Integrated Machine Learning-based Toxicity Prediction with Molecular Docking for Safer Drug Candidate Screening in Parkinson’s Disease
Integrated Machine Learning-based Toxicity Prediction with Molecular Docking for Safer Drug Candidate Screening in Parkinson’s Disease
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Original abstract
Parkinson disease is a progressive neurodegenerative disorder marked by the abnormal buildup of α-synuclein into toxic fibrils, which lead to neuronal degeneration and motor problems. Among all the identified variants, the type 5A polymorphic structure (8PK4) has been strongly associated with disease progression and represents a promising therapeutic target for the development of safer and more effective drug candidates. In the present study, an integrated computational framework combining with molecular docking and machine-learning-based toxicity prediction was employed to identify potential natural compounds with high therapeutic efficacy and minimal toxic effects. Five bioactive phytochemicals, namely baicalein, rutin, ellagic acid, kaempferol, and ferulic acid, were selected based on their reported neuroprotective potential and screened against the α-synuclein target protein. Molecular docking analysis was performed using the CB-Dock platform to evaluate binding affinity, interaction stability, and residue-level interactions within the active binding pocket. The results demonstrated that all selected compounds exhibited favourable binding interactions with critical amino acid residues, particularly PHE4, LYS6, and GLU35, which are associated with α-synuclein aggregation and stabilization. Among the tested compounds, ellagic acid displayed the strongest binding affinity and the most stable interaction profile, suggesting enhanced inhibitory potential against the target protein. To further assess drug safety, toxicity predictions were performed using the ProTox-II machine-learning platform, evaluating multiple toxicity endpoints, including hepatotoxicity, neurotoxicity, mutagenicity, carcinogenicity, immunotoxicity, and cytochrome P450-mediated interactions. The toxicity assessment revealed that ellagic acid exhibited the lowest predicted toxicity among all screened compounds, while rutin showed a comparatively high LD 50 value, indicating reduced acute toxicity and a favourable safety margin. The integration of molecular docking with artificial intelligence-driven toxicity prediction provides a rapid, cost-effective, and reliable strategy for safer drug candidate screening in Parkinson’s disease research. Overall, the study highlights the potential of natural compounds, particularly ellagic acid, as promising therapeutic leads for further experimental validation and future neuroprotective drug development.