REPOSITIONING DRUG OF GLP-1 AGONIST IN PARKINSON DISEASE BASED ON MACHINE LEARNING AND MOLECULAR DOCKING
This study aims to identify potential inhibitors of GLP-1 agonist drugs against ?Synuclein protein, relevant for Parkinson's treatment. Using data from CHEMBL (CHEMBL6152) and the crystal structure of the target ?-Synuclein (PDB ID 3Q28), a machine learning model was built with scikit-learn t...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/81497 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | This study aims to identify potential inhibitors of GLP-1 agonist drugs against ?Synuclein protein, relevant for Parkinson's treatment. Using data from CHEMBL
(CHEMBL6152) and the crystal structure of the target ?-Synuclein (PDB ID 3Q28),
a machine learning model was built with scikit-learn to predict the pIC50 values of
224 GLP-1 agonist compounds. Molecular descriptors were calculated using
RDKit and PaDEL-Descriptor to generate data in binary format. As a result,
compounds CHEMBL3237911, CHEMBL250091, CHEMBL250310, and
CHEMBL442281 emerged as promising candidates based on the predicted pIC50
with high values and showed binding affinity results from molecular docking
ranging from 7.9740 to 8.5240 kcal/mol. The results of molecular docking provided
strong interaction with the target based on residue analysis identifying several key
residues in ligand binding. In conclusion, CHEMBL3237911, CHEMBL250091,
CHEMBL250310, and CHEMBL442281 have significant potential as GLP-1
agonist inhibitors for the treatment of Parkinson's, however further experimental
testing is required to validate that GLP-1 agonistt can be used as an alternative
treatment of Parkinson's.
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