Background:Epilepsy remains the most common and chronic disorder demanding longterm
management. The impact of epilepsy disease is a cause of great concern and has resulted in
efforts to develop treatment for epilepsy. It occurs due to an increase in neuronal excitability produced
by changes affecting the voltage-dependent properties of Voltage-gated Sodium Channels
(VGSCs).Materials and Methods:Weka, a popular suite for machine learning techniques, was used on a
dataset comprising 1781 chemical compounds, showing inhibition activity for sodium channel protein
IX alpha subunit. After the analysis of the dataset obtained from ChEMBL, molecular fingerprints
were computed for the molecules by the ChemDes server. Different classifiers available in
the Weka software were explored to find out the algorithm that could be more suitable for the dataset
or produce the highest accuracy for the classification of molecules as active or inactive.Results:In this work, a comprehensive comparison of different classifiers in the Weka suite for the
prediction of active, inactive, and intermediate classes of molecules showing inhibition against
human NaV1.7 protein was made. The prediction accuracy of these classifiers was assessed based
on performance measures, including accuracy, Root Mean Squared Error (RMSE), Receiver Operating
Characteristic (ROC), precision, Mathews Correlation Coefficient (MCC), recall, and Fmeasure.
The comparison of results for model performance demonstrated that the OneR classifier
performed best over others when validated using percentage split, cross-validation, and supplied
test methods. J48 and Bagging also performed equally well in the prediction of different classes
with an MCC value of 1, ROC area equal to 1, and RMSE close to 0.Conclusion:Machine Learning (ML) tools provide a fast, reliable, and cost-effective approach
required to identify or predict inhibitory molecules for the treatment of a disease. This study shows
that the ML methods, particularly OneR, J48, and Bagging have the ability to identify active and
inactive classes of compounds for the human NaV1.7 protein target. Such predictive models may
provide a reliable and time-saving approach that can aid in the design of potential inhibitors for the
treatment of epilepsy disease.