Drug resistance in cancer treatment has posed a significant challenge, with cancer cells developing resistance to conventional drugs. A breakthrough in addressing this issue has been the development of polytherapy, a novel approach that combines synergistic drugs to maximize their effectiveness in treating cancer. However, the vast computational space for possible drug combinations has presented challenges. To address this, deep learning algorithms have been developed to predict drug synergy. Despite their effectiveness, these algorithms often lack interpretability and require tuning due to the complexity of parameters and hidden layers involved in their implementation