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Encyclopedia of Complexity and Systems Science pp 7071–7088Cite as

QSAR Modeling and QSAR Based Virtual Screening , Complexity and Challenges of Modern

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Definition of the Subject

In the early days of Quantitative Structure Activity Relationship (QSAR) modeling the experimental datasets were relativelysmall and chemically congeneric and the techniques employed were relatively unsophisticated. Since then, the sizeand complexity of experimental datasets has increased dramatically, and so had the complexity and challenges ofdata analytical approaches. This chapter examines the strategy and the output of the modern QSAR modelingapproaches especially as applied to complex biomolecular datasets. We discuss a data‐analyticalmodeling workflow developedin our laboratory that incorporates modules for combinatorial QSAR model development (i. e., using allpossible binary combinations of available descriptor sets and statistical data modeling techniques), rigorousmodel validation, and virtual screening of available chemical databases to identify novel biologically activecompounds. Our approach places...

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Abbreviations

QSAR – quantitative structure activity relationships:

a method to predict biological activity from chemical structure

Combi-QSAR – combinatorial QSAR:

implies concurrent generation of QSAR models using all possible binary combinations of different descriptor types and model optimization techniques

QSAR modeling workflow:

a hierarchy of QSAR model development and validation protocols that should be followed to establish validated and externally predictive model

kNN – k nearest neighbors:

a pattern recognition approach used in deriving non‐linear QSAR models

Model validation:

a set of computational routines used to establish internal and external predictive power of QSAR models

Applicability domain:

restriction on the chemistry space occupied by compounds for which the prediction of their activity from training set QSAR model is considered reliable.

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Acknowledgments

The studies described in this review were supported in parts by the National Institutes of Health's Cheminformatics Center planning grantP20‐RR20751 and the research grants R01GM066940 and R21GM076059.

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  1. School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, USA

    Alexander Tropsha

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  1. Alexander Tropsha
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  1. RAMTECH LIMITED, 122 Escalle Lane, Larkspur, CA, 94939, USA

    Robert A. Meyers Ph. D. (Editor-in-Chief) (Editor-in-Chief)

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© 2009 Springer-Verlag

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Tropsha, A. (2009). QSAR Modeling and QSAR Based Virtual Screening , Complexity and Challenges of Modern. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-30440-3_422

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