C-S.B.54: Multimodal Siamese Network for Parkinson's Disease Diagnosis and Structure-based Drug Discovery for Therapeutic Development
Authors
Shaurya Gandhi
East Brunswick High School
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Parkinson’s Disease (PD) is often diagnosed after irreversible neurodegeneration due to reliance on physical symptoms for diagnosis. Current machine learning (ML) diagnosis models often rely on a single data modality, which limits predictive performance and disease mechanisms identification. Integrating multiple molecular and imaging modalities offers an opportunity to improve diagnostic accuracy while enabling systems-level insights into PD pathogenesis. We developed a multimodal ML framework combining MRI, biomarkers, and miRNA from Parkinson's Precision Medicine Initiative (PPMI). We used a Siamese Network with triplet loss to overcome data scarcity and model complex patient-similarity representations. We used model feature importances to create therapeutic hypotheses for targeting PD, leading to the identification of IL-6 as a gene driving PD inflammation. We validated IL-6 as a target through differential gene expression and gene set enrichment analyses. We applied structure-based drug design to inhibit IL-6 by first generating fragments within computationally identified binding pockets. These fragments were developed into final candidates through iterative optimization based on random mutation and selection of high-scoring structures, mimicking an evolutionary model. Our model achieves an ROC AUC of 0.94, leading to performance superior to single modality or naive data integration baseline methods. Using CNN-based docking and scoring, we designed drug candidates with binding affinities of -11.2 and -11.5 kcal/mol. This study offers a unique end-to-end pipeline linking multi-omics integration with structure-based drug design, demonstrating how interpretable multimodal machine learning can improve PD diagnosis and uncover disease mechanisms while identifying therapeutic targets.
Co-authors: Allison Huang, Shaurya Gandhi, Yuqi Zhang
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