Before revision:  This study focused on predicting and optimizing inhibitors of NaV1.7 using machine learning methods , and using patch-clamp methods to validate them at the cellular level. Materials and Methods We used Chembl, BindingDB, and data from the literature to establish classification models for inhibitors . The imbalanced data set test2 was used to screen the best-performing model to screen commercial compound libraries, and whole-cell voltage-clamp was used to validate inhibitors. We propose a molecular group optimization method using a combination of Grammer Variational Autoencoder, classification model, and simulated annealing algorithm. Results and Conclusion We get the model RF-CDK that performs best in the imbalanced data set.
After revision:  This study focused on predicting and optimizing inhibitors of NaV1.7 using machine learning methods to predict NaV1.7 inhibitors and found the model RF-CDK that performs best in the imbalanced data set.