Machine learning-based QSAR models to predict sodium ion channel (Nav 1.5) blockers

Abstract

Aim: Conventional experimental approaches used for the evaluation of the proarrhythmic potential of compounds in the drug discovery process are expensive and time consuming but an integral element in the safety profile required for a new drug to be approved. The voltage-gated sodium ion channel 1.5 (Nav 1.5), a target known for arrhythmic drugs, causes adverse cardiac complications when the channel is blocked. Results: Machine learning classification and regression models were built to predict the possibility of blocking these channels by small molecules. The finalized models tested with balanced accuracies of 0.88, 0.93 and 0.94 at three thresholds (1, 10 and 30 µmol, respectively). The regression model built to predict the pIC50 of compounds had q2 of 0.84 (root-mean-square error = 0.46). Conclusion: The machine learning models that have been built can act as effective filters to screen out the potentially toxic compounds in the early stages of drug discovery.