Predicting Cell Line-Specific Synergistic Drug Combinations Through Siamese Network with Attention Mechanism
摘要
Drug combination therapy holds great promise for curing cancer patients, as it can significantly enhance treatment effectiveness and reduce drug toxicity. However, efficiently identifying synergistic drug combinations from a vast pool of potential drug combinations remains challenging, mainly due to the expensive and time-consuming nature of traditional experimental methods. In this work, we introduce a novel deep learning method called SNAADC for discovering synergistic drug combinations. In SNAADC, we employ Siamese Network and attention mechanism to extract more effective drug features for better performance. The experimental results demonstrate that SNAADC outperforms traditional and popular methods.