Drug screening for α-synuclein aggregation inhibitors via multimodal graph neural network.

The pathological aggregation of α-synuclein (α-syn) constitutes a pivotal hallmark in the progression of neurodegenerative disorders, including Parkinson's disease, underscoring the imperative need for identifying site-specific ligands. This study presents, for the first time, an advanced deep learning framework specifically designed for the prediction of molecular properties associated with α-syn. The framework integrates graph-based contextual attention mechanisms, structural feature aggregation protocols, and dual-channel feature integration, complemented by a composite regularization strategy that synergizes mean squared error minimization, Kullback-Leibler divergence-induced latent space regularization, and L2 norm penalization, thereby delivering outstanding predictive accuracy on the independent test dataset with MSE of 0.1812. Mechanistic insights derived from GNNExplainer analysis and molecular docking studies (PDB: 6A6B) elucidated that aromatic ring systems (benzene ring significance: 0.737) and hydrogen bond donor groups (amino group significance: 0.438) play critical roles in mediating high-affinity ligand-receptor interactions through π-π stacking within the hydrophobic pocket formed by Val82 and Ala89 residues, as well as directed hydrogen bonding involving catalytic residues Ser42 and Lys45. These findings not only enhance the understanding of inhibitor mechanisms but also establish a novel framework for the preliminary screening of small-molecule therapeutics, thereby laying a rigorous groundwork for structure-guided drug optimization and rational molecular design.