ArticleACS omega2025
Multimodal Cross-Attention Molecular Property Prediction for Text, Sequence, Graph, and Geometry.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
Funding
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Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.