Akademik Veri Yönetim Sistemi
9th International Conference on Biomedical Engineering and Bioinformatics, Praha, Çek Cumhuriyeti, 19 - 21 Eylül 2025, ss.52-53, (Özet Bildiri)
Parkinson’s disease is a progressive neurodegenerative disorder marked by the degeneration of dopaminergic neurons. Conventional therapies provide only symptomatic relief and often lead to undesirable side effects. In this study, we present a multi-step computational strategy to identify novel plant-based drug candidates targeting Parkinson’s disease. First, a comprehensive set of phytochemicals with known biological activity was collected from literature and public databases. All currently used anti-Parkinson drugs were also compiled. Tanimoto similarity scores were calculated between phytochemicals and approved drugs to identify the top 20 structurally similar candidates. These were then used to generate binary and ternary combinations, which were evaluated based on ADMET properties using SwissADME and pkCSM. After pharmacokinetic and toxicity filtering, the best combinations were docked against four key Parkinson 's-related proteins: D2R (6CM4), TH (6PAH), VMAT2 (8JSW), and 5HT2A (6A93, 6A94, 7VOE) using AutoDock Vina in a Linux-based automated docking environment. Some of them showed stronger binding affinities (up to −10.2 kcal/mol) than Levodopa. The top combination underwent a 100 ns molecular dynamics simulation in GROMACS.