A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases

Area of Science:

• Neurology
• Computational Biology
• Pharmacology

Background:

• Global population aging increases age-related diseases, with neurological disorders posing a significant challenge.
• Neurodegenerative diseases like Alzheimer’s and Parkinson’s require novel therapeutic strategies.
• Computational science offers powerful tools for advancing neurodrug discovery.

Purpose of the Study:

• To review innovative computational strategies for neurodrug development.
• To summarize the state-of-the-art in molecular docking and artificial intelligence for neurodegenerative disease drug design.
• To highlight the role of in silico methods in combating neurodegeneration.

Main Methods:

• Review of recent applications of molecular docking techniques.
• Analysis of artificial intelligence (AI) approaches for ligand- and target-based drug design.
• Evaluation of computational methods in neurodrug discovery.

Main Results:

• Molecular docking and AI are pivotal in designing novel drug candidates.
• In silico methods accelerate the identification of potential therapeutics for neurodegenerative diseases.
• Understanding molecular structures aids in developing drugs against dementia, AD, and PD.

Conclusions:

• Computational methods, including molecular docking and AI, are crucial for effective neurodrug discovery.
• There is a need for comprehensive guidelines to optimize computational approaches in this field.
• In silico strategies play a vital role in addressing the growing burden of neurodegenerative diseases.