Rethinking MS Therapeutics: From Disease Pathogenesis Mechanisms to AI-Driven Drug Discovery

Abstract

Multiple sclerosis (MS) is a chronic autoimmune disorder of the CNS, characterized by inflammation, demyelination, and progressive neurodegeneration. Disease progression involves four key interconnected stages: the activation of immune cells in peripheral lymph regions, the migration of autoreactive cells across the blood-brain barrier, demyelination, and an often incomplete remyelination response. Despite such significant therapeutic advances, major challenges remain in early diagnosis, patient stratification, and personalized intervention. Artificial intelligence has emerged as a powerful tool to address these challenges by integrating complex, multimodal data sets and uncovering patterns. This review provided a comprehensive overview of MS pathogenesis and evaluated current and emerging therapeutic strategies. Recent advances in applying AI-driven approaches to MS diagnosis, including MRI-based lesion detection, disease-activity prediction, and support for individualized prognosis, were also investigated. Additionally, generative and predictive computational frameworks that enable rapid drug development, repositioning, therapeutic target identification, and the rational design of new molecules with optimized safety and efficacy profiles in MS were reviewed. Finally, current limitations, ethical considerations, and barriers to clinical translation are discussed, emphasizing the need for high-quality datasets, standardized evaluation, and robust validation strategies. Synthesizing the emerging evidence, the current review highlights how AI-enabled methodologies are reshaping MS research, connecting molecular insights with clinical decision-making and opening new perspectives for more accurate diagnosis, deeper mechanistic understanding, and personalized therapeutic development.