Computational approaches to screen candidate ligands with anti- Parkinson's activity using R programming

Area of Science:

• Computational chemistry
• Neuroscience
• Pharmacology

Background:

• Parkinson's disease (PD) is a neurodegenerative disorder affecting millions, with projections indicating over 100 million cases by 2050.
• Dopamine receptor D3 is implicated in cognitive and emotional changes associated with PD, making it a key therapeutic target.
• The limitations and side effects of synthetic drugs drive research towards natural compounds for PD treatment.

Purpose of the Study:

• To computationally screen phytochemicals for anti-Parkinson's activity.
• To identify novel drug candidates targeting the dopamine receptor D3.
• To evaluate the efficacy and safety of potential natural compounds for PD.

Main Methods:

• A library of 100 phytochemicals was compiled from literature.
• Lipinski's rule of five was applied for initial screening, followed by energy minimization.
• Molecular docking using Autodock Vina was performed, with Ropinirole as a reference drug.
• Toxicity analysis and R programming were used for bias reduction and selection.

Main Results:

• 88 molecules passed Lipinski's rule of five.
• Top eleven molecules were identified based on docking scores.
• Rosmarinic acid and Gingkolide A were computationally selected after toxicity and bias analysis.
• These two phytochemicals demonstrated significant potential against dopamine receptor D3.

Conclusions:

• Rosmarinic acid and Gingkolide A are promising natural compounds for further investigation in Parkinson's disease.
• Computational screening provides an effective approach to discover novel anti-Parkinson's agents from phytochemicals.
• Targeting dopamine receptor D3 with natural compounds offers a potential therapeutic strategy for Parkinson's disease.