Data for: RANKING STRATEGIES TO SUPPORT TOXICITY PREDICTION: A CASE STUDY ON POTENTIAL LXR BINDERS

Published: 13 April 2019| Version 1 | DOI: 10.17632/wg5hmgjhh5.1
Contributors:
Anna Palczewska,
Daniel Neagu,
Arianna Bassan,
Simona Kovarich,
Elena Fioravanzo,
Andrea Ciacci

Description

A dataset of 356 compounds, mainly drugs or drug candidates, which consisted of groups of congeneric series sharing a common scaffold. The collected “LXR binders” covered a wide range of binding affinity, with IC50 values spanning from 1 nM to greater than 10000 nM. The dataset of LXR binders was enriched with decoy molecules, i.e. molecules that are presumed to be inactive against a target (they will not likely bind to the target). Decoys are commonly used to validate the performance of molecular modelling studies, as for example molecular docking, which was used in the present work. One-thousand decoy molecules were selected from Schrodinger 1K Drug-Like Ligand Decoys Set. For these molecules results obtained from the following modelling approaches are reported: ensemble docking, ePharmacophore, fingerprint similarity, structural alerts and QSAR PLS model. These results were used to build ranking strategies proposed in the paper.

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Categories

Molecular Modeling, Liver, Receptor Binding

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