Mechanisms of Compound Kushen Injection for the treatment of bladder cancer based on bioinformatics and network pharmacology with experimental validation

Antistroke Network Pharmacological Prediction of Xiaoshuan Tongluo Recipe Based on Drug-Target Interaction Based on Deep Learning

Stroke is a common cerebrovascular disease that threatens human health, and the search for therapeutic drugs is the key to treatment. New drug discovery was driven by many accidental factors in the early stage. With the deepening of research, disease-related target discovery and computer-aided drug design constitute a more rational drug discovery process. The deep learning model was constructed by using recurrent neural network, and then, the classification and prediction of compound-protein interactions were studied. In this study, the network pharmacological prediction of stroke based on deep learning is obtained. (1) In the case of discrete time, a distributed optimization algorithm with finite time convergence is applied. A distributed exact first-order algorithm for the case where the objective function is smooth. On the basis of the DGD algorithm, an additional cumulative correction term is added to correct the error caused by the fixed step size of DGD. Solve multiple optimization problems with equality constraints by using Lagrangian functions. Alternately update the original variable and the dual variable to get the solution of a large global problem. It converges to the optimal solution in an asymptotic or exponential way; that is, the node can reach the optimal solution more accurately when the time tends to infinity. (2) Deep learning, also sometimes called representation learning, has a set of algorithms that can automatically discover the desired classification or detection by feeding it into a machine using raw datasets. Multiple levels of abstraction are abstracted through the use of nonlinear models. This simplifies finding solutions to complex and nonlinear functions. Based on the automatic learning function, it provides the functions of modularization and transfer learning. Deep architectures, which usually contain hidden layers, differ from traditional machine learning, which requires a large amount of data to train the network. There are many levels of modules that are nonlinear and transform the information present on the first level into higher levels which are more abstract in nature and are basically used for feature extraction and transformation. (3) The accuracy rate of the framework based on the multitask deep learning algorithm is 91.73%, and the recall rate reaches 96.13%. The final model was predicted and analyzed using real sample data. In the inference problem, it has the advantages of fast training and low cost; in the generation problem, it also has the advantages of fast training, high stability, high diversity, and high quality of image reconstruction.