SMVSNN: An Intelligent Framework for Anticancer Drug–Drug Interaction Prediction Utilizing Spiking Multi-view Siamese Neural Networks

The study of synergistic drug combinations is vital in cancer treatment, enhancing efficacy, reducing resistance, and minimizing side effects through complementary drug actions. Drug-drug interaction (DDI) analysis offers essential theoretical support, and with the rise of data science, intelligent algorithms are increasingly replacing traditional in vitro screening for predicting potential DDIs. Considering the limitations of previous computational methods, such as the application of a single view, overly direct concatenation of drug pair features, and existing data encoding that is difficult to handle, this paper proposes a novel DDI analysis and prediction framework, called the Spiking Multi-View Siamese Neural Network-based (SMVSNN) framework. First, the data of two drugs in each view are processed into fused features using a Siamese spiking convolutional network and a spiking neural perceptron. Second, the processed features from multiple views are integrated into a unified representation through a self-learning attention weight module. Finally, this unified representation is fed into a spiking multilayer perceptron network to obtain the prediction results. Compared to traditional intelligent algorithms, the spiking neurons and the siamese network in SMVSNN can more effectively extract and integrate latent information from drug pair data. Real anticancer drug data, including 904 drugs, 7730 DDI records, and 19 drug interactions, were extracted from authoritative public databases to assess the effectiveness of our framework. The 5-fold cross-validation indicates that SMVSNN outperforms previous models on the majority of metrics. SMVSNN is poised to be an effective method for inferring potential synergistic drug combinations in anticancer therapy.