3.4.2.3 3D-QSAR — Comparative Molecular Field Analysis
Like other forms of QSAR, 3D-QSAR starts with a series of compounds with known
structures and known biological activities. The first step is to align the molecular struc-
tures. This is done with alignment algorithms that rotate and translate the molecule in
Cartesian coordinate space so that it aligns with another molecule. The work starts with
the most rigid analogs and then progresses to conformationally flexible molecules that
are aligned with the more rigid ones. The end result is that all the molecules are even-
tually aligned, each on top of another.
Once the molecules of the training set have been aligned, a molecular field is com-
puted around each molecule, based upon a grid of points in space. Various molecular
fields are composed of field descriptorsthat reflect properties such as steric factors or
electrostatic potential. The field pointsare then fitted to predict the bioactivity. A par-
tial least-squares algorithm(PLS) is used for this form of fitting. Based upon this PLS
calculation, two pieces of information are deduced for every region of space within the
molecular field about the molecule: the first piece of information states whether that
region of space correlates with biological activity; the second piece of information
determines whether the functional group on the molecule within that region of space
should be bulky, aromatic, electron donating, electron withdrawing, and so on. The pre-
dictions from these molecular field calculations are then validated by being applied to
a test set of compounds.
3.4.2.4 Pharmacophore Identification — A Corollary of QSAR
All drugs have pharmacological activity as a result of stereoelectronic interaction with
a receptor. The receptor macromolecule “recognizes” the arrangement of certain func-
tional groups in three-dimensional space and their electron density. It is the recognition
of these groups rather than the structure of the entire drug molecule that results in an
interaction, normally consisting of noncovalent binding. The collection of relevant
groups responsible for the effect is the pharmacophore,and their geometric arrange-
ment is called the pharmacophoric pattern,whereas the position of their complemen-
tary structures on the receptor is the receptor map. Over the years, many attempts have
been made to define the pharmacophores and their pattern on many drugs. The first
attempts were rather naive and simplistic, but the recent use of QSAR has contributed
greatly to the evolution of sophisticated methods of practical significance.
The identification of the pharmacophore is a logical corollary of a QSAR calculation.
If the minimum number of descriptors that differentiate activity from inactivity is known,
it is possible to deduce the bioactive face of the molecule — that part of the molecule
around which all of the relevant descriptors are focused. This bioactive face logically
defines the pharmacophoric pattern of the bioactive molecules.
When using QSAR calculations to optimize a drug for the pharmacodynamic phase,
it is important to use relevant biological activities. If in vivo activities are used, the
bioactivities will be influenced by pharmacokinetic and pharmaceutical factors. In
order for QSAR calculations to reflect the pharmacodynamic phase, the bioactivities
should be based on in vitro data — optimally, receptor binding studies.
QSAR studies are not restricted to the optimization of biological activity at the phar-
macodynamic phase. Since toxicity also arises from drug–receptor interactions, the QSAR
DESIGNING DRUG MOLECULES TO FIT RECEPTORS 145