Dr. Öğr. Üyesi SADETTİN YAVUZ UĞURLU

Yayın Ağı

Makaleler 13

1. Prodrug-ML: prodrug-likeness prediction via machine learning on sampled negative decoys

Uğurlu S. Y., He S.

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN , cilt.40, sa.1, ss.40-75, 2026 (SCI-Expanded, Scopus)

2. De-TarDis: Decoy-Target Discovery Database Integrating Off-Targets, Promiscuity, Adverse/Side-Effects, and Screening Panels via Reciprocal Rank Fusion for Safety Assessment

Uğurlu S. Y.

International journal of advances in engineering and pure sciences (Online) , cilt.38, sa.1, ss.210-230, 2026 (TRDizin)

3. 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, Scopus)

4. 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, Scopus)

5. 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)

6. 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)

7. 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)

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

UĞURLU S. Y.

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

9. Machine Learning Applications in Drug Discovery

Uğurlu S. Y.

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

10. 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, Scopus)

11. 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, Scopus)

12. Computational Methods in Drug Discovery and Development

UĞURLU S. Y.

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

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

UĞURLU S. Y.

OAJ Materials and Devices , 2024 (Hakemli Dergi)

Hakemli Bilimsel Toplantılarda Yayımlanmış Bildiriler 1

1. PDBMINER: RANK AGGREGATION-BASED SELECTION OF QUALIFIED PROTEIN DATA BANK STRUCTURES

Uğurlu S. Y.

XII. International Health, Engineering and Sciences Congress, Toskent, Özbekistan, 10 - 12 Nisan 2026, ss.326-340, (Tam Metin Bildiri)

Metrikler

Dr. Öğr. Üyesi SADETTİN YAVUZ UĞURLU

Yayınlar & Eserler

1. Prodrug-ML: prodrug-likeness prediction via machine learning on sampled negative decoys

2. De-TarDis: Decoy-Target Discovery Database Integrating Off-Targets, Promiscuity, Adverse/Side-Effects, and Screening Panels via Reciprocal Rank Fusion for Safety Assessment

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

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

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

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

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

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

9. Machine Learning Applications in Drug Discovery

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

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

12. Computational Methods in Drug Discovery and Development

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

1. PDBMINER: RANK AGGREGATION-BASED SELECTION OF QUALIFIED PROTEIN DATA BANK STRUCTURES

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