MLGT: A multimodal graph attention network for virtual screening of anti—Uveitis drugs

Abstract

Uveitis is a severe ocular inflammatory disease with complex immune—mediated pathogenesis, posing significant challenges for drug discovery. While artificial intelligence has accelerated virtual screening, existing models often inadequately integrate heterogeneous molecular features or address disease—specific mechanisms. To address these gaps, we propose MLGT (Multimodal Learning with Graph and molecular descriptors for Therapeutics), a novel graph attention network based on GATv2 that synergistically integrates molecular graph topology, bond attributes, and physicochemical descriptors within a unified deep learning framework. The model employs dynamic attention mechanisms to capture non—local atomic interactions and a dual—stream fusion module to combine graph embeddings with molecular descriptors. To mitigate data imbalance and overfitting, we implement label smoothing, class—balanced sampling, and SMILES randomization. Evaluated on a rigorously curated Uveitis—related compound dataset from ChEMBL, MLGT achieves state—of—the—art performance: 97.7% accuracy, 97.2% F1 score, 96.1% recall, and an AUC—ROC of 0.9156, surpassing existing graph learning and classical machine learning benchmarks. Ablation studies confirm the essential roles of multimodal fusion and attention mechanisms. This study provides an efficient, attention—based computational tool for targeted Uveitis drug screening and establishes a scalable AI—driven paradigm for precision drug discovery in complex diseases.