Theoretical Studies of QSAR and Molecular Design on a Novel Series of Ethynyl‐3‐Quinolinecarbonitriles as Src Inhibitors

Fang, Dan Qing; Wu, Wen Juan; Zhang, Rong; Zeng, Guo Hua; Zheng, Kang

doi:10.1111/j.1747-0285.2012.01385.x

Cited by 9 publications

(4 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the QSAR field, the term "local" is used to designate models based on a data set consisting of compounds related by their chemical structure, unlike global models, that are based on data sets consisting of structurally diverse chemical substances [43]. Another paper reported on the use of 2D-QSAR for c-src inhibitors, but these models were also local, focused on ethynyl-3-quinolinecarbonitriles [44]. Therefore, our study is the first one focused on global QSAR models for inhibitors targeting the c-src tyrosine kinase.…”

Section: Discussionmentioning

confidence: 99%

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase

Ancuceanu

Tamba

Stoicescu³

et al. 2019

IJMS

View full text Add to dashboard Cite

A prototype of a family of at least nine members, cellular Src tyrosine kinase is a therapeutically interesting target because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We explored the use of global quantitative structure-activity relationship (QSAR) models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a dataset of 1038 compounds from ChEMBL database, we developed over 350 QSAR classification models. A total of 49 models with reasonably good performance were selected and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over 100,000 compounds. A total of 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using AutoDock Vina and LeDock, and 89 were predicted to be active based on the energy of binding.

show abstract

Section: Discussionmentioning

confidence: 99%

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase

Ancuceanu

Tamba

Stoicescu³

et al. 2019

IJMS

View full text Add to dashboard Cite

show abstract

“…In the QSAR field, the term "local" is used to designate models based on a data set consisting of compounds related by their chemical structure, unlike global models, that are based on data sets consisting of structurally diverse chemical substances (85). An additional paper reported on the use of 2D-QSAR for c-src inhibitors, but these models were also local, focused on ethynyl-3-quinolinecarbonitriles (86).Therefore, our study is the first one focused on global QSAR models for inhibitors targeting the c-src tyrosine kinase. It has been argued (and it stands to reason) that local models tend to have limited predictive power, even when their apparent performance indicates that they are robust (85).…”

Section: Discussionmentioning

confidence: 99%

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Ancuceanu

Tamba

Stoicescu³

et al. 2019

Preprint

View full text Add to dashboard Cite

Prototype of a family of at least nine members, c-src tyrosine kinase is a therapeutically interesting target, because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We have explored the use of global QSAR models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a data set of 1038 compounds from ChEMBL and 19 blocks of molecular descriptors, we have developed over 200 QSAR classification models, based on six machine learning algorithms and 17 feature selection methods. We have selected 49 with reasonably good performance (positive predictive value and balanced accuracy higher than 70% in nested cross validation) and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over the “named” ZINC data set (over 100,000 compounds). 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using Vina and Ledock, and a number of 90 were predicted to be active based on the binding energy. External data from CHEMBL and PUBCHEM confirmed that at least 7.83% (in the case of QSAR) or 6.67% (in the case of integrated QSAR and molecular docking) of the compounds are active on the c-src target.

show abstract

“…Another combined method based on density functional theory (DFT), molecular mechanics, and statistics as well as the comparative molecular field analysis was applied to develop 2D‐ and 3D‐QSAR models for a novel series of ethynyl‐3‐ quinolinecarbonitriles acting as Src kinase inhibitors . The leave‐one‐out cross‐validation q 2 values of 2D‐QSAR and CoMFA models resulted 0.834 and 0.812, respectively.…”

Section: D‐qsarmentioning

confidence: 99%

Molecular interaction fields in drug discovery: recent advances and future perspectives

Artese

Cross

Costa

et al. 2013

WIREs Comput Mol Sci

View full text Add to dashboard Cite

Drug discovery is a highly complex and costly process, and in recent years, the pharmaceutical industry has shifted from traditional to genomics‐ and proteomics‐based drug research strategies. The identification of druggable target sites, promising hits, and high quality leads are crucial steps in the early stages of drug discovery projects. Pharmacokinetic (PK) and drug metabolism profiling to optimize bioavailability, clearance, and toxicity are increasingly important areas to prevent costly failures in preclinical and clinical studies. The integration of a wide variety of technologies and expertise in multidisciplinary research teams combining synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds to pass these hurdles is now commonplace, although there remain challenging areas. Molecular interaction fields (MIFs) are widely used in a range of applications to support the discovery teams, characterizing molecules according to their favorable interaction sites and therefore enabling predictions to be made about how molecules might interact. The utility of MIF‐based in silico approaches in drug design is extremely broad, including approaches to support experimental design in hit‐finding, lead‐optimization, physicochemical property prediction and PK modeling, drug metabolism prediction, and toxicity. WIREs Comput Mol Sci 2013, 3:594–613. doi: 10.1002/wcms.1150 This article is categorized under: Computer and Information Science > Chemoinformatics

show abstract

Theoretical Studies of QSAR and Molecular Design on a Novel Series of Ethynyl‐3‐Quinolinecarbonitriles as Src Inhibitors

Cited by 9 publications

References 44 publications

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Molecular interaction fields in drug discovery: recent advances and future perspectives

Contact Info

Product

Resources

About