Exploring structural requirements of unconventional Knoevenagel-type indole derivatives as anticancer agents through comparative QSAR modeling approaches

Amin, Sk. Abdul; Adhikari, Nilanjan; Jha, Tarun; Gayen, Shovanlal

doi:10.1139/cjc-2016-0050

Cited by 18 publications

(1 citation statement)

References 28 publications

(19 reference statements)

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“…Alignment is playing a crucial role in the development of 3D‐QSAR models . Therefore, the aligned geometry of these compounds, obtained by three different methods, such as pharmacophore‐based, docking‐based as well as the Open3DALIGN‐based methods were used for development of CoMFA, CoMSIA, and Open3DQSAR models.…”

Section: Resultsmentioning

confidence: 99%

Understanding Chemico‐Biological Interactions of Glutamate MMP‐2 Inhibitors through Rigorous Alignment‐Dependent 3D‐QSAR Analyses

et al. 2017

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The current study involves different alignment techniques (pharmacophore‐based, docking‐based and Open3DALIGN‐based) extensively at the first time to build three‐dimensional quantitative structure‐activity relationship (3D‐QSAR) models on some potential glutamate‐based in house MMP‐2 inhibitors. The docking‐based alignment method yields the best 3D‐QSAR models though other alignment‐based methods also provide statistically significant and validated models. 3D‐QSAR models are correlated with molecular docking study and pharmacophore mapping. Biphenyl moiety of glutamines enters into the S1′ pocket and involves π‐π interactions with Tyr142 and His120, while none of the phenylacetyl/naphthylacetyl isoglutamines interacts with Tyr142. 3D‐QSAR models also suggest the importance of hydrophobicity and steric features of biphenyl function required for higher MMP‐2 inhibition. The sulfonyl oxygen may form potential hydrogen bonding interaction with Leu83 and Ala84 of S1' pocket. This study clearly points out that biphenylsulfonyl glutamines are favorable than phenylacetyl/naphthylacetyl isoglutamines for imparting higher MMP‐2 inhibition.

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Section: Resultsmentioning

confidence: 99%

Understanding Chemico‐Biological Interactions of Glutamate MMP‐2 Inhibitors through Rigorous Alignment‐Dependent 3D‐QSAR Analyses

et al. 2017

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In silico prediction of ROCK II inhibitors by different classification approaches

Luo

et al. 2017

Mol Divers

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ROCK II is an important pharmacological target linked to central nervous system disorders such as Alzheimer's disease. The purpose of this research is to generate ROCK II inhibitor prediction models by machine learning approaches. Firstly, four sets of descriptors were calculated with MOE 2010 and PaDEL-Descriptor, and optimized by F-score and linear forward selection methods. In addition, four classification algorithms were used to initially build 16 classifiers with k-nearest neighbors [Formula: see text], naïve Bayes, Random forest, and support vector machine. Furthermore, three sets of structural fingerprint descriptors were introduced to enhance the predictive capacity of classifiers, which were assessed with fivefold cross-validation, test set validation and external test set validation. The best two models, MFK + MACCS and MLR + SubFP, have both MCC values of 0.925 for external test set. After that, a privileged substructure analysis was performed to reveal common chemical features of ROCK II inhibitors. Finally, binding modes were analyzed to identify relationships between molecular descriptors and activity, while main interactions were revealed by comparing the docking interaction of the most potent and the weakest ROCK II inhibitors. To the best of our knowledge, this is the first report on ROCK II inhibitors utilizing machine learning approaches that provides a new method for discovering novel ROCK II inhibitors.

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An integrated QSAR modeling approach to explore the structure–property and selectivity relationships of N-benzoyl-l-biphenylalanines as integrin antagonists

et al. 2017

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Integrins [Formula: see text] and [Formula: see text] are important targets to treat different inflammatory diseases, such as multiple sclerosis, inflammatory bowel diseases, rheumatoid arthritis, atherosclerosis, and asthma. Despite being valuable targets, only a few work has been reported to date regarding molecular modeling studies on these integrins. Not only that, none of these reports addressed the selectivity issue between integrins [Formula: see text] and [Formula: see text]. Therefore, a major challenge regarding the design and discovery of selective integrin antagonists remains. In this study, a series of 142 N-benzoyl-L-biphenylalanines having both integrin [Formula: see text] and [Formula: see text] inhibitory activities were considered for a variety of QSAR approaches including regression and classification-based 2D-QSARs, Hologram QSARs, 3D-QSAR CoMFA and CoMSIA studies to identify the structural requirements of these integrin antagonists. All these QSAR models were statistically validated and subsequently correlated with each other to get a detailed understanding of the activity and selectivity profiles of these molecules.

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Exploring structural requirements of unconventional Knoevenagel-type indole derivatives as anticancer agents through comparative QSAR modeling approaches

Cited by 18 publications

References 28 publications

Understanding Chemico‐Biological Interactions of Glutamate MMP‐2 Inhibitors through Rigorous Alignment‐Dependent 3D‐QSAR Analyses

Understanding Chemico‐Biological Interactions of Glutamate MMP‐2 Inhibitors through Rigorous Alignment‐Dependent 3D‐QSAR Analyses

In silico prediction of ROCK II inhibitors by different classification approaches

An integrated QSAR modeling approach to explore the structure–property and selectivity relationships of N-benzoyl-l-biphenylalanines as integrin antagonists

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