Editing Drug design (section)

== Types ==

[[File:Drug discovery cycle 2.png|400px|thumb|Drug discovery cycle highlighting both ligand-based (indirect) and structure-based (direct) drug design strategies.]]

There are two major types of drug design.  The first is referred to as [[ligand]]-based drug design and the second, structure-based drug design.<ref name = "Reynolds_2010"/>

=== Ligand-based ===

Ligand-based drug design (or ''indirect drug design'') relies on knowledge of other molecules that bind to the biological target of interest. These other molecules may be used to derive a [[pharmacophore]] model that defines the minimum necessary structural characteristics a molecule must possess in order to bind to the target.<ref name="isbn0-9636817-6-1">{{cite book | vauthors = Guner OF | title = Pharmacophore Perception, Development, and use in Drug Design | publisher = International University Line | location = La Jolla, Calif | year = 2000 | isbn = 978-0-9636817-6-8 | name-list-style = vanc }}</ref> A model of the biological target may be built based on the knowledge of what binds to it, and this model in turn may be used to design new molecular entities that interact with the target. Alternatively, a [[quantitative structure-activity relationship]] (QSAR), in which a correlation between calculated properties of molecules and their experimentally determined [[biological activity]], may be derived. These QSAR relationships in turn may be used to predict the activity of new analogs.<ref name = "Tropsha_2010">{{cite book | vauthors = Tropsha A | chapter = QSAR in Drug Discovery | veditors = Reynolds CH, Merz KM, Ringe D | title = Drug Design: Structure- and Ligand-Based Approaches  | chapter-url = https://books.google.com/books?id=EOInhAYMae4C&q=QSAR%20drug%20design&pg=PA151 | pages = 151–164 | date = 2010 | publisher = Cambridge University Press | location = Cambridge, UK | isbn = 978-0521887236 | edition = 1st }}</ref>

=== Structure-based ===

Structure-based drug design (or ''direct drug design'') relies on knowledge of the [[tertiary structure|three dimensional structure]] of the biological target obtained through methods such as [[X-ray crystallography#Protein crystallography|x-ray crystallography]] or [[Protein nuclear magnetic resonance spectroscopy|NMR spectroscopy]].<ref name="isbn1-4020-4406-2">{{cite book | vauthors = Leach AR, Harren J | title = Structure-based Drug Discovery | publisher = Springer | location = Berlin | year = 2007 | isbn = 978-1-4020-4406-9 }}</ref> If an experimental structure of a target is not available, it may be possible to create a [[homology modeling|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 [[dissociation constant|affinity]] and [[Ligand (biochemistry)#Selective and non-selective|selectivity]] to the target may be designed using interactive graphics and the intuition of a [[medicinal chemistry|medicinal chemist]].  Alternatively, various automated computational procedures may be used to suggest new drug candidates.<ref>{{cite journal | vauthors = Mauser H, Guba W | title = Recent developments in de novo design and scaffold hopping | journal = Current Opinion in Drug Discovery & Development | volume = 11 | issue = 3 | pages = 365–374 | date = May 2008 | pmid = 18428090 }}</ref>

Current methods for structure-based drug design can be divided roughly into three main categories.<ref name = "Klee_2000">{{cite journal | vauthors = Klebe G | title = Recent developments in structure-based drug design | journal = Journal of Molecular Medicine | volume = 78 | issue = 5 | pages = 269–281 | year = 2000 | pmid = 10954199 | doi = 10.1007/s001090000084 | s2cid = 21314020 }}</ref> 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 (molecular)|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.<ref name="ligbuilder">{{cite journal | vauthors = Wang R, Gao Y, Lai L | title = LigBuilder: A Multi-Purpose Program for Structure-Based Drug Design | journal = Journal of Molecular Modeling | year=2000 | volume=6 | issue = 7–8 | pages=498–516 | doi = 10.1007/s0089400060498| s2cid = 59482623 }}</ref><ref name="CBDDreview">{{cite journal | vauthors = Schneider G, Fechner U | title = Computer-based de novo design of drug-like molecules | journal = Nature Reviews. Drug Discovery | volume = 4 | issue = 8 | pages = 649–663 | date = August 2005 | pmid = 16056391 | doi = 10.1038/nrd1799 | s2cid = 2549851 }}</ref><ref name="pmid15031495">{{cite journal | vauthors = Jorgensen WL | title = The many roles of computation in drug discovery | journal = Science | volume = 303 | issue = 5665 | pages = 1813–1818 | date = March 2004 | pmid = 15031495 | doi = 10.1126/science.1096361 | s2cid = 1307935 | bibcode = 2004Sci...303.1813J }}</ref>  A third method is the optimization of known ligands by evaluating proposed analogs within the binding cavity.<ref name = "Klee_2000"/>

==== Binding site identification ====

[[Binding site]] identification is the first step in structure based design.<ref name = "Yuan_2013"/><ref name = "Leis_2010">{{cite journal | vauthors = Leis S, Schneider S, Zacharias M | title = In silico prediction of binding sites on proteins | journal = Current Medicinal Chemistry | volume = 17 | issue = 15 | pages = 1550–1562 | year = 2010 | pmid = 20166931 | doi = 10.2174/092986710790979944 }}</ref>  If the structure of the target or a sufficiently similar [[protein homology|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 modulator|allosteric binding sites]] that may be of interest.  Furthermore, it may be that only [[wikt:apoprotein|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 [[wikt:concave|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.<ref name = "Yuan_2013"/><ref name = "Leis_2010"/>

==== 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>