Prediction of the inhibitory concentrations of chloroquine derivatives using deep neural networks models

In recent years, deep neural networks have begun to receive much attention, which has obvious advantages in feature extraction and modeling. However, in the using of deep neural networks for the QSAR modeling process, the selection of various parameters (number of neurons, hidden layers, transfer functions, data set partitioning, number of iterations, etc.) becomes difficult. Thus, we proposed a new and easy method for optimizing the model and selecting Deep Neural Networks (DNN) parameters through uniform design ideas and orthogonal design methods. By using this approach, 222 chloroquine (CQ) derivatives with half maximal inhibitory concentration values reported in different kinds of literature were selected to establish DNN models and a total number of 128,000 DNN models were built to determine the optimized parameters for selecting the better models. Comparing with linear and Artificial Neural Network (ANN) models, we found that DNN models showed better performance. Communicated by Ramaswamy H. Sarma

Automated summary

A new method for optimizing model and selecting parameters for Deep Neural Networks (DNN) was proposed, and it was found that DNN models showed better performance compared with linear and Artificial Neural Network (ANN) models in QSAR modeling.