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

## TL;DR

This review explores how machine learning is being used to improve drug combinations for treating neurodegenerative diseases like Alzheimer's and Parkinson's.

## Contribution

The paper highlights novel machine learning approaches for drug combination optimization in neurodegenerative diseases.

## Key findings

- Machine learning methods like CNNs and transformers are being used for drug discovery and screening in NDDs.
- ML approaches show potential for improving therapeutic development and patient outcomes in neurodegenerative diseases.
- The review emphasizes the rising socioeconomic burden of NDDs and the need for algorithmic innovation.

## Abstract

As the global population
ages, the prevalence of neurodegenerative
diseases (NDDs)including Alzheimer’s disease, Parkinson’s
disease, Huntington’s disease, Multisystem Atrophy (multiple
system atrophy), and amyotrophic lateral sclerosiscontinues
to rise, largely driven by environmental, metabolic, and lifestyle
risk factors. Advances in computational technologies, particularly
machine learning (ML) and deep learning, are reshaping research in
this field. This review summarizes the major features of these diseases
and emphasizes the role of ML in drug discovery, virtual screening,
drug repurposing, and drug combination optimization. Representative
approaches include support vector machines for classification, convolutional
neural networks|convolutional neural network for imaging analysis,
recurrent neural networks for temporal biomedical data, and transformers
for multimodal integration. These methods highlight the potential
of computational strategies to improve therapeutic development. In
addition, the review underscores the substantial incidence rates and
socioeconomic burden of these conditions, which have made them focal
points for algorithmic innovation. With research evolving rapidly,
the development of AI-driven approaches is expected to enable more
effective, targeted interventions and improve patient outcomes. This
Perspective provides a concise overview of current progress and identifies
promising future directions in the fight against NDDs.