Prediction and optimization of NaV1.7 inhibitors based on machine learning methods

TL;DR

This paper applies machine learning to predict NaV1.7 inhibitors, identifying promising compounds and validating them experimentally, though it acknowledges limitations and the need for further research.

Contribution

Introduces the RF-CDK machine learning model for predicting NaV1.7 inhibitors and demonstrates its application in drug screening and experimental validation.

Findings

RF-CDK outperformed other models on imbalanced data

Identified effective compound K1 for NaV1.7 inhibition

Experimental validation confirmed activity of K1

Abstract

We used machine learning methods to predict NaV1.7 inhibitors and found the model RF-CDK that performed best on the imbalanced dataset. Using the RF-CDK model for screening drugs, we got effective compounds K1. We use the cell patch clamp method to verify K1. However, because the model evaluation method in this article is not comprehensive enough, there is still a lot of research work to be performed, such as comparison with other existing methods. The target protein has multiple active sites and requires our further research. We need more detailed models to consider this biological process and compare it with the current results, which is an error in this article. So we want to withdraw this article.