Combination drug therapies hold significant promise for treating complex diseases such as cancer, as they can target multiple pathways, overcome resistance, and reduce toxicity compared to monotherapies. Artificial intelligence offers powerful tools for predicting synergistic combinations; however, current approaches typically rely on a single drug descriptor, usually chemical structure, which insufficiently captures the complex biology of drug action individually and in combination. While additional modalities could help, missingness in descriptor coverage poses a fundamental challenge for multimodal integration.
To address these limitations, we propose a metric-agnostic, missing-aware, attention-based deep learning architecture. that integrates pharmacologically and biologically informed drug representations including bioassay activity profiles, structural descriptors, morphological profiles, and genetic signatures alongside cell line gene expression profiles. The model simultaneously predicts four established synergy scoring frameworks (ZIP, Loewe, Bliss, HSA), while dynamically attending to whichever modalities are present per sample, enabling robust learning under severe missingness.
Relative to the single-modality structural baseline (R² = 0.73, MSE = 120.81), our full multimodal integration improved R² by 9.78% and reduced MSE by 7.1% for synergistic samples. These gains were consistent across five cross-validation folds and statistically significant under a paired t-test (p = 0.034) and exceeded those of state-of-the-art synergy baselines.
To move beyond predictive accuracy toward interpretable biological insight, we apply SHAP-IQ interaction analysis to quantify how drug descriptors and gene expression features jointly influence predictions, revealing cross-modal interactions that highlight both drug–drug and drug mechanism–gene pairs driving synergy, and ultimately uncovering why specific combinations are synergistic in cellular contexts.

Co-authors: Bryan Lye, Muhammad Javad Heydari, Thomas Marsland, James Mckenna, Fatemeh Vafaee