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

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.