Much of chemistry, molecular biology, and drug design, are centered
around the relationships between chemical structure and measured
properties of compounds and polymers, such as viscosity, acidity,
solubility, toxicity, enzyme binding, and membrane penetration. For
any set of compounds, these relationships are by necessity
complicated, particularly when the properties are of biological
nature. To investigate and utilize such complicated relationships,
henceforth abbreviated SAR for structure-activity relationships,
and QSAR for quantitative SAR, we need a description of the
variation in chemical structure of relevant compounds and
biological targets, good measures of the biological properties,
and, of course, an ability to synthesize compounds of interest. In
addition, we need reasonable ways to construct and express the
relationships, i. e. , mathematical or other models, as well as
ways to select the compounds to be investigated so that the
resulting QSAR indeed is informative and useful for the stated
purposes. In the present context, these purposes typically are the
conceptual understanding of the SAR, and the ability to propose new
compounds with improved property profiles. Here we discuss the two
latter parts of the SARlQSAR problem, i. e. , reasonable ways to
model the relationships, and how to select compounds to make the
models as "good" as possible. The second is often called the
problem of statistical experimental design, which in the present
context we call statistical molecular design, SMD. 1.
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