Editing Drug design (section)

== Computer-aided drug design {{anchor|computer-assisted drug design}} ==

The most fundamental goal in drug design is to predict whether a given molecule will bind to a target and if so how strongly.  [[Molecular mechanics]] or [[molecular dynamics]] is most often used to estimate the strength of the [[intermolecular interaction]] between the [[small molecule]] and its biological target.  These methods are also used to predict the [[conformational isomerism|conformation]] of the small molecule and to model conformational changes in the target that may occur when the small molecule binds to it.<ref name="sciencedirect.com"/><ref name="ReferenceA"/>  [[Semi-empirical quantum chemistry method|Semi-empirical]], [[ab initio quantum chemistry methods]], or [[density functional theory]] are often used to provide optimized parameters for the molecular mechanics calculations and also provide an estimate of the electronic properties (electrostatic potential, [[polarizability]], etc.) of the drug candidate that will influence binding affinity.<ref name = "Lewis_2011">{{cite book | title = Drug Design Strategies: Quantitative Approaches  | veditors = Gramatica P, Livingstone DJ, Davis AM | isbn = 978-1849731669 | chapter = Chapter 4: The Development of Molecular Modelling Programs: The Use and Limitations of Physical Models  | publisher = Royal Society of Chemistry | year = 2011 | pages = 88–107 | vauthors = Lewis RA | doi = 10.1039/9781849733410-00088 | series = RSC Drug Discovery }}</ref>

Molecular mechanics methods may also be used to provide semi-quantitative prediction of the binding affinity.  Also, knowledge-based [[scoring functions for docking|scoring function]] may be used to provide binding affinity estimates.  These methods use [[linear regression]], [[machine learning]], [[neural net]]s or other statistical techniques to derive predictive binding affinity equations by fitting experimental affinities to computationally derived interaction energies between the small molecule and the target.<ref name="pmid17554857">{{cite journal | vauthors = Rajamani R, Good AC | title = Ranking poses in structure-based lead discovery and optimization: current trends in scoring function development | journal = Current Opinion in Drug Discovery & Development | volume = 10 | issue = 3 | pages = 308–315 | date = May 2007 | pmid = 17554857 }}</ref><ref name="pmid19128212">{{cite journal | vauthors = de Azevedo WF, Dias R | title = Computational methods for calculation of ligand-binding affinity | journal = Current Drug Targets | volume = 9 | issue = 12 | pages = 1031–1039 | date = December 2008 | pmid = 19128212 | doi = 10.2174/138945008786949405 }}</ref>

Ideally, the computational method will be able to predict affinity before a compound is synthesized and hence in theory only one compound needs to be synthesized, saving enormous time and cost. The reality is that present computational methods are imperfect and provide, at best, only qualitatively accurate estimates of affinity. In practice, it requires several iterations of design, synthesis, and testing before an optimal drug is discovered. Computational methods have accelerated discovery by reducing the number of iterations required and have often provided novel structures.<ref name="pmid14643325">{{cite journal | vauthors = Singh J, Chuaqui CE, Boriack-Sjodin PA, Lee WC, Pontz T, Corbley MJ, Cheung HK, Arduini RM, Mead JN, Newman MN, Papadatos JL, Bowes S, Josiah S, Ling LE | display-authors = 6 | title = Successful shape-based virtual screening: the discovery of a potent inhibitor of the type I TGFbeta receptor kinase (TbetaRI) | journal = Bioorganic & Medicinal Chemistry Letters | volume = 13 | issue = 24 | pages = 4355–4359 | date = December 2003 | pmid = 14643325 | doi = 10.1016/j.bmcl.2003.09.028 }}</ref><ref name="pmid16722631">{{cite journal | vauthors = Becker OM, Dhanoa DS, Marantz Y, Chen D, Shacham S, Cheruku S, Heifetz A, Mohanty P, Fichman M, Sharadendu A, Nudelman R, Kauffman M, Noiman S | display-authors = 6 | title = An integrated in silico 3D model-driven discovery of a novel, potent, and selective amidosulfonamide 5-HT1A agonist (PRX-00023) for the treatment of anxiety and depression | journal = Journal of Medicinal Chemistry | volume = 49 | issue = 11 | pages = 3116–3135 | date = June 2006 | pmid = 16722631 | doi = 10.1021/jm0508641 }}</ref>

Computer-aided drug design may be used at any of the following stages of drug discovery:
# hit identification using [[virtual screening]] (structure- or ligand-based design)
# [[drug discovery hit to lead|hit-to-lead]] optimization of affinity and selectivity (structure-based design, [[Quantitative structure-activity relationship|QSAR]], etc.)
# [[drug development|lead optimization]] of other pharmaceutical properties while maintaining affinity

[[File:wiki Clustering.png|thumb|400px |alt=Flowchart of a common Clustering Analysis for Structure-Based Drug Design|Flowchart of a Usual Clustering Analysis for Structure-Based Drug Design]]
In order to overcome the insufficient prediction of binding affinity calculated by recent scoring functions, the protein-ligand interaction and compound 3D structure information are used for analysis. For structure-based drug design, several post-screening analyses focusing on protein-ligand interaction have been developed for improving enrichment and effectively mining potential candidates:
* Consensus scoring<ref name="pmid18831053">{{cite journal | vauthors = Liang S, Meroueh SO, Wang G, Qiu C, Zhou Y | title = Consensus scoring for enriching near-native structures from protein-protein docking decoys | journal = Proteins | volume = 75 | issue = 2 | pages = 397–403 | date = May 2009 | pmid = 18831053 | pmc = 2656599 | doi = 10.1002/prot.22252 }}</ref><ref name="pmid16426072">{{cite journal | vauthors = Oda A, Tsuchida K, Takakura T, Yamaotsu N, Hirono S | title = Comparison of consensus scoring strategies for evaluating computational models of protein-ligand complexes | journal = Journal of Chemical Information and Modeling | volume = 46 | issue = 1 | pages = 380–391 | year = 2006 | pmid = 16426072 | doi = 10.1021/ci050283k }}</ref>
** Selecting candidates by voting of multiple scoring functions
** May lose the relationship between protein-ligand structural information and scoring criterion
* Cluster analysis<ref name="pmid14711306">{{cite journal | vauthors = Deng Z, Chuaqui C, Singh J | title = Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions | journal = Journal of Medicinal Chemistry | volume = 47 | issue = 2 | pages = 337–344 | date = January 2004 | pmid = 14711306 | doi = 10.1021/jm030331x }}</ref><ref name="pmid16426058">{{cite journal | vauthors = Amari S, Aizawa M, Zhang J, Fukuzawa K, Mochizuki Y, Iwasawa Y, Nakata K, Chuman H, Nakano T | display-authors = 6 | title = VISCANA: visualized cluster analysis of protein-ligand interaction based on the ab initio fragment molecular orbital method for virtual ligand screening | journal = Journal of Chemical Information and Modeling | volume = 46 | issue = 1 | pages = 221–230 | year = 2006 | pmid = 16426058 | doi = 10.1021/ci050262q }}</ref>
** Represent and cluster candidates according to protein-ligand 3D information
** Needs meaningful representation of protein-ligand interactions.