Integrating artificial intelligence with small molecule therapeutics and precision medicine for neurochemical understanding of Alzheimer's diseases.

Alzheimer's disease (AD) is the most common neurodegenerative condition and continues to pose significant clinical and research challenges due to its complex causes and limited treatment success. Conventional therapies have primarily focused on amyloid-beta (Aβ) and tau proteins, but these efforts have yet to produce optimal results. This review explores emerging interdisciplinary strategies that integrate artificial intelligence (AI), small molecule drugs, and precision treatments to tackle AD's intricacies. AI has significantly enhanced early detection, neuroimaging, and biomarker discovery using machine learning and deep learning. These state-of-art methods helps to analyze biomarker profiles and imaging data, thereby enabling tailored diagnostic and therapeutic strategies for individual patients. At the same time, computational methods have expedited the development of small molecules targeting Aβ buildup, tau pathology, and inflammation. Emerging treatments, including monoclonal antibodies, RNA-based therapies, and nanotechnology, provide promising alternatives to conventional approaches. The integration of AI with multi-omics and structure-guided drug design supports the creation of precise, individualized treatments. However, challenges in ethics, regulation, and clinical application persist. This review emphasizes the collaborative potential of AI, pharmacology, and biomedical engineering in revolutionizing AD treatment, highlighting the need for cross-disciplinary efforts to confront this urgent neurological condition.