Addressing model overcomplexity in drug-drug interaction prediction with molecular fingerprints

Accurately predicting interactions between drugs is critical for pharmaceutical research and clinical safety. The literature keeps moving toward increasingly complex architectures, yet gains on standard benchmarks are often small. We use a deliberately simple setup that keeps the classifier fixed and swaps only the molecular representation. We compare ECFP4 Morgan fingerprints (MFPs), pretrained graph convolutional networks (GCNs), and MoLFormer embeddings on common DrugBank DDI splits and on an