What is Wrong with Quantitative Structure–Property Relations Models Based on Three-Dimensional Descriptors?

Hechinger, Manuel; Leonhard, Kai; Marquardt, Wolfgang

doi:10.1021/ci300246m

Cited by 41 publications

(25 citation statements)

References 78 publications

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“…reiteratively increase the number of descriptors bit by bit (stepwise forward procedure). QSAR studies carried out without considering the active conformation compounds (tautomers, ionic forms, prodrugs) are omitting critical information reducing the quality of the input data needed for equations which in turn lose predictive power, ending up in an incorrect molecular design [3, 81, 82]. …”

Section: Discussionmentioning

confidence: 99%

Antiprotozoal Nitazoxanide Derivatives: Synthesis, Bioassays and QSAR Study Combined with Docking for Mechanistic Insight

Scior

Lozano-Aponte²,

Ajmani³

et al. 2015

CAD

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In view of the serious health problems concerning infectious diseases in heavily populated areas, we followed the strategy of lead compound diversification to evaluate the near-by chemical space for new organic compounds. To this end, twenty derivatives of nitazoxanide (NTZ) were synthesized and tested for activity against Entamoeba histolytica parasites. To ensure drug-likeliness and activity relatedness of the new compounds, the synthetic work was assisted by a quantitative structure-activity relationships study (QSAR). Many of the inherent downsides – well-known to QSAR practitioners – we circumvented thanks to workarounds which we proposed in prior QSAR publication. To gain further mechanistic insight on a molecular level, ligand-enzyme docking simulations were carried out since NTZ is known to inhibit the protozoal pyruvate ferredoxin oxidoreductase (PFOR) enzyme as its biomolecular target.

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Section: Discussionmentioning

confidence: 99%

Antiprotozoal Nitazoxanide Derivatives: Synthesis, Bioassays and QSAR Study Combined with Docking for Mechanistic Insight

Scior

Lozano-Aponte²,

Ajmani³

et al. 2015

CAD

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“…Because in all other variable selection methods tested here, 3D descriptors gave better results than 2D descriptors (results not shown), in the further discussions only the 3D descriptors are considered. The benefit of 3D descriptors is treated here on a purely empirical basis, only considering the performance of QSPR regression models; a recent study [36] discusses fundamental problems with 3D descriptors, e. g., their temperature dependence.…”

Section: Resultsmentioning

confidence: 99%

Multivariate Linear QSPR/Qsar Models: Rigorous Evaluation of Variable Selection for PLS

Вармуза

Filzmoser²,

Dehmer³

2013

Computational and Structural Biotechnology Journal

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Basic chemometric methods for making empirical regression models for QSPR/QSAR are briefly described from a user's point of view. Emphasis is given to PLS regression, simple variable selection and a careful and cautious evaluation of the performance of PLS models by repeated double cross validation (rdCV). A demonstration example is worked out for QSPR models that predict gas chromatographic retention indices (values between 197 and 504 units) of 209 polycyclic aromatic compounds (PAC) from molecular descriptors generated by Dragon software. Most favorable models were obtained from data sets containing also descriptors from 3D structures with all H-atoms (computed by Corina software), using stepwise variable selection (reducing 2688 descriptors to a subset of 22). The final QSPR model has typical prediction errors for the retention index of ±12 units (95% tolerance interval, for test set objects). Programs and data are provided as supplementary material for the open source R software environment.

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“…This results in a significant increase of calculation time at every step of calculation. Nevertheless, the methods of molecular dynamics reduce the calculating time for multi-atomic systems by 1-4 orders of magnitude in comparison to non-and semiempirical methods [74].…”

Section: Molecular Mechanicsmentioning

confidence: 99%

Theoretical description of 2D-cluster formation of nonionic surfactants at the air/water interface

2015

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Recent progress in modeling of the surfactant behavior from atomistic to continuous at the air/water interface across different space-time scales is reviewed. Advantages and disadvantages of modern quantum mechanical, molecular dynamical, and mesoscale methods are discussed for description of interactions between amphiphilic molecules leading to formation of 2D films. The use of nonempirical and semiempirical methods for assessment of the thermodynamic and structural parameters of large van der Waals complexes is of particular interest. An approach for calculation of the thermodynamic and structural parameters of clusterization for nonionic surfactants at the air/water interface is proposed on the basis of the quantum chemical semiempirical PM3 method. This approach implicitly takes into account the influence of the interface on the surfactant molecules via stretching and orienting effect. The calculations are carried out in the supermolecule approximation for a limited number of small amphiphilic aggregates with different alkyl chain. The correlation analysis of the calculated data array provides the increments contributing by the intermolecular CH···HC interactions and interactions between hydrophilic parts into the thermodynamic parameters of formation and clusterization. Obtained increments are further used for constructing the dependencies of the thermodynamic clusterization parameters per one monomer on the alkyl chain length for large clusters up to 2D films. In the framework of the proposed theoretical approach, the next parameters are calculated as follows: enthalpy, entropy, and Gibbs' energy of clusterization for 11 homologous series of nonionic surfactants, threshold chain length enabling the process of monolayer formation at standard conditions, the Btemperature effect^of clusterization, and the structural parameters of the monolayer unit cell (particularly the tilt angle) depending on the size of the hydrophilic headgroup of the amphiphilic compound.

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What is Wrong with Quantitative Structure–Property Relations Models Based on Three-Dimensional Descriptors?

Cited by 41 publications

References 78 publications

Antiprotozoal Nitazoxanide Derivatives: Synthesis, Bioassays and QSAR Study Combined with Docking for Mechanistic Insight

Antiprotozoal Nitazoxanide Derivatives: Synthesis, Bioassays and QSAR Study Combined with Docking for Mechanistic Insight

Multivariate Linear QSPR/Qsar Models: Rigorous Evaluation of Variable Selection for PLS

Theoretical description of 2D-cluster formation of nonionic surfactants at the air/water interface

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