Multimodal Cross-Attention Molecular Property Prediction for Text, Sequence, Graph, and Geometry

The use of single-modal molecular representations limits the accuracy of standard Quantitative Structure-Property Relationship (QSPR) models, which are essential for speeding up drug discovery and material design. We address this by introducing the multimodal cross-attention molecular property prediction (MCMPP) model, which integrates SMILES, ECFP fingerprints, molecular graphs, and 3D molecular conformations through a cross-attention mechanism after being independently processed by Transformer-Encoder, BiLSTM, GCN, and reduced Unimol+. Tests on four data sets (Delaney, Lipophilicity, SAMPL, and BACE) demonstrate how MCMPP improves prediction accuracy by using complementary effects across modalities. According to experimental data, MCMPP works better than other fusion procedures, obtaining the greatest Pearson correlation coefficient and demonstrating its effectiveness as a material design and drug discovery tool.