SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy

Drug combinations can be the prime strategy for increasing the initial treatment options in cancer therapy. However, identifying the combinations through experimental approaches is very laborious and costly. Notably, in vitro and/or in vivo examination of all the possible combinations might not be plausible. This study presented a novel computational approach to predicting synergistic drug combinations. Specifically, the deep neural network-based binary classification was utilized to develop the model. Various physicochemical, genomic, protein-protein interaction and protein-metabolite interaction information were used to predict the synergy effects of the combinations of different drugs. The performance of the constructed model was compared with shallow neural network (SNN), k-nearest neighbors (KNN), random forest (RF), support vector machines (SVMs), and gradient boosting classifiers (GBC). Based on our findings, the proposed deep neural network model was found to be capable of predicting synergistic drug combinations with high accuracy. The prediction accuracy and AUC metrics for this model were 92.21% and 97.32% in tenfold cross-validation. According to the results, the integration of different types of physicochemical and genomics features leads to more accurate prediction of synergy in cancer drugs.

Automated summary

This study presented a novel computational approach using a deep neural network-based binary classification model to predict synergistic drug combinations. Various physicochemical, genomic, protein-protein interaction, and protein-metabolite interaction information were used to predict the synergy effects of different drug combinations. The proposed deep neural network model demonstrated high accuracy in predicting synergistic drug combinations with a prediction accuracy of 92.21% and an AUC of 97.32% in tenfold cross-validation.