Data for: hERG Liability Classification Models Using Machine Learning Techniques

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 models2) list of descriptors that were used to build model

Data for: hERG Liability Classification Models Using Machine Learning Techniques

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 models2) list of descriptors that were used to build modelsTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Identification of structural requirements of estrogen receptor modulators using pharmacoinformatics techniques for application to estrogen therapy

An attempt was made in the present study to explore the structural requirements of known estrogen receptor (ER) modulators for biological activity using pharmacoinformatics approaches to elucidate critical functionalities for new, potent and less toxic chemical agents for successful application in estrogen therapy. For this purpose a group of non-steroidal ligands, 7-thiabicyclo[2.2.1]hept-2-ene-7- oxide derivatives were collected from the literature to perform quantitative structure-activity relationship (QSAR), pharmacophore and molecular docking studies. The 2D QSAR models (R2 α = 0.857, seα = 0.370, Q2 α = 0.848, R2 pred-α = 0.675, spα = 0.537; R2 β = 0.874, seβ = 0.261, Q2 β = 0.859, R2 pred-β = 0.659, spβ = 0.408) explained that hydrophobicity and molar refractivity were crucial for binding affinity in both α- and β-subtypes. The space modeling study (R2 α = 0.955, seα = 1.311, Q2 α = 0.932, R2 pred-α = 0.737, spα = 0.497; R2 β = 0.885, seβ = 1.328, Q2 β = 0.878, R2 pred-β = 0.769, spβ = 0.336) revealed the importance of HB donor and hydrophobic features for both subtypes, whereas, HB acceptor and aromatic ring were critical for α- and β-subtypes respectively. The functionalities developed in the QSAR and pharmacophore studies were substantiated by molecular docking which provided the preferred orientation of ligands for effective interaction at the active site cavity.MA Islam and TS Pillay were funded by the University of Pretoria Vice Chancellor’s post-doctoral fellowship and National Research Foundation (NRF), South Africa Innovation Post-doctoral fellowship schemes.http://link.springer.com/journal/442017-03-31hb2016Chemical Patholog