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Kim2022 - BayeshERG: A deep learning model for predicting hERG channel blockers

ABSTRACT: BayeshERG is a predictor of small molecule-induced blockade of the hERG ion channel. To increase its predictive power, the authors pretrained a bayesian graph neural network with 300,000 molecules as a transfer learning exercise. The pretraining set was obtained from Du et al, 2015, and the fine tuning dataset is a collection of 14,322 molecules from public databases (8488 positives and 5834 negatives). The model was validated on external datasets and experimentally, from 12 selected compounds (>0.95 probability) one candidate showed strong hERG inhibition (IC 50 Model Type: Predictive machine learning model. Model Relevance: Prediction of hERG channel blockade probability br> Model Encoded by: Azycn (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos4tcc

SUBMITTER: Zainab Ashimiyu-Abdusalam  

PROVIDER: MODEL2408060001 | BioModels | 2024-08-06

REPOSITORIES: BioModels

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BayeshERG: a robust, reliable and interpretable deep learning model for predicting hERG channel blockers.

Kim Hyunho H   Park Minsu M   Lee Ingoo I   Nam Hojung H  

Briefings in bioinformatics 20220701 4

Unintended inhibition of the human ether-à-go-go-related gene (hERG) ion channel by small molecules leads to severe cardiotoxicity. Thus, hERG channel blockage is a significant concern in the development of new drugs. Several computational models have been developed to predict hERG channel blockage, including deep learning models; however, they lack robustness, reliability and interpretability. Here, we developed a graph-based Bayesian deep learning model for hERG channel blocker prediction, nam  ...[more]

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