CDCR-Rank: a computational model for predicting drug combination dose response using ranking-based optimization.

Motivation: Predicting synergistic drug combinations for cancer therapies is vital but challenged by interaction complexity and vast combinatorial possibilities. To address this, we propose a novel ranking-based approach for drug synergy prediction. Our method builds upon the pre-trained CDCR model to predict absolute synergy scores and introduces a novel architecture to directly compare a list of combinations. A key innovation is our custom loss function, which combines mean squared error with a ranking loss term to simultaneously learn accurate synergy values and their correct relative ordering. Results: We comprehensively evaluated our model, CDCR-Rank, on the NCI-ALMANAC benchmark under three challenging scenarios, including predicting synergy for novel drug pairs without monotherapy data. CDCR-Rank demonstrated superior performance, consistently outperforming state-of-the-art methods such as comboFM and comboLTR. Ablation studies validated the importance of each component: a one-dimensional convolutional neural network (1D-CNN) for generating task-specific drug representations from SMILES strings, a sinusoidal encoder for modeling non-linear dose-response curves, and the uRank loss for optimizing combination rankings. Our integrated framework achieves significantly improved accuracy and robustness in predicting drug synergy. By reliably prioritizing the most promising combinations for experimental validation, this work has the potential to accelerate the discovery of effective multi-drug cancer therapies. Availability and implementation: CDCR-Rank is implemented in Python and is available at https://github.com/ParvinRazzaghi/CDCR-Rank.