<ul><li>Accurately predicting drug-drug interactions (DDIs) is crucial for pharmaceutical research and clinical safety.</li><li>This study proposes a simpler approach using molecular representations like Morgan fingerprints, graph-based embeddings, and transformer-derived embeddings.</li><li>The combination of these representations achieves competitive performance in DDI prediction tasks.</li><li>The study highlights the importance of dataset curation and progressive complexity scaling in drug interaction prediction models.</li></ul>

Addressing Model Overcomplexity in Drug-Drug Interaction Prediction With Molecular Fingerprints

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