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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Drug design | 3/4 | https://en.wikipedia.org/wiki/Drug_design | reference | science, encyclopedia | 2026-05-05T09:50:04.282020+00:00 | kb-cron |
=== Structure-based === Structure-based drug design (or direct drug design) relies on knowledge of the three dimensional structure of the biological target obtained through methods such as x-ray crystallography or NMR spectroscopy. If an experimental structure of a target is not available, it may be possible to create a homology model of the target based on the experimental structure of a related protein. Using the structure of the biological target, candidate drugs that are predicted to bind with high affinity and selectivity to the target may be designed using interactive graphics and the intuition of a medicinal chemist. Alternatively, various automated computational procedures may be used to suggest new drug candidates. Current methods for structure-based drug design can be divided roughly into three main categories. The first method is identification of new ligands for a given receptor by searching large databases of 3D structures of small molecules to find those fitting the binding pocket of the receptor using fast approximate docking programs. This method is known as virtual screening. A second category is de novo design of new ligands. In this method, ligand molecules are built up within the constraints of the binding pocket by assembling small pieces in a stepwise manner. These pieces can be either individual atoms or molecular fragments. The key advantage of such a method is that novel structures, not contained in any database, can be suggested. A third method is the optimization of known ligands by evaluating proposed analogs within the binding cavity.
==== Binding site identification ==== Binding site identification is the first step in structure based design. If the structure of the target or a sufficiently similar homolog is determined in the presence of a bound ligand, then the ligand should be observable in the structure in which case location of the binding site is trivial. However, there may be unoccupied allosteric binding sites that may be of interest. Furthermore, it may be that only apoprotein (protein without ligand) structures are available and the reliable identification of unoccupied sites that have the potential to bind ligands with high affinity is non-trivial. In brief, binding site identification usually relies on identification of concave surfaces on the protein that can accommodate drug sized molecules that also possess appropriate "hot spots" (hydrophobic surfaces, hydrogen bonding sites, etc.) that drive ligand binding.
==== Scoring functions ====
Structure-based drug design attempts to use the structure of proteins as a basis for designing new ligands by applying the principles of molecular recognition. Selective high affinity binding to the target is generally desirable since it leads to more efficacious drugs with fewer side effects. Thus, one of the most important principles for designing or obtaining potential new ligands is to predict the binding affinity of a certain ligand to its target (and known antitargets) and use the predicted affinity as a criterion for selection. One early general-purposed empirical scoring function to describe the binding energy of ligands to receptors was developed by Böhm. This empirical scoring function took the form:
Δ
G
bind
=
Δ
G
0
+
Δ
G
hb
Σ
h
−
b
o
n
d
s
+
Δ
G
ionic
Σ
i
o
n
i
c
−
i
n
t
+
Δ
G
lipophilic
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A
|
+
Δ
G
rot
N
R
O
T
{\displaystyle \Delta G_{\text{bind}}=\Delta G_{\text{0}}+\Delta G_{\text{hb}}\Sigma _{h-bonds}+\Delta G_{\text{ionic}}\Sigma _{ionic-int}+\Delta G_{\text{lipophilic}}\left\vert A\right\vert +\Delta G_{\text{rot}}{\mathit {NROT}}}
where:
ΔG0 – empirically derived offset that in part corresponds to the overall loss of translational and rotational entropy of the ligand upon binding. ΔGhb – contribution from hydrogen bonding ΔGionic – contribution from ionic interactions ΔGlip – contribution from lipophilic interactions where |Alipo| is surface area of lipophilic contact between the ligand and receptor ΔGrot – entropy penalty due to freezing a rotatable in the ligand bond upon binding A more general thermodynamic "master" equation is as follows:
Δ
G
bind
=
−
R
T
ln
K
d
K
d
=
[
Ligand
]
[
Receptor
]
[
Complex
]
Δ
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bind
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Δ
G
desolvation
+
Δ
G
motion
+
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G
configuration
+
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interaction
{\displaystyle {\begin{array}{lll}\Delta G_{\text{bind}}=-RT\ln K_{\text{d}}\\[1.3ex]K_{\text{d}}={\dfrac {[{\text{Ligand}}][{\text{Receptor}}]}{[{\text{Complex}}]}}\\[1.3ex]\Delta G_{\text{bind}}=\Delta G_{\text{desolvation}}+\Delta G_{\text{motion}}+\Delta G_{\text{configuration}}+\Delta G_{\text{interaction}}\end{array}}}
where:
desolvation – enthalpic penalty for removing the ligand from solvent motion – entropic penalty for reducing the degrees of freedom when a ligand binds to its receptor configuration – conformational strain energy required to put the ligand in its "active" conformation interaction – enthalpic gain for "resolvating" the ligand with its receptor The basic idea is that the overall binding free energy can be decomposed into independent components that are known to be important for the binding process. Each component reflects a certain kind of free energy alteration during the binding process between a ligand and its target receptor. The Master Equation is the linear combination of these components. According to Gibbs free energy equation, the relation between dissociation equilibrium constant, Kd, and the components of free energy was built. Various computational methods are used to estimate each of the components of the master equation. For example, the change in polar surface area upon ligand binding can be used to estimate the desolvation energy. The number of rotatable bonds frozen upon ligand binding is proportional to the motion term. The configurational or strain energy can be estimated using molecular mechanics calculations. Finally the interaction energy can be estimated using methods such as the change in non polar surface, statistically derived potentials of mean force, the number of hydrogen bonds formed, etc. In practice, the components of the master equation are fit to experimental data using multiple linear regression. This can be done with a diverse training set including many types of ligands and receptors to produce a less accurate but more general "global" model or a more restricted set of ligands and receptors to produce a more accurate but less general "local" model.