Ligand-based drug design (LBDD) approaches are applied
when the three-dimensional structure of the target is unknown
or when it is not fully reliable (i.e., homology models of the
target are based on poor sequence identity) [1].Con- ventional
3D QSAR models consider the ensemble of conformations,
orientations, tautomers,
Fig.8 An example of apharmacophoremodelof(R)-roscovitine,which
tightly bindsCDK5[23].Crucialinteractingresidues of CDK5 are
shown in green (hydrophobic residues), blue (H-bond acceptors),
and magenta (H-bond donors).That is, for a given molecular
structure, properties such as solubility, membrane permeability,
partition coefficients, blood-brain barrier penetration, plasma
protein binding, and metabolite formation are computed and
compared against a database of known drugs.The same
principles are valid for molecular similarity methods, where
similitudes in geometrical or physicochemical properties among
active compounds are used to identify new active compounds
from among the many that can be virtually screened.Typical descriptors are physicochemical
features such as molecular weight, geometry, surface accessible
area, aromaticity index, electronegativity, polarizability, and
solvation properties.Absorption, distribution, metabolism, excretion, and toxicity
(ADMET) are the key parameters that can be optimized with QSAR
approaches to get a drug candidate with a proper drug-like
pharmacokinetic (PK) profile.Despite its practical utility for
quickly evaluating drug-likeness, it is nowadays clear that this rule
can only estimate the compound's probable success by highlighting
potentially problematic aspects of its physicochemical and structural
profile [22].This says that an optimal orally bioavailable
drug should have a molecular weight less than 500, less than 5 H-
bond donor sites, and less than 10 H-bond acceptor sites, while the
log of the octanol/water partition coefficient (logP), a measure of
hydrophobicity, should be below 5.Additionally, interactions with influx/efflux
transporter proteins or metabolic enzymes can greatly affect the
final therapeutic effect and should be considered in the early phases
of a drug discovery program.For example, activity data of
active molecules can be used to extract mathematical models
for early prediction of activity or, more often, metabolic
properties that could generate toxicity problems. 8).