Kernel-based prediction of a synergistic drug combination
Jun ZHANG, Rui Yuan, Shilong Chen, YongCui WANG · Scientia Sinica Vitae · 2023
Complex diseases, such as cancer, often exhibit resistance to single drug therapy because of their heterogeneity and complex metabolic pathways. Combination therapy is an efficient strategy to overcome drug resistance. As experimental screenings consume considerable resources and have low efficacy, the computational method is a good alternative. Thus, this article proposes a new method for computing features of a drug-drug-cell line (DDC) combination based on similarity, where the S-kernel and Gaussian-kernel methods are used to calculate the drug-drug combination similarity and cell line similarity, respectively. The final feature vector for machine learning input was obtained by concatenating these two vectors. The output for machine learning was based on the experimental results of the synergistic drug combination. Cross validation was performed on three machine learning algorithms, including the random forest, support vector machine, and deep neural network models. The results suggested that the novel method had a robust performance with an area under the curve value of 0.89–0.91. Importantly, the model predicted the novel DDC combinations with new drugs or new cell lines based on unique input features. In conclusion, this novel method improved predictive performance and provided a new strategy for predicting synergistic drug combinations.