Multimodal Multi-Granularity Fusion Model with Mamba Architecture for Ames Mutagenicity Prediction

Traditional Ames tests for chemical mutagenicity are slow, costly, and often yield inconsistent results between in vitro and in vivo assays, hindering high-throughput safety screening. To address these limitations, we propose AMPred-LWN, a multimodal multi-granularity model that fuses atomic-level graphs, functional group sequences, and molecular fingerprints for Ames mutagenicity prediction. Our model integrates enhanced graph neural networks (GIN and GAT) with the Mamba-2 sequence modeling architecture and a novel bidirectional ConBiMamba module that synchronously processes forward and reverse paths to mitigate unidirectional biases to capture multiscale and long-range chemical features efficiently. AMPred-LWN achieves state-of-the-art performance on Ames data set, with AUROC of 0.922 and ACC of 0.852, outperforming baselines and generalizing well to external sets while reducing inference time by over 30%. Interpretability analysis shows that our model highlights mutagenic substructures and recognizes features of non-mutagenic molecules like polyhydroxylation, offering valuable structure-activity insights.