Supervised PhD and MSc Theses

Supervised PhD theses:

• Beril Çolak Günay (2024). Investigation of the mechanisms of hERG1 blocker toxins as anti-cancer agent with molecular modeling techniques — Thesis No: 876534 

  
  

• İsmail Erol (2023). Investigation of dimerization in angiotensin receptors by computational methods — Thesis No: 806943 

  
  

• Hind Al-Janabi (2022). Drug repurposing effort for the novel acetylcholinesterase and butyrylcholinesterase targets: A combined in silico and in vitro study — Thesis No: 717729 

  
  

• Busecan Aksoydan (2021). Integrated molecular modeling approaches for the novel therapeutics by using cytosolic and membrane-bound target proteins as model systems — Thesis No: 692481 

  
  

• Gülşah Aydın (2020). Identification of p53-MDM2 potential inhibitors with virtual screening and multidimensional molecular modeling methods — Thesis No: 636315 

  
  

• Yusuf Serhat İş (2019). Design of monoamine oxidase enzyme (MAO) inhibitors play important role in the treatment of neurodegenerative diseases using computer aided methods — Thesis No: 608516 

  
  

• Gülru Kayık (2017). In silico design of hERG non-blocker compounds with retained pharmacological activity using multi-scale molecular modeling applications — Thesis No: 485207

  
  

• Ramin Ekhteiari Salmas (2015). Multi-scale modeling and investigation of activation mechanisms of G protein-coupled receptors — Thesis No: 363831 

  
  

Supervised MSc theses:

• Ezgi Sambur (2023). Virtual screening of large-scale small molecule libraries against Bruton tyrosine kinase effective in chronic lymphocytic leukaemia — Thesis No: 814375 

  
  

• Ehsan Sayyah (2023). Novel resistance-free RET tyrosine kinase inhibitor discovery through dynamic structure-based pharmacophore and QSAR modeling and virtual screening of ultra-large ligand libraries — Thesis No: 816163 

  
  

• Safa Haddad (2023). Developing novel hERG blocker models using heteroatom type and numbers from extensive ligand libraries — Thesis No: 816749 

  
  

• Haneen Ammuri (2022). Identification of novel PARP1 inhibitors based on structural similarities of FDA approved drugs — Thesis No: 793527 

  
  

• İlayda Tolu (2022). In silico screening of the approved drugs, peptidomimetics and designing of new peptides against Axl-Gas6 target — Thesis No: 751106 

  
  

• Ntsoaki Baithédi Motapanyane (2022). Applications of molecular modelling approaches for the identification of novel SARS-CoV-2 RdRp inhibitors — Thesis No: 758552 

  
  

• Md Kamrul Hasan (2021). Molecular mechanism of AT1R/PARP1 inhibitors interactions using combined molecular modeling approaches and physics-driven virtual identification of novel therapeutics against retinal inflammation — Thesis No: 710115 

  
  

• Lalehan Oktay (2020). Integrated ligand- and target-driven-based virtual screening studies for the identification of novel therapeutics against breast cancer — Thesis No: 648941 

  
  

• Arhun Ali Balkan (2020). Investigation of anti-quorum sensing and anti-biofilm activities on Pseudomonas aeruginosa of Peltigera species by lichen and endolichenic fungus specimens in vitro and in silico methods — Thesis No: 634588 

  
  

• Ayla Yıldız (2020). Determination of the effects of Chrysophanol on Pseudomonas aeruginosa quorum sensing mechanism and biofilm formation via in vitro and in silico methods — Thesis No: 634586 

  
  

• Asena Himmetoğlu (2020). Machine learning algorithms and combined multi-scale molecular modeling simulations against NADPH oxidase (NOX) enzymes for designing of small molecule therapeutics — Thesis No: 650515 

  
  

• Vuslat Öykü Sayın (2019). Structure-based drug design studies for the discovery of novel carbonic anhydrase IX-selective inhibitors — Thesis No: 595464 

  
  

• Gurbet Tutumlu (2019). Identification of novel hit molecules against B-Cell Leukemia/Lymphoma-2 (Bcl-2) — Thesis No: 535273 

  
  

• Işık Kantarıcıoğlu (2017). Discovery of novel AT1 inhibitors using computational methods — Thesis No: 491210