DDI-MMAF: Multi-modal affine fusion of visual and semantic representations for anticancer drug synergy prediction

Predicting anticancer drug synergy is pivotal for personalizing combination therapies; however, existing deep learning models often rely heavily on complex, high-dimensional multi-omics data and precomputed molecular properties. Such dependence increases data acquisition barriers and limits model applicability in resource-constrained or rapid screening scenarios. In this study, we propose DDI-MMAF, a lightweight cross-modal framework that avoids using explicit high-dimensional omics profiles as direct model inputs. It utilizes a minimalist input protocol consisting of drug SMILES sequences and verbalized biological context, encompassing cell line names and their corresponding tissue origins. The architecture integrates a domain-specific semantic encoder to extract coarse-grained biomedical semantic priors from cell-line nomenclature and a deep residual visual network to capture hierarchical spatial features from molecular images. The core innovation lies in a multi-modal affine fusion mechanism that dynamically modulates molecular visual features conditioned on biological semantic embeddings. Systematic evaluations demonstrate that despite its simplified inputs, the model achieves a ROC AUC of 0.934 on benchmark datasets, showing the best performance against methods that utilize explicit omics information or handcrafted molecular descriptors. Furthermore, our approach maintains robust performance under the internal scaffold-split setting, while also achieving competitive performance compared with the evaluated baselines on the independent AstraZeneca blind test set. Overall, this research demonstrates that effective semantic-guided modulation enables accurate synergy prediction from raw minimalist inputs, offering a practical and efficient computational solution for cost-effective drug combination discovery.