Medicinal Chemistry

3.4.1.7 Correlating Analog Structure with Bioactivity

The approach involving the design of analogs of an active lead compound has remained
unchanged for decades, and the expertise of the synthetic medicinal chemist is as much
in demand as ever; however, the intuitive process of selecting structural modifications
for synthesis becomes circumspect in this approach, and models based on multiple
regression analysis and pattern recognition methods, using very powerful computer
techniques, are increasingly being employed as aids. It is obviously much faster and
cheaper to calculate the required properties of novel compounds from a large pool of
data on their analogs than to synthesize and screen all such new compounds in the clas-
sical fashion. Only promising candidates are investigated experimentally. The results
gained this way are incorporated into the database, expanding and strengthening the
theoretical search. Eventually, sufficient material accumulates to aid in making a confi-
dent decision about whether the “best” analog has been prepared or whether the series
should be abandoned. Quantitative structure–activity relationship studies represent a
systematic approach to this correlation of structure with pharmacological activity.

3.4.2 Quantitative Structure–Activity Relationship (QSAR) Studies

The relationship between chemical structure and biological activity has always been at
the center of drug research. In the past, drug structures were modified intuitively and
empirically, depending on the imagination and experience of the synthesizing chemist,
and were based on analogies. Surprisingly, the results were often gratifying, even if
obtained only serendipitously or on the basis of the wrong hypothesis. However, this
hit-or-miss approach, practiced even now, is enormously wasteful. Considering that
only one of several thousand synthesized compounds will reach the pharmacy shelves,
and that the development of a single drug can cost millions of dollars, it is imperative
that rational short-cuts to drug design be found. Quantitative structure–activity rela-
tionship (QSAR) studies represent this important rational short-cut. QSAR endeavors
to elevate drug design from an art to a science.
QSAR methods are in part retrospective as well as predictive, since a “training set”
of compounds of known pharmacological activity must first be established. The pur-
pose of such methods is to increase the probability of finding active compounds among
those eventually synthesized, thus keeping synthetic and screening efforts within rea-
sonable limits in relation to the success rate. There are three main classifications of
QSAR methods:

1. 1D-QSAR (e.g., Hansch analysis)


2. 2D-QSAR (e.g., pattern recognition analysis)


3. 3D-QSAR (e.g., comparative molecular field analysis)

Each method has its own strengths and weaknesses.

3.4.2.1 1D-QSAR — Hansch Analysis

Historically, this is the most popular mathematical approach to QSAR. The major con-
tribution of Hansch analysis is in recognizing the importance of logP, where Pis the
octanol–water partition coefficient. LogPis perhaps the most important measure of a

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