• Uma Kumari
• Neha Singh

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder driven by complex molecular dysfunctions in the brain over many years. A central hallmark is the accumulation of α-Synuclein, which forms distinct protein aggregates known as polymorphs. These polymorphic variants differ in toxicity, propagation, and responsiveness to therapies, underscoring the need for advanced, systems-level approaches in drug development. Recent breakthroughs in multi-omics technologies, artificial intelligence (AI), and computational biology offer powerful tools to unravel PD pathogenesis and accelerate therapeutic discovery. Yet, integrating these innovations into reproducible and unified pipelines remains a major challenge. Multi-omics disciplines—including genomics, transcriptomics, proteomics, metabolomics, and lipidomics—enable identification of novel drug targets, patient stratification based on pathology, and prioritization of compounds with higher therapeutic potential. A notable example is the 5A polymorph (PDB ID: 8PK4), which illustrates how structural variations in α-Synuclein can inform rational, structure-based drug design. Building on this, we propose a conceptual pipeline that integrates multi-omics data with AI, BioPython, and machine learning to design drugs targeting specific polymorphs. This framework combines disease modules, virtual screening, and predictive modeling. Finally, we address current limitations, ethical considerations, and highlight future opportunities such as digital twin modeling to transform PD drug discovery.

Parkinson’s Disease, α-Synuclein polymorphs,Multi-omics, Genomics, Proteomics, Metabolomics, Artificial Intelligence (AI), Machine Learning (ML), Structure-based drug design, Digital twin modeling

"Mult-omics-based drug discovery pipelines incorporating machine learning, AI, and BioPython in Parkinson's disease: A case study of 5A polymorph of alpha-synuclein", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 2, page no.b617-b634, February-2026, Available :http://www.jetir.org/papers/JETIR2602181.pdf

"Mult-omics-based drug discovery pipelines incorporating machine learning, AI, and BioPython in Parkinson's disease: A case study of 5A polymorph of alpha-synuclein", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 2, page no. ppb617-b634, February-2026, Available at : http://www.jetir.org/papers/JETIR2602181.pdf