Efficacy of Small Molecules Blocking in Kv1.5 Potassium Channel From Machine Learning Models

Abstract

Abstract Atrial fibrillation and associated cardiac problems may be treated with the development of potent potassium ion channel Kv1.5 blockers. Since the use of these blockers provides therapeutic advantages and potential side effects, it is significant to identify Kv1.5 channel blockers from compounds. In this work, we employed optimized machine learning models to predict the potential of small molecules in blocking the Kv1.5 channel to address the limitations of traditional screening methods in the drug discovery process. Several machine learning classifiers and regression models were employed utilizing molecular descriptors and fingerprints incorporating with SMOTE oversampling technique to overcome the class imbalance in active and inactive molecules. The results show that distinct models excelled in predicting different molecular attributes. The regression models demonstrated superior performance with random forest regression (RFR) (root-mean-square error = 0.668) and Substructure-Count-HGBR (Histogram-based Gradient Boosting Regression) having adjusted R² of 39.50% for predicting binding affinity. The best-performing models among the fingerprint-based models were the k-Nearest Neighbors Classifier (KNNC) and Substructure-RFC (Random Forest Classifier), which both demonstrated well-balanced predictive models. The generalized machine learning models for Kv1.5 can help researchers quickly narrow down drug candidates that are toxic or beneficial for treating atrial fibrillation in the early stages of drug discovery.