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10 - QSAR in drug discovery

from PART II - COMPUTATIONAL CHEMISTRY METHODOLOGY

Published online by Cambridge University Press:  06 July 2010

By
Alexander Tropsha
Edited by
Kenneth M. Merz, Jr ,
Dagmar Ringe  and
Charles H. Reynolds
Kenneth M. Merz, Jr
Affiliation:
University of Florida
Dagmar Ringe
Affiliation:
Brandeis University, Massachusetts
Charles H. Reynolds
Affiliation:
Johnson & Johnson Pharmaceutical Research & Development
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Summary

INTRODUCTION

With nearly fifty years of rich history of methodology developments and applications (the Hansch article of 1963 is often considered first in the field), quantitative structure/activity relationship (QSAR) modeling is a well-established area of research. As is true perhaps for any computational field, QSAR modeling has been both blessed and sometimes cursed in the literature. In the first volume of the famous book series titled Reviews in Computational Chemistry, Boyd summarized several documented cases when QSAR modeling was instrumental in discovering new drugs of drug candidates in advanced phases of clinical trials. The methodologies used by that time were relatively simple, employing a small number of physical chemical descriptors and statistical methods such as multiple linear regression. QSAR modeling was viewed solely as a tool for lead optimization; that is, it was employed to elucidate the relationship between structure and activity in relatively small congeneric compound series and predict relatively small structural modifications leading to enhanced activity.

Since the late 1980s the field has changed dramatically, fueled by changes in the size, complexity, and availability of experimental data sets of biologically active compounds. These changes have been coincidental with the advances in chemometrics, resulting in a significant increase in the number of chemical descriptors as well as growing implementation of machine learning and advanced statistical modeling techniques available for QSAR studies.

Type
Chapter
Information
Drug Design
Structure- and Ligand-Based Approaches
 , pp. 151 - 164
Publisher: Cambridge University Press
Print publication year: 2010

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References

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