Computational Preprocessing Framework for Small-Molecule and $\alpha$-Synuclein Data Towards Parkinson's Drug Repurposing

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by the loss of dopaminergic neurons and abnormal aggregation of the$\alpha$synuclein protein, resulting in motor and cognitive deficits. The complexity of PD pathology and the restricted blood-brain barrier (BBB) permeability of most compounds present major challenges for therapeutic discovery. This study presents an implemented computational preprocessing framework for curating small-molecule and$\alpha$-synuclein datasets, supporting reliable drug-target interaction (DTI) prediction and drug repurposing. During drug preprocessing, a multi-step curation pipeline was executed to standardize and refine PubChem compounds through canonicalization, charge normalization, tautomer correction, and PAINS filtering, followed by Lipinski and Veber criteria with CNS multiparameter optimization to identify molecules with optimal drug-like neuroactive profiles. The protein preprocessing stage employed a confidence-guided refinement approach on the AlphaFold-Predicted$\alpha$-synuclein model (AF-P37840-F1-v6), combining per-residue pLDTT scoring, masking of low-confidence regions, and intrinsic disorder was assessed with IUPred2A, and structural repair under physiological conditions to generate a confidenceweighted structure optimized for computational modelling. Together, these harmonized preprocessing stages establish a reproducible, domain-specific foundation for subsequent ensemble docking, feature extraction, and graph-based learning. This framework enhances data quality, consistency, and interpretability in computational neurotherapeutics, supporting the future discovery of small-molecule modulators that effectively target$\alpha$-synuclein and potentially slow Parkinson's disease progression.