QED statistics

Tool stats & trends

Looking to identify usage trends or leading experts?

Protocols

QED specifications

Information


Unique identifier OMICS_27234
Name QED
Alternative name Quantitative Estimation of Drug-likeness
Interface Web user interface
Restrictions to use None
Input data Some chemical compounds.
Input format SDF
Computer skills Basic
Stability Stable
Maintained Yes

Maintainer


  • person_outline Andrew Hopkins

Publication for Quantitative Estimation of Drug-likeness

QED citations

 (18)
library_books

Automatic Chemical Design Using a Data Driven Continuous Representation of Molecules

2018
PMCID: 5833007
PMID: 29532027
DOI: 10.1021/acscentsci.7b00572

[…] m 400 decoding attempts from the latent space points encoded from the same 1000 seed molecules. We compare the water–octanol partition coefficient (logP), the synthetic accessibility score (SAS), and Quantitative Estimation of Drug-likeness (QED), which ranges in value between 0 and 1, with higher values indicating that the molecule is more drug-like. SI3 Figure 2 shows histograms of the propertie […]

call_split

Synthesis, characterization and in vitro antitrypanosomal activities of new carboxamides bearing quinoline moiety

2018
PLoS One
PMCID: 5764481
PMID: 29324817
DOI: 10.1371/journal.pone.0191234
call_split See protocol

[…] The drug-likeness was predicted using online quantitative estimation of drug-likeness tools. The prediction was based on the calculated logP, topological polar surface area (TPSA), hydrogen bond donors (HBD), hydrogen bond acceptor (HBA), number […]

library_books

Application of Generative Autoencoder in De Novo Molecular Design

2017
Mol Inform
PMCID: 5836887
PMID: 29235269
DOI: 10.1002/minf.201700123

[…] RNN with a reinforcement learning method, deep Q‐learning, to train models that can generate molecular structures with desirable property values for cLogP and quantitative estimate of drug‐likeness (QED). Olivecrona et al. proposed a policy based reinforcement learning approach to tune the pretrained RNNs for generating molecules with user defined properties. The method has been successfully appl […]

library_books

Chemometric Evaluation of THz Spectral Similarity for the Selection of Early Drug Candidates

2017
Sci Rep
PMCID: 5674078
PMID: 29109507
DOI: 10.1038/s41598-017-14819-6

[…] and partial least squares regression (PLS). Using PCA, we projected the provided a priori molecular descriptors based on the Lipinski’s Rule of Five and the quantitative estimate of drug-likeliness (QED) onto 2D space, detecting that only two variables were responsible for >95% of the data variance: the melting point and molecular mass. Next, we applied the PCA method to the acquired THz spectra […]

call_split

An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine protein kinase Yes

2017
Sci Rep
PMCID: 5607274
PMID: 28931921
DOI: 10.1038/s41598-017-10275-4
call_split See protocol

[…] e SAR model were selected, followed by a filtering of drug-likeness and diverse selection.G3: Compounds that were physicochemically similar to those of known inhibitors were filtered using a modified QED. A randomized tree model was built on the bases of the concatenated descriptors of known inhibitors, their target kinases, and experimental conditions (concentration of reagents) and was applied t […]

library_books

Molecular de novo design through deep reinforcement learning

2017
J Cheminform
PMCID: 5583141
PMID: 29086083
DOI: 10.1186/s13321-017-0235-x

[…] RNN as well as an RL only approach. They later extend this method to several other tasks including the generation of chemical structures, and optimize toward molecular properties such as cLogP [] and QED drug-likeness []. However, they report that the method is dependent on a reward function incorporating handwritten rules to penalize undesirable types of sequences, and even then can lead to explo […]

Citations

Looking to check out a full list of citations?

QED institution(s)
Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee, UK; Gaia Paolini Ltd, Canterbury, UK; DECS Computational Compound Sciences, Computational Chemistry, AstraZeneca R&D, Mölndal, Sweden
QED funding source(s)
Supported by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement N° 223461 and the Scottish Universities Life Sciences Alliance.

QED reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review QED