Data for: hERG Liability Classification Models Using Machine Learning Techniques

Name: Data for: hERG Liability Classification Models Using Machine Learning Techniques
Creator: Rajendra Kristam
Published: 2019-05-13T17:03:38.788Z
Keywords: Chemoinformatics, Quantitative Structure-Activity Relationship, Toxicity, Cardiotoxicity, Long QT Syndrome, Computational Chemistry, Modelling

Kristam, Rajendra; S, Keerthi Praba; Konda, Leela Sarath Kumar

doi:10.17632/32pdx7y72x.1

Data for: hERG Liability Classification Models Using Machine Learning Techniques

Published: 13 May 2019| Version 1 | DOI: 10.17632/32pdx7y72x.1

Contributors:

Rajendra Kristam, Keerthi Praba S, Leela Sarath Kumar Konda

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This file pertains 1) all SMILES(except the evaluation set-3 which contains the compound data from in-house proprietary projects) with respective pIC50 values that were used in training and evaluating the models 2) list of descriptors that were used to build models

Data for: hERG Liability Classification Models Using Machine Learning Techniques

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