PREDICTION OF ANTI-PARKINSON POTENTIAL OF PHYTOCONSTITUENTS USING PREDICTION OF ACTIVITY SPECTRA OF SUBSTANCES SOFTWARE
Keywords:Nil, Prediction of activity spectra of substances, Levodopa, Postural instability
Objective: Neurodegenerative disorders are group of diseased conditions in which there is loss of neuron cells occur. The main objective of this study to find/search out the phytochemical with the help of prediction of activity spectra of substances (PASSs), those show maximum activity over the selected targets of the Parkinson's disease (PD).
Methods: PASSs is a valuable software which is used in this study, to predict the anti-Parkinson activity of different compounds. Canonical simplified molecular-input line-entry system is used for the prediction of anti-Parkinson activity which is obtained from PubChem website. The predicted activity also compared with marketed compound like levodopa.
Results: From the study, it was found that resveratrol was the only compound which has the activity on all the selected targets. On the other hand, stemazole and celastrol were found to have the least active compounds as both have the activity only on a single target.
Conclusion: In this research work, we tried to compile the information regarding the PASS predicted anti-Parkinson activity of some important phytoconstituents. We found that resveratrol can be a target for further investigation in the development of drug therapy for PD.
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