A k-nearest neighbor classification of hERG K+ channel blockers

Abstract

A series of 172 molecular structures that block the hERG K ? channel were used to develop a classification model where, initially, eight types of PaDEL fingerprints were used for k-nearest neighbor model development. A consensus model constructed using Extended-CDK, PubChem and Substructure count fingerprint-based models was found to be a robust predictor of hERG activity. This consensus model demonstrated sensitivity and specificity values of 0.78 and 0.61 for the internal dataset compounds and 0.63 and 0.54 for the external (PubChem) dataset compounds, respectively. This model has identified the highest number of true positives (i.e. 140) from the PubChem dataset so far, as compared to other published models, and can potentially serve as a basis for the prediction of hERG active compounds. Validating this model against FDA-withdrawn substances indicated that it may even be useful for differentiating between mechanisms underlying QT prolongation.

Prediction of hERG K+ channel blockage using deep neural networks

“…The Pubchem set (AID376) was chosen as the external validation set for further validation of the models. This data set was used widely as test set to evaluate the quality of models by many published papers (Bo‐Han et al, ; Chavan et al, ; Q. Li et al, ; Su et al, ; Wang et al, ). The reason why we chose this data set was to compare the performance of the optimal model M15 with other models on the same data set.…”

“…Furthermore, the optimal model M15 was compared to some models from previous literature (Bo‐Han et al, ; Chavan et al, ; Q. Li et al, ; Su et al, ; Wang et al, ) with the external validation set. Due to the time issue and data duplication, the total number of molecules might be slightly different for models from different papers, but the vast majority of the data was still the same.…”

“…Therefore, it is essential to ensure that the drug would not cause blockade of the hERG function before entering into the market. According to the guideline from the International Conference of Harmonization, all new drugs should be tested for cardiac safety to protect the patients (Chavan, Abdelaziz, Wiklander, & Nicholls, ). Nowadays, many in vitro assays have been developed to evaluate hERG affinity, including radio ligand binding assays, electrophysiology measurements, and rubidium‐flux assays (X. Li, Zhang, Li, & Zhao, ).…”

“…In this study, data set of hERG channel activity was collected from the literature (Chavan et al, 2016;Li, Jørgensen, Oprea, Brunak, & Taboureau, 2008) and the Pubchem database (AID376) (Kim et al, 2016) was set as the external validation set. Different combinations of network architectures, descriptors, and sampling methods were compared to obtain a well-behaved predictive model.…”

“…Random forest (RF) (Polishchuk et al, 2009), which is the "gold standard" in QSAR researches, was also included in this study for comparison. The DNN models with three hidden layers were selected as the final strategy, which not only performed well on the test set, but also showed better performance on statistical parameters than other published models (Bo-Han et al, 2010;Chavan et al, 2016;Li et al, 2008;Su et al, 2012;Wang et al, 2012) on external Pubchem validation set.…”