Machine Learning Approaches for Optimizing Drug Combinations in Neurodegenerative Diseases: A Brief Review

Area of Science:

• Computational biology and neuroscience
• Artificial intelligence in medicine
• Drug discovery and development

Background:

• Rising global population aging leads to increased prevalence of neurodegenerative diseases (NDDs).
• Environmental, metabolic, and lifestyle factors contribute significantly to NDD development.
• NDDs pose a substantial socioeconomic burden, driving the need for innovative research.

Purpose of the Study:

• To review the role of machine learning (ML) in neurodegenerative disease research.
• To highlight ML applications in drug discovery, including virtual screening, repurposing, and combination optimization.
• To provide an overview of current progress and future directions for AI-driven NDD interventions.

Main Methods:

• Utilizing machine learning (ML) and deep learning algorithms for NDD research.
• Applying support vector machines for disease classification.
• Employing convolutional neural networks for medical image analysis.
• Leveraging recurrent neural networks for temporal biomedical data analysis.
• Implementing transformers for multimodal data integration.

Main Results:

• ML methods demonstrate potential for improving therapeutic development in NDDs.
• Computational strategies are crucial for addressing the complexities of NDDs.
• AI-driven approaches are poised to enhance the effectiveness and targeting of NDD interventions.

Conclusions:

• The integration of ML and AI is transforming the landscape of neurodegenerative disease research.
• Continued development of AI-driven approaches is essential for improving patient outcomes.
• Future research should focus on leveraging AI for more personalized and effective NDD treatments.