Docking-based CoMFA and CoMSIA studies of non-nucleoside reverse transcriptase inhibitors of the pyridinone derivative type

Medina‐Franco, José L.; Rodrı́guez-Morales, Sergio; Juarez‐Gordiano, Cecilia; Hernández‐Campos, Alicia; Castillo, Rafael

doi:10.1023/b:jcam.0000047816.15514.ab

Cited by 26 publications

(25 citation statements)

References 1 publication

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“…Docked conformations were the starting point of three-dimensional quantitative structure-activity relationship analyses (3D-QSAR). [34] Docking results further support the binding model developed for L-697,661 and other potent inhibitors including the calculated hydrogen bonding of the nitrogen at the pyridinone ring with the main chain oxygen of K101. 3D-QSAR models also suggested that the proposed interaction between Y181 and the benzoxazole ring of the potent pyridinone derivatives may stabilize the complex.…”

Section: Discovery and Developmentsupporting

confidence: 64%

Pyridin‐2(1H)‐ones: A Promising Class of HIV‐1 Non‐nucleoside Reverse Transcriptase Inhibitors

Medina‐Franco¹,

Martínez‐Mayorga²,

Juarez‐Gordiano³

et al. 2007

ChemMedChem

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Section: Discovery and Developmentsupporting

confidence: 64%

Pyridin‐2(1H)‐ones: A Promising Class of HIV‐1 Non‐nucleoside Reverse Transcriptase Inhibitors

Medina‐Franco¹,

Martínez‐Mayorga²,

Juarez‐Gordiano³

et al. 2007

ChemMedChem

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“…Compound 27 (cf., Table 1) showed good activity in clinical studies but resistant strains of the virus emerged [9,10]. Several molecular modeling studies of the pyridinone derivatives have been reported including QSAR modeling using Hansch method [11,12], 3D-QSAR approach [13], and docking [14].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure–activity Relationship Analysis of Pyridinone HIV-1 Reverse Transcriptase Inhibitors using the k Nearest Neighbor Method and QSAR-based Database Mining

Medina‐Franco

Gelbukh

Oloff

et al. 2005

J Comput Aided Mol Des

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We have developed quantitative structure-activity relationship (QSAR) models for 44 non-nucleoside HIV-1 reverse transcriptase inhibitors (NNRTIs) of the pyridinone derivative type. The k nearest neighbor (kNN) variable selection approach was used. This method utilizes multiple descriptors such as molecular connectivity indices, which are derived from two-dimensional molecular topology. The modeling process entailed extensive validation including the randomization of the target property (Y-randomization) test and the division of the dataset into multiple training and test sets to establish the external predictive power of the training set models. QSAR models with high internal and external accuracy were generated, with leave-one-out cross-validated R2 (q2) values ranging between 0.5 and 0.8 for the training sets and R2 values exceeding 0.6 for the test sets. The best models with the highest internal and external predictive power were used to search the National Cancer Institute database. Derivatives of the pyrazolo[3,4-d]pyrimidine and phenothiazine type were identified as promising novel NNRTIs leads. Several candidates were docked into the binding pocket of nevirapine with the AutoDock (version 3.0) software. Docking results suggested that these types of compounds could be binding in the NNRTI binding site in a similar mode to a known non-nucleoside inhibitor nevirapine.

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“…Medina-Franco et al used AutoDock and apo1VRT to align a series of NNRTIs [39]. Zhou and Madura [40] subsequently applied CoMFA and CoMSIA to 50 TIBO derivatives, another class of NNRTI, in the only analysis directly comparable to this study.…”

Section: Discussionmentioning

confidence: 90%

“…Several CoMFA studies have been published [38][39][40] as well as a range of other analytical approaches [41,42].…”

Section: Reverse Transcriptasementioning

confidence: 99%

A ligand’s-eye view of protein binding

Clark¹

2008

J Comput Aided Mol Des

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Docking tools created for structure-based design and virtual screening have also been used to automate ligand alignment for comparative molecular field analysis (CoMFA). Models based on such alignments have been compared with those obtained based solely on shared ligand substructures, but such comparisons have generally failed to distinguish between conformational specification (alignment in the internal coordinate space) and embedding in a shared external frame of reference (Cartesian alignment). Here, large sets of inhibitors were docked into two cyclooxygenase and two reverse transcriptase crystal structures, and the poses generated were evaluated in terms of the CoMFA models they produced. Realigning the conformers obtained by docking by rigid-body rotation and translation to overlay their common substructures improved model statistics and interpretability, provided the protein structure used for docking was reasonably appropriate to the ligands being considered.

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Docking-based CoMFA and CoMSIA studies of non-nucleoside reverse transcriptase inhibitors of the pyridinone derivative type

Cited by 26 publications

References 1 publication

Pyridin‐2(1H)‐ones: A Promising Class of HIV‐1 Non‐nucleoside Reverse Transcriptase Inhibitors

Pyridin‐2(1H)‐ones: A Promising Class of HIV‐1 Non‐nucleoside Reverse Transcriptase Inhibitors

Quantitative Structure–activity Relationship Analysis of Pyridinone HIV-1 Reverse Transcriptase Inhibitors using the k Nearest Neighbor Method and QSAR-based Database Mining

A ligand’s-eye view of protein binding

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