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Hierarchical QSAR technology based on the Simplex representation of molecular structure

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Abstract

This article is about the hierarchical quantitative structure–activity relationship technology (HiT QSAR) based on the Simplex representation of molecular structure (SiRMS) and its application for different QSAR/QSP(property)R tasks. The essence of this technology is a sequential solution (with the use of the information obtained on the previous steps) to the QSAR problem by the series of enhanced models of molecular structure description [from one dimensional (1D) to four dimensional (4D)]. It is a system of permanently improved solutions. In the SiRMS approach, every molecule is represented as a system of different simplexes (tetratomic fragments with fixed composition, structure, chirality and symmetry). The level of simplex descriptors detailing increases consecutively from the 1D to 4D representation of the molecular structure. The advantages of the approach reported here are the absence of “molecular alignment” problems, consideration of different physical–chemical properties of atoms (e.g. charge, lipophilicity, etc.), the high adequacy and good interpretability of obtained models and clear ways for molecular design. The efficiency of the HiT QSAR approach is demonstrated by comparing it with the most popular modern QSAR approaches on two representative examination sets. The examples of successful application of the HiT QSAR for various QSAR/QSPR investigations on the different levels (1D–4D) of the molecular structure description are also highlighted. The reliability of developed QSAR models as predictive virtual screening tools and their ability to serve as the base of directed drug design was validated by subsequent synthetic and biological experiments, among others. The HiT QSAR is realized as a complex of computer programs known as HiT QSAR software that also includes a powerful statistical block and a number of useful utilities.

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Notes

  1. A preliminary step is that the list of structural parameters must be well-organized on a defined principle (for example, lexicographic).

  2. This classification is offered by the authors of Cerius 2.

  3. The authors express their sincere gratitude to Prof. J. Leszczynski, Dr. L. Gorb and Dr. M. Quasim for fruitful cooperation during the development of this task.

  4. The authors express their sincere gratitude to Dr. M. Schmidtke, Prof. P. Wutzler, Dr. V. Makarov, Dr. O. Riabova, Mr. N. Kovdienko and Mr. A. Hromov for their most fruitful cooperation that made the development of this task possible.

  5. The authors express their sincere gratitude to Dr. M. Schmidtke, Prof. P. Wutzler, Dr. V. Makarov, Dr. O. Riabova and Ms. Volineckaya for their fruitful cooperation that made possible the development of this task.

  6. The authors express their sincere gratitude to Prof. G.L. Kamalov, Dr. S.A. Kotlyar and Dr. G.N. Chuprin for fruitful cooperation during the development of this task.

  7. The authors express their sincere gratitude to Dr. V.P. Lozitsky, Dr. R.N. Lozytska and Dr. A.S. Fedtchouk for their fruitful cooperation during the development of this task.

  8. The anti-influenza and antiherpetic investigations described were carried out as a result of fruitful cooperation with Dr. V.P. Lozitsky, Dr. R.N. Lozytska and Dr. A.S. Fedtchouk, Dr. T.L. Gridina, Dr. S. Basok, Dr. D. Chikhichin, Mr. V. Chelombitko and Dr. J.-J. Vanden Eynde. The authors express their sincere gratitude to all of these colleagues.

  9. In this and anti-iherpetic research 1D modeling was not carried out.

  10. We are aware that these models can approximate not only the variation in activity but also the variation in experimental errors. The high values of the R 2 test can be explained by the fact that test compounds are very similar to the training ones, that there are only few compounds in test set, by the high quality of the obtained models, by simple good luck and/or by a combination of all these factors.

Abbreviations

A/I/EVS:

Automatic/interactive/evolutionary variables selection

ACE:

Angiotensin converting enzyme

AchE:

Acetylcholinesterase

CoMFA:

Comparative molecular fields analysis QSAR approach

CoMSIA:

Comparative molecular similarity indexes analysis QSAR approach

DA:

Applicability domain

DSTP:

Dispirotripiperazine

EVA:

Eigenvalue analysis QSAR approach

GA:

Genetic algorithm

HiT QSAR:

Hierarchical QSAR technology

HQSAR:

Hologram QSAR approach

HRV:

Human rhinovirus

HSV:

Herpes simplex virus

MLR:

Multiple linear regression statistical method

PLS:

Partial least squares or projection on latent structures statistical method

Q 2 :

Cross-validation determination coefficient

QSAR/QSPR:

Quantitative structure–activity/property relationship

R 2 :

Determination coefficient for training set

R 2 test :

Determination coefficient for test set

SD:

Simplex descriptor

SI:

Selectivity index

SiRMS:

Simplex representation of the molecular structure QSAR approach

TV:

Trend-vector statistical method

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Acknowledgments

This work was partially supported by a grant of the President of Ukraine (President of Ukraine grant for young investigators GP/F11/0115), the Science & Technology Center in Ukraine (STCU project 3147) and INTAS foundation (INTAS Grant 97-31528).

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Authors and Affiliations

  1. A.V. Bogatsky Physico-Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya doroga 86, Odessa, 65080, Ukraine

    V. E. Kuz’min, A. G. Artemenko & E. N. Muratov

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  1. V. E. Kuz’min
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  2. A. G. Artemenko
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  3. E. N. Muratov
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Correspondence to E. N. Muratov.

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Kuz’min, V.E., Artemenko, A.G. & Muratov, E.N. Hierarchical QSAR technology based on the Simplex representation of molecular structure. J Comput Aided Mol Des 22, 403–421 (2008). https://doi.org/10.1007/s10822-008-9179-6

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