SMFP: A self-supervised multimodal molecular feature fusion method with bidirectional cross-attention for molecular property prediction.

Mol-ME: Enhancing Molecular Property Prediction via Multi-Modal Alignment Learning and Ensemble Methods.

A novel multimodal alignment framework for joint modeling of molecular graphs and sequences, called Mol-ME, which employs ensemble learning to predict on extracted representations, which captures complex nonlinear relationships and compensates for the modeling limitations of single shallow networks.

GSSCMI: Efficient Co-Attention and Multimodal Contrastive Learning for Enhanced circRNA-miRNA Interaction Prediction.

GSSCMI is innovatively design an efficient co-attention mechanism that simultaneously modulates fused modalities through unified and fine-grained attention scores, achieving balanced fusion while significantly reducing computational overhead.

HyperMolFusion: A Hypergraph-Enhanced Multi-Modal Fusion Framework for Accurate Molecular Property Prediction.

By holistically integrating atomic, motif, and global fingerprint information via hypergraph modeling, HyperMolFusion offers a more reliable computational tool to enhance the efficiency and accuracy of drug development pipelines.

MG-CMIF: A Multi-Granularity Enhanced Cross-Modal Information Fusion Framework for Molecular Property Prediction

MG-CMIF is developed, a multi-granularity cross-modal framework for molecular property prediction that achieves better prediction results than competitive methods in both classification and regression settings and confirms that hierarchical structural modeling and cross-view integration both contribute to the effective...

CrossMol: Cross-Modal Mask-Predict Pre-training For 3D Molecular Data.

MOTIVATION Self-supervised pre-training models for molecular data have demonstrated notable results across many downstream tasks. The inherent multimodal properties of molecules have also motivated efforts to capture information from different modalities. However, current multimodal molecular pre-training models usuall...

Umamifusion: attention-enhanced multimodal fusion of graph and sequence features for umami peptide identification.