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New Advances in QSPR/QSAR Analysis of Nitrocompounds: Solubility, Lipophilicity, and Toxicity

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Practical Aspects of Computational Chemistry II

Abstract

This chapter discusses QSAR/QSPR applications of the simplex representation of molecular structure (SiRMS) methodology. It has been determined that SiRMS proves to be quite an efficient tool for analyzing nitroaromatic aqueous solubility, lipophilicity, and toxicity. Using multiple linear regression (MLR) and random forest (RF) statistical methods at the 2D level of representation of molecular structure, models possessing high statistical characteristics (MLR: R 2=0.85, Q 2=0.83; RF: R 2=0.99, \( R_{\text{OOB}}^2 = 0.{88} \)) were obtained for aqueous solubility of more than 2,800 organic compounds. The external validation set of 301 compounds (including 47 nitro-, nitroso-, and nitrogen-rich military compounds) was used for evaluation of the models’ predictive ability.

A 2D QSPR model based on SiRMS and RF approaches has been developed to predict “structure of octanol-water partition coefficient (LogK ow)” for a set of more than 10,970 organic compounds and has been successfully validated with two external test sets. This model predicts LogK ow values with the greatest accuracy among available modern models. LogK ow values of 29 military compounds with unknown experimental value of LogK ow have been predicted. The correspondence between observed and predicted toxicity values obtained using 1D and 2D models is quite high.

The most comprehensive consensus model allows for improved accuracy of toxicity predictions and has been shown to be an effective virtual screening tool. It was found that substitution of fluorine and hydroxyl groups into nitroaromatic compounds increases toxicity, whereas substitution of a methyl group has the opposite effect. The influence of chlorine on toxicity has not been determined unambiguously. The mutual influence of substituents in the benzene ring is substantially nonadditive and plays a crucial role regarding toxicity.

This chapter contains 5 subsections, 12 tables, and 8 figures. In the first and second subsections, a short introduction is presented, and applied methods are described. The third, fourth, and fifth subsections are devoted to the QSPR analysis of aqueous solubility, lipophilicity, and toxicity for nitroaromatic compounds with military interest.

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Notes

  1. 1.

    Most probably, the molecules of first external test were used for the construction of ALOGPS and EPI models.

  2. 2.

    For fluorine, there is only one contribution value because it occurs only in compound 23.

  3. 3.

    Differentiations of atoms by their refractions and electronegativities were not used in this model because of representation of the whole substituent like one pseudoatom.

  4. 4.

    This model is similar within certain limits to Free-Wilson approach.

  5. 5.

    The isomers cannot be distinguished by 1D QSAR models.

Abbreviations

CART:

Classification and regression trees algorithm

COSMO:

Conductor-like screening model

CR:

Continuum regression

DA:

Applicability domain

GA:

Genetic algorithm

HIT QSAR:

Hierarchical QSAR technology

LD50 :

50% lethal dose concentration

LogK ow :

Octanol-water partition coefficient

LUMO:

Lowest unoccupied molecular orbital

MAE:

Mean absolute error

MCI:

Molecular connectivity indices

MLR:

Multiple linear regression statistical method

MOE:

Molecular Operating Environment

MP:

Melting point

NN:

Neural network

OOB:

Out-of-bag set

PLS:

Partial least squares or projection on latent structures statistical method

RF:

Random forest statistical method

Q 2 :

Cross-validation determination coefficient

QSAR/QSPR:

Quantitative structure-activity/property relationship

R 2 :

Determination coefficient for training set

\( {R}_{\text{test}}^2 \) :

Determination coefficient for test set

SE:

Standard errors of prediction

SiRMS:

Simplex representation of molecular structure

S w :

Aqueous solubility

TV:

Trend-vector statistical method

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Acknowledgments

The authors thank the NSF CREST Interdisciplinary Nanotoxicity Center NSF-CREST for support–Grant # HRD-0833178, and the NSF-EPSCoR Award #: 362492-190200-01\NSFEPS-0903787. The use of trade, product, or firm names in this report is for descriptive purposes only and does not imply endorsement by the U.S. Government. The tests described and the resulting data presented herein, unless otherwise noted, were obtained from research conducted under the Environmental Quality Technology Program of the United States Army Corps of Engineers by the USAERDC. Permission was granted by the Chief of Engineers to publish this information. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.

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Authors and Affiliations

  1. Laboratory of Theoretical Chemistry, Department of Molecular Structure, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa, 65080, Ukraine

    Liudmyla N. Ognichenko, Victor E. Kuz’min, Eugene N. Muratov, Anatoly G. Artemenko, Nikolay A. Kovdienko & Pavel G. Polishchuk

  2. Badger Technical Services, LLC, 39180, Vicksburg, MS, USA

    Leonid Gorb

  3. Environmental Laboratory, US Army ERDC, Vicksburg, MS, 39180, USA

    Frances C. Hill

  4. Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Jackson State University, 17910, Lynch Street, Jackson, MS, 39217, USA

    Jerzy Leszczynski

  5. US Army ERDC, Vicksburg, MS, 39180, USA

    Jerzy Leszczynski

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    Jerzy Leszczynski

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Ognichenko, L.N. et al. (2012). New Advances in QSPR/QSAR Analysis of Nitrocompounds: Solubility, Lipophilicity, and Toxicity. In: Leszczynski, J., Shukla, M. (eds) Practical Aspects of Computational Chemistry II. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0923-2_8

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