An Attention-Driven Graph Transformer With Nonlinear Modeling and Neuro-Fuzzy Fusion for High-Order Toxic Molecular...

Learning expressive representations for complex, size-varied molecular graphs remains a fundamental challenge in toxic molecular property prediction and regression. The inherent nonlinearity of atomic interactions, together with intricate structural dependencies and latent high-order structural characteristics, makes accurate graph-based learning particularly difficult. Although recent deep learning (DL)/graph neural network (GNN) approaches have leveraged message passing, attention mechanisms, and graph transformer architectures, these techniques still suffer from limited nonlinear expressiveness, insufficient modeling of high-order structural information, and a lack of explicit uncertainty handling. To address these limitations, we propose a novel AKAGTL model, which is an attention-driven Kolmogorov-Arnold network (KAN)-based graph transformer framework for toxic molecular graph embedding and regression learning. Unlike existing approaches that rely on linear or shallow nonlinear transformations, our proposed AKAGTL model introduces a structured KAN-based functional transformation to explicitly model complex high-order atomic interactions within an attention-driven graph transformer backbone. In addition, high-order structural representations are systematically incorporated to complement structural encoding, while a Gaussian neuro-fuzzy fusion mechanism enables uncertainty-aware aggregation of heterogeneous feature spaces. The proposed framework is evaluated on multiple molecular toxicity benchmarks under a unified experimental protocol with repeated runs. Comprehensive experiments within benchmark molecular graph datasets demonstrate that our AKAGTL model can consistently improve regression accuracy compared to representative GNN and graph transformer baselines. These findings suggest that jointly modeling nonlinear functional interactions, structural dependencies, and uncertainty-aware fusion provides a more expressive and robust solution for toxic molecular graph embedding and regression learning.