Three-dimensional QSAR of HPPD inhibitors, PSA inhibitors, and anxiolytic agents: Effect of tautomerism on the CoMFA models

Zou, Jian‐Wei; Luo, Congcong; Zhang, Hua-Xing; Liu, Haichun; Jiang, Yong‐Jun; Q, Yu

doi:10.1016/j.jmgm.2007.03.002

Cited by 19 publications

(10 citation statements)

References 43 publications

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“…They may, for instance, point up critical points of metabolism in one species or another. They may also result from incidental errors in the input structures—wrong protonation states, tautomerization27 or other overlooked chemical transformations. The neutral hydroxyquinoline ketone 1 , for example, might correspond to the structure registered in the corporate database, but the compound it represents would likely exist in aqueous solution as the hydrated and protonated quinolone 2 .…”

Section: Things That Remain the Samementioning

confidence: 99%

A perspective on the role of quantitative structure–activity and structure–property relationships in herbicide discovery

Clark

2012

Pest Management Science

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Section: Things That Remain the Samementioning

confidence: 99%

A perspective on the role of quantitative structure–activity and structure–property relationships in herbicide discovery

Clark

2012

Pest Management Science

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“…MEIV covers the contribution of each atom of the molecule and can be obtained directly from molecular structure computation. Compared with the CoMFA [14][15][16][17] or other methods [18][19][20][21] which have proved useful in many cases, MEIV does not suffer from certain limitations such as the requirement of molecular superposition. The MEIV was used to describe the structures of a set of components of N-PACs.…”

Section: Resultsmentioning

confidence: 99%

Quantitative Structure–Retention Relationship Study on Nitrogen-Containing Polycyclic Aromatic Compounds by Using Molecular Electronegativity Interaction Vector

Liu

2011

Chromatographia

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The molecular structures of 117 nitrogen-containing polycyclic aromatic compounds (N-PACs) were described by a method of molecular structural characterization (MSC) called molecular electronegativity interaction vector (MEIV). The samples were divided into a training set and a test set. For the training set, a quantitative structure-retention relationship (QSRR) model was built up by multiple linear regression (MLR) and the model was evaluated by performing the cross validation with the leave-one-out (LOO) procedure. The correlation coefficient (R) and the cross-verification correlation coefficient (R CV ) of the model were 0.992 and 0.991, respectively. Moreover, the model was evaluated by the test set and satisfactory results with a correlation coefficient (R test ) of 0.993 were obtained. The results suggested good stability and predictability of the model.

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“…Based on the known HPPD inhibitor [ 29 , 30 , 31 ] six typical compounds, as the training set in the HipHop model, were used to generate common feature based pharmacophore models. The bioactivity value IC50 and molecular properties of these compounds were listed in Table 2 .…”

Section: Methodsmentioning

confidence: 99%

3D Pharmacophore-Based Virtual Screening and Docking Approaches toward the Discovery of Novel HPPD Inhibitors

Sun

et al. 2017

Molecules

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p-Hydroxyphenylpyruvate dioxygenase (HPPD) is not only the useful molecular target in treating life-threatening tyrosinemia type I, but also an important target for chemical herbicides. A combined in silico structure-based pharmacophore and molecular docking-based virtual screening were performed to identify novel potential HPPD inhibitors. The complex-based pharmacophore model (CBP) with 0.721 of ROC used for screening compounds showed remarkable ability to retrieve known active ligands from among decoy molecules. The ChemDiv database was screened using CBP-Hypo2 as a 3D query, and the best-fit hits subjected to molecular docking with two methods of LibDock and CDOCKER in Accelrys Discovery Studio 2.5 (DS 2.5) to discern interactions with key residues at the active site of HPPD. Four compounds with top rankings in the HipHop model and well-known binding model were finally chosen as lead compounds with potential inhibitory effects on the active site of target. The results provided powerful insight into the development of novel HPPD inhibitors herbicides using computational techniques.

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Three-dimensional QSAR of HPPD inhibitors, PSA inhibitors, and anxiolytic agents: Effect of tautomerism on the CoMFA models

Cited by 19 publications

References 43 publications

A perspective on the role of quantitative structure–activity and structure–property relationships in herbicide discovery

A perspective on the role of quantitative structure–activity and structure–property relationships in herbicide discovery

Quantitative Structure–Retention Relationship Study on Nitrogen-Containing Polycyclic Aromatic Compounds by Using Molecular Electronegativity Interaction Vector

3D Pharmacophore-Based Virtual Screening and Docking Approaches toward the Discovery of Novel HPPD Inhibitors

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