Topological Characterization of Parkinson’s Disease Drugs: A Graph-Theoretical Pilot Study

Parkinson’s disease (PD) is a neurodegenerative disorder with limited disease-modifying therapies. Computational models can provide predictive insights into drug properties, although critically limited datasets pose challenges. Fifteen FDA-approved Parkinson’s disease drugs were represented as hydrogen-suppressed molecular graphs. Twelve degree-based topological indices were computed and used as descriptors for predicting seven physicochemical properties (MR, P, MV, MW, nHA, nRotB, Complexity). Multi-layer perceptron artificial neural network (ANN) and Random Forest (RF) models were trained. Model performance was evaluated using Leave-One-Out Cross-Validation (LOOCV). The statistical robustness of the models was verified using a Y-randomization test. Shapley Additive Explanations (SHAP) were applied for interpretability. The ANN demonstrated high predictive correlation on the small dataset for MR (R² = 0.876), P (R² = 0.875), MW (R² = 0.837), and nHA (R² = 0.901). Lower predictive performance was observed for MV (R² = 0.729), molecular Complexity (R² = 0.706), and nRotB (R² = 0.308). RF provided comparable results but was generally outperformed by ANN. The Y-randomization test yielded consistently negative average R²rand values (lowest R²rand = -1.708), confirming the absence of chance correlation. SHAP analysis identified the most influential topological indices for each property in ANN. ANN-based QSPR modeling with degree-based descriptors can accurately predict physicochemical properties of PD drugs for certain endpoints. These models were proven statistically robust through Y-randomization validation. Limitations include the small dataset size and high-dimensional descriptor space, highlighting the need for external validation, larger datasets, and inclusion of additional 3D/quantum descriptors for more complex pharmacokinetic endpoints.