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Consensus Drug Design Using IT Microcosm

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Application of Computational Techniques in Pharmacy and Medicine

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 17))

Abstract

This chapter discusses Microcosm, an information technology package for predicting the pharmacological activity of chemical compounds. This technology is based on a complex prediction methodology with a consensus approach to prediction as its central component. The complex methodology of prediction in IT Microcosm is essentially different from that of other QSAR approaches in that it employs a redundant multi-descriptor, multi-level representation of the structure of chemical compounds by an aggregate of fragment descriptors with different physicochemical meanings and varying extents of complexity. The methodology also includes several classification methods that differ in their mathematical formalisms and several decision making circuits that are conceptual in the results they yield. At the same time, no feature space reductions are made, and no significant variables are isolated; all of the parameters of description are used in the construction of the prediction regularities. The integral decision rules are constructed by generalizing the spectrum of primary prediction estimates using different levels and types of consensus. In this chapter, we describe the paradigm of IT Microcosm, including its theoretical concepts, a specialized QL language for chemical structure representation, and prediction methods and strategies using the package. The adequacy, validity and high accuracy of IT Microcosm are demonstrated via sample predictions of the various pharmacological activities of structurally similar and structurally diverse organic compounds, complex organic salts, supramolecular complexes and substance mixtures, accounting for the synergy between the individual components of mixtures. The authors also present the results of a successful application of IT Microcosm, along with in vivo and in vitro experimental methods for (1) the search for novel potent antioxidants, antiarrhythmics and antiplatelet agents; (2) the optimization of the composition of supramolecular complexes with antioxidant and antiarrhythmic activity; (3) the evaluation of the spectrum and the extent of pharmacological effects and the optimization of the composition of naturally occurring multicomponent drugs; and (4) the evaluation of the synergistic effects of mixtures of drug substances. IT Microcosm consists of a package of 20 computer programs; there is a separate free Microcosm White computer program.

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Abbreviations

5-HT:

5-Hydroxytryptamine

BA:

Bayesian approach

CS:

Conservative strategy

DLOOCV:

Double leave-one-out cross-validation

DM:

Distance method

GA:

Glycyrrhizinic acid

H:

Histamine

in silico:

research of biologically active substances that is performed using a computer or via computer simulation

IT:

Information Technology

LBDD:

Ligand-Based Drug Design

LDM:

Local distribution method

LOOCV:

Leave-one-out cross-validation

LP:

Lipid peroxidation

NNM:

Nearest neighbor method

NS:

Normal strategy

QSAR:

Quantitative Structure-Activity Relationships

QSPR:

Quantitative Structure-Property Relationships

RS:

Risk strategy

SBDD:

Structure-Based Drug Design

SHCV:

Split-half cross-validation

ST:

Self-testing

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Authors and Affiliations

  1. Volgograd State Medical University (VSMU), Pavshikh Bortsov Sq. 1, 400131, Volgograd, Russian Federation

    Pavel M. Vassiliev, Alexander A. Spasov, Vadim A. Kosolapov, Aida F. Kucheryavenko & Nataliya A. Gurova

  2. Institute of Physical and Organic Chemistry at Southern Federal University (IPOC SFU), Stachka Ave. 194/2, 344090, Rostov-on-Don, Russian Federation

    Vera A. Anisimova

Authors
  1. Pavel M. Vassiliev
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  2. Alexander A. Spasov
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  3. Vadim A. Kosolapov
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  4. Aida F. Kucheryavenko
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  5. Nataliya A. Gurova
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  6. Vera A. Anisimova
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Correspondence to Pavel M. Vassiliev .

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  1. Department of Molecular Biophysics, Laboratory of Computational Structural Biology, Kiev, Ukraine

    Leonid Gorb

  2. Department of Molecular Structure and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute, National Academy of Sciences of Ukraine, Odessa, Ukraine

    Victor Kuz'min

  3. Department of Chemical Biology and Medicinal Chemistry, University of North Carolina, Laboratory for Molecular Modeling, Chapel Hill, North Carolina, USA

    Eugene Muratov

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Vassiliev, P., Spasov, A., Kosolapov, V., Kucheryavenko, A., Gurova, N., Anisimova, V. (2014). Consensus Drug Design Using IT Microcosm. In: Gorb, L., Kuz'min, V., Muratov, E. (eds) Application of Computational Techniques in Pharmacy and Medicine. Challenges and Advances in Computational Chemistry and Physics, vol 17. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9257-8_12

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