex formation pipeline with sequential filtering and rank aggregation

Ugurlu S. Y., McDonald D., Enisoglu R., Zhu Z., He S.

Scientific Reports , cilt.15, sa.1, 2025 (SCI-Expanded)

3. CoBdock-2: enhancing blind docking performance through hybrid feature selection combining ensemble and multimodel feature selection approaches

Ugurlu S. Y.

Journal of Computer-Aided Molecular Design , cilt.39, sa.1, 2025 (SCI-Expanded)

4. Inter-HVI: Bridging Interpretability and Accuracy in Hypervalent Iodine Reactivity Prediction

Uğurlu S. Y.

JOURNAL OF SOLUTION CHEMISTRY , cilt.54, sa.11, ss.1403-1453, 2025 (SCI-Expanded)

5. Inter-Pol: An Interpretable Machine Learning Framework for Solvent Polarity Prediction

Uğurlu S. Y.

JOURNAL OF SOLUTION CHEMISTRY , cilt.15, sa.7, ss.1001-1035, 2025 (SCI-Expanded)

6. Inter-Hammett: Enhancing Interpretability in Hammett‘s Constant Prediction via Extracting Rules

UĞURLU S. Y.

ChemistrySelect , cilt.10, sa.30, 2025 (SCI-Expanded)

7. Machine Learning Applications in Drug Discovery

Uğurlu S. Y.

FRENCH-UKRAINIAN JOURNAL OF CHEMISTRY , cilt.13, sa.1, ss.1-66, 2025 (ESCI)

8. Cobdock: an accurate and practical machine learning-based consensus blind docking method

Ugurlu S. Y., McDonald D., Lei H., Jones A. M., Li S., Tong H. Y., et al.

Journal of Cheminformatics , cilt.16, sa.1, 2024 (SCI-Expanded)

9. MEF-AlloSite: an accurate and robust Multimodel Ensemble Feature selection for the Allosteric Site identification model

Ugurlu S. Y., McDonald D., He S.

Journal of Cheminformatics , cilt.16, sa.1, 2024 (SCI-Expanded)

10. Computational Methods in Drug Discovery and Development

UĞURLU S. Y.

OAJ Materials and Devices , cilt.8, 2024 (Hakemli Dergi)

11. Investigation of metallacages for cisplatin encapsulation using Density Functional Theory (DFT)

UĞURLU S. Y.

OAJ Materials and Devices , 2024 (Hakemli Dergi)

Dr. Öğr. Üyesi SADETTİN YAVUZ UĞURLU

Yayınlar & Eserler

1. μOR-ligand: target-aware view-based hybrid feature selection for μ-opioid receptor ligand functional classification

2. MEGA PROTAC, MEGA DOCK-based PROTAC mediated ternary complex formation pipeline with sequential filtering and rank aggregation

3. CoBdock-2: enhancing blind docking performance through hybrid feature selection combining ensemble and multimodel feature selection approaches

4. Inter-HVI: Bridging Interpretability and Accuracy in Hypervalent Iodine Reactivity Prediction

5. Inter-Pol: An Interpretable Machine Learning Framework for Solvent Polarity Prediction

6. Inter-Hammett: Enhancing Interpretability in Hammett‘s Constant Prediction via Extracting Rules

7. Machine Learning Applications in Drug Discovery

8. Cobdock: an accurate and practical machine learning-based consensus blind docking method

9. MEF-AlloSite: an accurate and robust Multimodel Ensemble Feature selection for the Allosteric Site identification model

10. Computational Methods in Drug Discovery and Development

11. Investigation of metallacages for cisplatin encapsulation using Density Functional Theory (DFT)

Yayın

Yayın (Scopus)

Atıf (Scopus)

H-İndeks (Scopus)

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