Key result
Analysis of the ChEMBL database probed the correlation between hERG binding and functional assays and trained classification models based on structural and physicochemical properties.
Provides an open-source computational analysis and classification models for hERG active and inactive compounds in the ChEMBL database.
Open hERG models may aid early cardiotoxicity screening; leaves open prospective validation before clinical adoption.
A detailed analysis of the hERG content inside the ChEMBL database is performed. The correlation between the outcome from binding assays and functional assays is probed. On the basis of descriptor distributions, design paradigms with respect to structural and physicochemical properties of hERG active and hERG inactive compounds are challenged. Finally, classification models with different data sets are trained. All source code is provided, which is based on the Python open source packages RDKit and scikit-learn to enable the community to rerun the experiments. The code is stored on github ( https://github.com/pzc/herg_chembl_jcim).
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Paul Czodrowski (2013) studied hERG active and inactive compounds. Analysis of the ChEMBL database probed the correlation between hERG binding and functional assays and trained classification models based on structural and physicochemical properties.
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