Key result
The directed message passing neural network with moe206 descriptors achieved an AUC-ROC of 0.956 ± 0.005 under random split for identifying hERG blockers.
Why the study?
Methods for predicting hERG channel blockers are actively pursued.
Effect estimate: AUC-ROC 0.956 ± 0.005
The D-MPNN + moe206 model demonstrates high accuracy in predicting hERG channel blockers, offering a valuable computational tool for drug discovery and cardiac safety screening.
methods for predicting hERG channel blockers are actively pursued. In the present study, the directed message passing neural network (D-MPNN) was applied to construct classification models for identifying hERG blockers based on diverse datasets. Several descriptors and fingerprints were tested along with the D-MPNN model. Among all these combinations, D-MPNN with the moe206 descriptors generated from MOE (D-MPNN + moe206) showed significantly improved performances. The AUC-ROC values of the D-MPNN + moe206 model reached 0.956 ± 0.005 under random split and 0.922 ± 0.015 under scaffold split on Cai's hERG dataset, respectively. Moreover, the comparisons between our models and several recently reported machine learning models were made based on various datasets. Our results indicated that the D-MPNN + moe206 model is among the best classification models. Overall, the excellent performance of the DMPNN + moe206 model achieved in this study highlights its potential application in the discovery of novel and effective hERG blockers.
No takes yet. Share an insight, caveat, or question.
Shan et al. (2022) studied hERG channel blockers. Directed message passing neural network (D-MPNN) with moe206 descriptors vs. Other machine learning models was evaluated on AUC-ROC for identifying hERG blockers (AUC-ROC 0.956 ± 0.005). The directed message passing neural network with moe206 descriptors achieved an AUC-ROC of 0.956 ± 0.005 under random split for identifying hERG blockers.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: