<ul data-eligibleForWebStory="true"><li>TRIDENT is a new framework for molecular representation learning that integrates molecular SMILES, textual descriptions, and taxonomic functional annotations to learn rich molecular representations.</li><li>The framework utilizes a volume-based alignment objective to align tri-modal features globally and introduces a local alignment objective to capture detailed relationships between molecular substructures and their corresponding sub-textual descriptions.</li><li>TRIDENT achieves state-of-the-art performance on 11 downstream tasks, showcasing the benefits of combining multiple modalities for molecular property prediction.</li><li>The article presents a new approach in molecular property prediction that takes into account textual and taxonomic information, leading to improved performance across various tasks.</li></ul>

TRIDENT: Tri-Modal Molecular Representation Learning with Taxonomic Annotations and Local Correspondence

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