Prediction of drug-target interactions via neural tangent kernel extraction feature matrix factorization model

Drug discovery is a complex and lengthy process that often requires years of research and development. Therefore, drug research and development require a lot of investment and resource support, as well as professional knowledge, technology, skills, and other elements. Predicting of drug-target interactions (DTIs) is an important part of drug development. If machine learning is used to predict DTIs, the cost and time of drug development can be significantly reduced. Currently, machine learning methods are widely used to predict DTIs. In this study neighborhood regularized logistic matrix factorization method based on extracted features from a neural tangent kernel (NTK) to predict DTIs. First, the potential feature matrix of drugs and targets is extracted from the NTK model, then the corresponding Laplacian matrix is constructed according to the feature matrix. Next, the Laplacian matrix of the drugs and targets is used as the condition for matrix factorization to obtain two low-dimensional matrices. Finally, the matrix of the predicted DTIs was obtained by multiplying these two low-dimensional matrices. For the four gold standard datasets, the present method is significantly better than the other methods that is compared to, indicating that the automatic feature extraction method using the deep learning model is competitive compared with the manual feature selection method.