DPASyn: Mechanism-Aware Drug Synergy Prediction via Dual Attention and Precision-Aware Quantization
This work addresses the challenge of scalable and accurate drug synergy prediction for cancer therapy, representing a domain-specific advancement with incremental improvements in method efficiency.
The paper tackles the problem of predicting drug synergy for cancer therapy by proposing DPASyn, a framework that uses dual-attention mechanisms and precision-aware quantization to model drug-drug interactions more effectively while reducing computational costs, achieving state-of-the-art performance on the O'Neil dataset with 13,243 combinations and enabling full-batch processing of up to 256 graphs on a single GPU.
Drug combinations are essential in cancer therapy, leveraging synergistic drug-drug interactions (DDI) to enhance efficacy and combat resistance. However, the vast combinatorial space makes experimental screening impractical, and existing computational models struggle to capture the complex, bidirectional nature of DDIs, often relying on independent drug encoding or simplistic fusion strategies that miss fine-grained inter-molecular dynamics. Moreover, state-of-the-art graph-based approaches suffer from high computational costs, limiting scalability for real-world drug discovery. To address this, we propose DPASyn, a novel drug synergy prediction framework featuring a dual-attention mechanism and Precision-Aware Quantization (PAQ). The dual-attention architecture jointly models intra-drug structures and inter-drug interactions via shared projections and cross-drug attention, enabling fine-grained, biologically plausible synergy modeling. While this enhanced expressiveness brings increased computational resource consumption, our proposed PAQ strategy complements it by dynamically optimizing numerical precision during training based on feature sensitivity-reducing memory usage by 40% and accelerating training threefold without sacrificing accuracy. With LayerNorm-stabilized residual connections for training stability, DPASyn outperforms seven state-of-the-art methods on the O'Neil dataset (13,243 combinations) and supports full-batch processing of up to 256 graphs on a single GPU, setting a new standard for efficient and expressive drug synergy prediction.