Comparison Data Sets for Benchmarking QSAR Methodologies in Lead Optimization

Mittal, Ruchi; McKinnon, Ross A.; Sorich, Michael J.

doi:10.1021/ci900117m

Cited by 24 publications

(18 citation statements)

References 57 publications

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“…There are several possible mutual confirmations in which binding may occur. These are commonly called binding modes [11]. ChemSketch developed by advanced chemistry development, inc., (http;//www.acdlabs.com) was used to construct structure of rutin.…”

Section: Preparation Of Ligand Structurementioning

confidence: 99%

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<i>In Silico </i>Molecular Docking Studies of Rutin Compound against Apoptotic Proteins (Tumor Necrosis Factor, Caspase-3, NF-Kappa-B, P53, Collagenase, Nitric Oxide Synthase and Cytochrome C)

Jayameena¹,

Sivakumari²,

Ashok³

et al. 2018

JCRT

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Rutin as a flavonoid compound contains many flavonoids having antitumor properties. Therefore, the present study was aimed to dock rutin compound with apoptotic proteins like TNF, Caspase-3, NF-Kappa-B, P53, Collagenase, Nitric Oxide Synthase and Cytohrome C by AutoDock software. The docking scores were highest in Nitric oxide synthase (-3.68 kcal/mol) followed by Tumor Necrosis Factor (-3.22 kcal/mol), Caspase-3 (-2.95 kcal/mol), Collagenase (-2.47 kcal/mol), Cytochrome C (-2.31 kcal/mol), NF-kappa-B (-1.8 kcal/mol) and P53 (-0.32 kcal/mol). The Log P value and lower hydrogen bond counts, confirming the ability of rutin compound for binding at the active sites of the receptor was determined by the in silico method. The potential drug candidate can further be validated by wet lab studies for its proper function.

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Section: Preparation Of Ligand Structurementioning

confidence: 99%

“…The goal of ligand-protein docking is to predict the predominant binding model(s) of a ligand with a protein of known three dimensional structures [34]. Ligand binding is the key step in enzymatic reactions and, thus, for their inhibition.…”

Section: Details Of Docking Interactionmentioning

confidence: 99%

<i>In Silico </i>Molecular Docking Studies of Rutin Compound against Apoptotic Proteins (Tumor Necrosis Factor, Caspase-3, NF-Kappa-B, P53, Collagenase, Nitric Oxide Synthase and Cytochrome C)

Jayameena¹,

Sivakumari²,

Ashok³

et al. 2018

JCRT

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“…Interaction Studies: The goal of ligand-protein docking is to predict the predominant binding model(s) of a ligand with a protein of known three dimensional structure 29 . To study the binding mode of canthin-6-one in the binding site of epidermal growth factor receptor (EGFR) tyrosine kinase (target enzyme), docking studies was performed and energy values were calculated from the docked conformations of the protein-inhibitor complexes.…”

Section: Fig 3: Active Sites Cavity Of Epidermal Growth Factor Recepmentioning

confidence: 99%

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2017

IJPSR

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“…23 From this collection, we studied the data sets that contained more than 100 compounds ( Table 2). We refer to these data sets as congeneric.…”

Section: Journal Of Chemical Information and Modelingmentioning

confidence: 99%

Assessment of Machine Learning Reliability Methods for Quantifying the Applicability Domain of QSAR Regression Models

Toplak

Močnik

Polajnar

et al. 2014

J. Chem. Inf. Model.

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The vastness of chemical space and the relatively small coverage by experimental data recording molecular properties require us to identify subspaces, or domains, for which we can confidently apply QSAR models. The prediction of QSAR models in these domains is reliable, and potential subsequent investigations of such compounds would find that the predictions closely match the experimental values. Standard approaches in QSAR assume that predictions are more reliable for compounds that are "similar" to those in subspaces with denser experimental data. Here, we report on a study of an alternative set of techniques recently proposed in the machine learning community. These methods quantify prediction confidence through estimation of the prediction error at the point of interest. Our study includes 20 public QSAR data sets with continuous response and assesses the quality of 10 reliability scoring methods by observing their correlation with prediction error. We show that these new alternative approaches can outperform standard reliability scores that rely only on similarity to compounds in the training set. The results also indicate that the quality of reliability scoring methods is sensitive to data set characteristics and to the regression method used in QSAR. We demonstrate that at the cost of increased computational complexity these dependencies can be leveraged by integration of scores from various reliability estimation approaches. The reliability estimation techniques described in this paper have been implemented in an open source add-on package (https:// bitbucket.org/biolab/orange-reliability) to the Orange data mining suite.

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Comparison Data Sets for Benchmarking QSAR Methodologies in Lead Optimization

Cited by 24 publications

References 57 publications

<i>In Silico </i>Molecular Docking Studies of Rutin Compound against Apoptotic Proteins (Tumor Necrosis Factor, Caspase-3, NF-Kappa-B, P53, Collagenase, Nitric Oxide Synthase and Cytochrome C)

<i>In Silico </i>Molecular Docking Studies of Rutin Compound against Apoptotic Proteins (Tumor Necrosis Factor, Caspase-3, NF-Kappa-B, P53, Collagenase, Nitric Oxide Synthase and Cytochrome C)

Untitled

Assessment of Machine Learning Reliability Methods for Quantifying the Applicability Domain of QSAR Regression Models

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