Editing Drug design (section)

==== Scoring functions ====
{{Main|Scoring functions for docking}}
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]].  [[Binding selectivity|Selective]] high [[affinity (pharmacology)|affinity]] binding to the target is generally desirable since it leads to more [[efficacy|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 [[antitarget]]s) and use the predicted affinity as a criterion for selection.<ref name = "Warren_2011">{{cite book | title = Drug Design Strategies: Quantitative Approaches  | veditors = Gramatica P, Livingstone DJ, Davis AM | isbn = 978-1849731669 | chapter = Chapter 16: Scoring Drug-Receptor Interactions | publisher = Royal Society of Chemistry | year = 2011 | pages = 440–457 | vauthors = Warren GL, Warren SD | series = RSC Drug Discovery | doi = 10.1039/9781849733410-00440 }}</ref>
<!-- [[File:Master Equation in Scoring Function.jpg|thumb|400 px]] -->

One early general-purposed empirical scoring function to describe the binding energy of ligands to receptors was developed by Böhm.<ref name="pmid7964925">{{cite journal | vauthors = Böhm HJ | title = The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure | journal = Journal of Computer-Aided Molecular Design | volume = 8 | issue = 3 | pages = 243–256 | date = June 1994 | pmid = 7964925 | doi = 10.1007/BF00126743 | s2cid = 2491616 | bibcode = 1994JCAMD...8..243B }}</ref><ref name = "Liu_2015">{{cite journal | vauthors = Liu J, Wang R | title = Classification of current scoring functions | journal = Journal of Chemical Information and Modeling | volume = 55 | issue = 3 | pages = 475–482 | date = March 2015 | pmid = 25647463 | doi = 10.1021/ci500731a | name-list-style = vanc }}</ref> This empirical scoring function took the form:

<math>\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} </math>

where:
* ΔG<sub>0</sub> – empirically derived offset that in part corresponds to the overall loss of translational and rotational entropy of the ligand upon binding.
* ΔG<sub>hb</sub> – contribution from hydrogen bonding
* ΔG<sub>ionic</sub> – contribution from ionic interactions
* ΔG<sub>lip</sub> – contribution from lipophilic interactions where |A<sub>lipo</sub>| is surface area of lipophilic contact between the ligand and receptor
* ΔG<sub>rot</sub> – entropy penalty due to freezing a rotatable in the ligand bond upon binding

A more general thermodynamic "master" equation is as follows:<ref name="Ajay_1995">{{cite journal | vauthors = Murcko MA | title = Computational methods to predict binding free energy in ligand-receptor complexes | journal = Journal of Medicinal Chemistry | volume = 38 | issue = 26 | pages = 4953–4967 | date = December 1995 | pmid = 8544170 | doi = 10.1021/jm00026a001 }}</ref>

<math>\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}</math>

where:
* desolvation – [[enthalpy|enthalpic]] penalty for removing the ligand from solvent
* motion – [[entropy|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, K<sub>d</sub>, 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 [[potential of mean force|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.<ref>{{cite book | title = Drug Design Strategies: Quantitative Approaches  | veditors = Gramatica P, Livingstone DJ, Davis AM | isbn = 978-1849731669 | chapter-url = https://books.google.com/books?id=YTguNlEpmnoC&q=drug+design+local+global+models&pg=PA466 | chapter = Chapter 17: Modeling Chemicals in the Environment  | publisher = Royal Society of Chemistry | year = 2011 | page = 466 | vauthors = Gramatica P | doi = 10.1039/9781849733410-00458 | series = RSC Drug Discovery }}</ref>