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The Use of Qsar and Computational Methods in Drug Design

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Recent Advances in QSAR Studies

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 8))

Abstract

The application of quantitative structure–activity relationships (QSARs) has significantly impacted the paradigm of drug discovery. Following the successful utilization of linear solvation free-energy relationships (LSERs), numerous 2D- and 3D-QSAR methods have been developed, most of them based on descriptors for hydrophobicity, polarizability, ionic interactions, and hydrogen bonding. QSAR models allow for the calculation of physicochemical properties (e.g., lipophilicity), the prediction of biological activity (or toxicity), as well as the evaluation of absorption, distribution, metabolism, and excretion (ADME). In pharmaceutical research, QSAR has a particular interest in the preclinical stages of drug discovery to replace tedious and costly experimentation, to filter large chemical databases, and to select drug candidates. However, to be part of drug discovery and development strategies, QSARs need to meet different criteria (e.g., sufficient predictivity). This chapter describes the foundation of modern QSAR in drug discovery and presents some current challenges and applications for the discovery and optimization of drug candidates

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Authors and Affiliations

  1. School of Pharmacy and Chemistry, Liverpool John Moores University, Liverpool, L3 3AF, England

    Fania Bajot

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  1. Fania Bajot
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Editors and Affiliations

  1. Dept. Chemistry, University of Gdansk, ul. Jana Sobieskiego 18, Gdansk, 80-952, Poland

    Tomasz Puzyn

  2. Dept. Chemistry, Jackson State University, J. R. Lynch St. 1325, Jackson, 39217, U.S.A.

    Jerzy Leszczynski

  3. Dept. Chemistry, John Moores University, Byrom St., Liverpool, L3 3AF, United Kingdom

    Mark T. Cronin

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Bajot, F. (2010). The Use of Qsar and Computational Methods in Drug Design. In: Puzyn, T., Leszczynski, J., Cronin, M. (eds) Recent Advances in QSAR Studies. Challenges and Advances in Computational Chemistry and Physics, vol 8. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9783-6_9

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