ToxTree: descriptor-based machine learning models for both hERG and Nav1.5 cardiotoxicity liability predictions
This addresses the problem of costly drug withdrawals due to cardiotoxicity for pharmaceutical researchers, though it is incremental as it applies existing machine learning methods to a specific domain.
The paper tackled predicting cardiotoxicity from hERG and Nav1.5 channel blockers in drug development by introducing two QSAR models, with the hERG model outperforming state-of-the-art tools and the Nav1.5 model achieving 74.9% Q4 and 86.7% Q2 accuracy on an external test set.
Drug-mediated blockade of the voltage-gated potassium channel(hERG) and the voltage-gated sodium channel (Nav1.5) can lead to severe cardiovascular complications. This rising concern has been reflected in the drug development arena, as the frequent emergence of cardiotoxicity from many approved drugs led to either discontinuing their use or, in some cases, their withdrawal from the market. Predicting potential hERG and Nav1.5 blockers at the outset of the drug discovery process can resolve this problem and can, therefore, decrease the time and expensive cost of developing safe drugs. One fast and cost-effective approach is to use in silico predictive methods to weed out potential hERG and Nav1.5 blockers at the early stages of drug development. Here, we introduce two robust 2D descriptor-based QSAR predictive models for both hERG and Nav1.5 liability predictions. The machine learning models were trained for both regression, predicting the potency value of a drug, and multiclass classification at three different potency cut-offs (i.e. 1$μ$M, 10$μ$M, and 30$μ$M), where ToxTree-hERG Classifier, a pipeline of Random Forest models, was trained on a large curated dataset of 8380 unique molecular compounds. Whereas ToxTree-Nav1.5 Classifier, a pipeline of kernelized SVM models, was trained on a large manually curated set of 1550 unique compounds retrieved from both ChEMBL and PubChem publicly available bioactivity databases. The proposed hERG inducer outperformed most metrics of the state-of-the-art published model and other existing tools. Additionally, we are introducing the first Nav1.5 liability predictive model achieving a Q4 = 74.9% and a binary classification of Q2 = 86.7% with MCC = 71.2% evaluated on an external test set of 173 unique compounds. The curated datasets used in this project are made publicly available to the research community.