Multimodal Multi-Granularity Fusion Model with Mamba Architecture for Ames Mutagenicity Prediction.
Journal:
Journal of medicinal chemistry
Published Date:
Jan 5, 2026
Abstract
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.
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