One of the most popular computational drug discovery approaches, namely, Quantitative Structure-Activity Relationship (QSAR) modeling, employs statistical or machine learning approaches to establish and validate correlations between computed molecular features and experimentally measured biological activities of molecules . This approach has been widely applied in cancer drug discovery , using methods varying from simple linear to non-linear machine learning methods, such as Neural Network (NN) , Support Vector Machine (SVM) or Random Forest (RF) . Most of the previous efforts covered the attempts to design novel monotherapies. However, recently, Cheng et al. developed a biological network proximity measure using QSAR to predict drug synergy for hypertension and cancer. Other studies have utilized various machine learning techniques for synergy prediction , including deep learning approaches . Preuer et al. trained a deep neural network on a large oncology screen and demonstrated the advantage of deep learning over standard machine learning models such as RFs and SVMs.