DiffPro: Decoupled Generative Prior with Diffusion Models for Efficient Few-Shot Drug Synergy Prediction

Abstract

Predicting drug synergy is essential for optimizing combination therapies in cancer treatment. Under extreme data scarcity, existing computational methods struggle to generalize to new cell lines. Although meta-learning approaches have shown promise, a critical limitation lies in their reliance on a unimodal Gaussian prior for task representation. In extreme few-shot settings, this assumption can overly pull the task posterior toward the prior mean, reducing the discriminability of task representations and pushing the model toward a generic mean-value predictor. To overcome this, we propose DiffPro, a novel framework that integrates a Latent Diffusion Model (LDM) as a structural prior. Unlike Gaussian-based methods, our diffusion module learns a flexible, data-driven prior that can model highly complex task distributions. This learned prior better reflects task heterogeneity in few-shot drug synergy prediction under extreme data scarcity, yielding more discriminative task representations. Comprehensive experiments on the DrugComb dataset demonstrate that DiffPro achieves a significant improvement in performance. Notably, in the challenging 5-shot setting, it outperforms the best-performing baseline, delivering approximately a 17.3% relative gain in R2 (0.264 vs. 0.225) and a 4.6% relative reduction in MSE. These results confirm that the diffusion prior successfully regularizes the latent space, mitigating task representation collapse and enabling robust, high-precision predictions for combination therapy design.