Outliers in SAR and QSAR: Is unusual binding mode a possible source of outliers?

Kim, Ki Hwan

doi:10.1007/s10822-007-9106-2

Cited by 38 publications

(38 citation statements)

References 57 publications

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“…19,70,89 In cases where the ligand binding mode is maintained as functional groups are modified, conventional relative free energy calculations may often work well without any special treatment of alternate potential binding modes. However, small modifications to ligands do on occasion yield big differences in ligand binding orientation, 65,88,[90][91][92][93][94][95][96][97] and there is currently no way to know when this will happen. For example, Stout et al developed a series of thymidylate synthase (TS) inhibitors based on phenolphthalein and phthalein derivatives.…”

Section: E Binding Modes Are Difficult To Predictmentioning

confidence: 99%

Perspective: Alchemical free energy calculations for drug discovery

Mobley

Klimovich²

2012

The Journal of Chemical Physics

223

295

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Section: E Binding Modes Are Difficult To Predictmentioning

confidence: 99%

Perspective: Alchemical free energy calculations for drug discovery

Mobley

Klimovich²

2012

The Journal of Chemical Physics

223

295

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“…It seems more likely that the discrepancy reflects systematic changes in the protein around the binding site, as well as variations in binding mode [67][68][69]. Both effects can profitably be addressed by shifting to ligand-based alignment instead of insisting on a protein-centric frame of reference when using docking or crystal structures to align ligands.…”

Section: Discussionmentioning

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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“…The most common explanation proposed for the outcome of outliers in QSAR is based on the finding that these outliers bind to the receptor in a different binding mode and therefore do not follow the same mechanism of interaction showed by other molecules in the study. [57] Other reasons proposed for their occurrence in QSAR studies are an incorrectly measured experimental value (Y outlier), a significant difference in the physicochemical properties, and their structural uniqueness. Outliers may even arise due to the lack of certain descriptors or parameters included in the study that are unable to describe the QSAR for all the compounds in the entire data set.…”

Section: Outliersmentioning

confidence: 99%

Deciphering the Structural Requirements of Nucleoside Bisubstrate Analogues for Inhibition of MbtA in Mycobacterium tuberculosis: A FB‐QSAR Study and Combinatorial Library Generation for Identifying Potential Hits

Maganti

Das

Mascarenhas

et al. 2011

Molecular Informatics

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The re-emergence of tuberculosis infections, which are resistant to conventional drug therapy, has steadily risen in the last decade. Inhibitors of aryl acid adenylating enzyme known as MbtA, involved in siderophore biosynthesis in Mycobacterium tuberculosis, are being explored as potential antitubercular agents. The ability to identify fragments that interact with a biological target is a key step in fragment based drug design (FBDD). To expand the boundaries of quantitative structure activity relationship (QSAR) paradigm, we have proposed a Fragment Based QSAR methodology, referred here in as FB-QSAR, for deciphering the structural requirements of a series of nucleoside bisubstrate analogs for inhibition of MbtA, a key enzyme involved in siderophore biosynthetic pathway. For the development of FB-QSAR models, statistical techniques such as stepwise multiple linear regression (SMLR), genetic function approximation (GFA) and GFAspline were used. The predictive ability of the generated models was validated using different statistical metrics, and similarity-based coverage estimation was carried out to define applicability boundaries. To aid the creation of novel antituberculosis compounds, a bioisosteric database was enumerated using the combichem approach endorsed mining in a lead-like chemical space. The generated library was screened using an integrated in-silico approach and potential hits identified.

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Outliers in SAR and QSAR: Is unusual binding mode a possible source of outliers?

Cited by 38 publications

References 57 publications

Perspective: Alchemical free energy calculations for drug discovery

Perspective: Alchemical free energy calculations for drug discovery

A ligand’s-eye view of protein binding

Deciphering the Structural Requirements of Nucleoside Bisubstrate Analogues for Inhibition of MbtA in Mycobacterium tuberculosis: A FB‐QSAR Study and Combinatorial Library Generation for Identifying Potential Hits

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