View source for Drug design

{{Short description|Invention of new medications based on knowledge of a biological target}}
{{Distinguish|Designer drug}}
{{AI4 | image = Drug discovery cycle.svg | annotations = | align = right | image-width = 300 | width = 300 | height = 225 | alt = Drug discovery cycle schematic | caption =}}

'''Drug design''', often referred to as '''rational drug design''' or simply [[rational design]], is the [[invention|inventive]] process of finding new [[medications]] based on the knowledge of a [[biological target]].<ref name = "isbn0-415-...">{{cite book | vauthors = Madsen U, Krogsgaard-Larsen P, Liljefors T | title = Textbook of Drug Design and Discovery | publisher = Taylor & Francis | location = Washington, D.C. | year = 2002 | isbn = 978-0-415-28288-8 | name-list-style = vanc }}</ref> The [[drug]] is most commonly an [[organic compound|organic]] [[small molecule]] that activates or inhibits the function of a [[biomolecule]] such as a [[protein]], which in turn results in a [[therapeutic effect|therapeutic]] benefit to the [[patient]]. In the most basic sense, drug design involves the design of molecules that are complementary in [[shape]] and [[electric charge|charge]] to the biomolecular target with which they interact and therefore will bind to it. Drug design frequently but not necessarily relies on [[molecular modelling|computer modeling]] techniques.<ref name = "Reynolds_2010">{{cite book | veditors = Reynolds CH, Merz KM, Ringe D | title = Drug Design: Structure- and Ligand-Based Approaches | date = 2010 | publisher = Cambridge University Press | location = Cambridge, UK | isbn = 978-0521887236 | edition = 1 | name-list-style = vanc }}</ref> This type of modeling is sometimes referred to as '''computer-aided drug design'''. Finally, drug design that relies on the knowledge of the three-dimensional structure of the biomolecular target is known as '''structure-based drug design'''.<ref name = "Reynolds_2010"/> In addition to small molecules, [[biopharmaceutical]]s including [[peptide]]s<ref name="sciencedirect.com">{{cite journal | vauthors = Fosgerau K, Hoffmann T | title = Peptide therapeutics: current status and future directions | journal = Drug Discovery Today | volume = 20 | issue = 1 | pages = 122–128 | date = January 2015 | pmid = 25450771 | doi = 10.1016/j.drudis.2014.10.003 | doi-access = free }}</ref><ref name="ReferenceA">{{cite journal | vauthors = Ciemny M, Kurcinski M, Kamel K, Kolinski A, Alam N, Schueler-Furman O, Kmiecik S | title = Protein-peptide docking: opportunities and challenges | journal = Drug Discovery Today | volume = 23 | issue = 8 | pages = 1530–1537 | date = August 2018 | pmid = 29733895 | doi = 10.1016/j.drudis.2018.05.006 | doi-access = free }}</ref> and especially [[therapeutic antibodies]] are an increasingly important class of drugs and computational methods for improving the affinity, selectivity, and stability of these protein-based therapeutics have also been developed.<ref>{{cite journal | vauthors = Shirai H, Prades C, Vita R, Marcatili P, Popovic B, Xu J, Overington JP, Hirayama K, Soga S, Tsunoyama K, Clark D, Lefranc MP, Ikeda K | display-authors = 6 | title = Antibody informatics for drug discovery | journal = Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics | volume = 1844 | issue = 11 | pages = 2002–2015 | date = November 2014 | pmid = 25110827 | doi = 10.1016/j.bbapap.2014.07.006 }}</ref>

==Definition==
The phrase "drug design" is similar to [[ligand (biochemistry)|ligand]] design (i.e., design of a molecule that will bind tightly to its target).<ref name = "pmid8739258">{{cite journal | vauthors = Tollenaere JP | title = The role of structure-based ligand design and molecular modelling in drug discovery | journal = Pharmacy World & Science | volume = 18 | issue = 2 | pages = 56–62 | date = April 1996 | pmid = 8739258 | doi = 10.1007/BF00579706 | s2cid = 21550508 }}</ref> Although design techniques for prediction of binding affinity are reasonably successful, there are many other properties, such as [[bioavailability]], [[biological half-life|metabolic half-life]], and [[adverse drug reaction|side effects]], that first must be optimized before a ligand can become a safe and effictive drug. These other characteristics are often difficult to predict with rational design techniques.

Due to high attrition rates, especially during [[clinical trial|clinical phases]] of [[drug development]], more attention is being focused early in the drug design process on selecting candidate drugs whose [[physical chemistry|physicochemical]] properties are predicted to result in fewer complications during development and hence more likely to lead to an approved, marketed drug.<ref name="pmid26091267">{{cite journal | vauthors = Waring MJ, Arrowsmith J, Leach AR, Leeson PD, Mandrell S, Owen RM, Pairaudeau G, Pennie WD, Pickett SD, Wang J, Wallace O, Weir A | display-authors = 6 | title = An analysis of the attrition of drug candidates from four major pharmaceutical companies | journal = Nature Reviews. Drug Discovery | volume = 14 | issue = 7 | pages = 475–486 | date = July 2015 | pmid = 26091267 | doi = 10.1038/nrd4609 | s2cid = 25292436 }}</ref>  Furthermore, [[in vitro]] experiments complemented with computation methods are increasingly used in early [[drug discovery]] to select compounds with more favorable [[ADME]] (absorption, distribution, metabolism, and excretion) and [[toxicological]] profiles.<ref name="pmid12963322">{{cite journal | vauthors = Yu H, Adedoyin A | title = ADME-Tox in drug discovery: integration of experimental and computational technologies | journal = Drug Discovery Today | volume = 8 | issue = 18 | pages = 852–861 | date = September 2003 | pmid = 12963322 | doi = 10.1016/S1359-6446(03)02828-9 }}</ref>

== Drug targets ==

A [[biomolecular target]] (most commonly a [[protein]] or a [[nucleic acid]]) is a key molecule involved in a particular [[metabolic pathway|metabolic]] or [[signal transduction|signaling]] pathway that is associated with a specific disease condition or [[pathology]] or to the [[infectivity]] or survival of a [[microorganism|microbial]] [[pathogen]].  Potential drug targets are not necessarily disease causing but must by definition be disease modifying.<ref name="pmid19740696">{{cite journal | vauthors = Dixon SJ, Stockwell BR | title = Identifying druggable disease-modifying gene products | journal = Current Opinion in Chemical Biology | volume = 13 | issue = 5–6 | pages = 549–555 | date = December 2009 | pmid = 19740696 | pmc = 2787993 | doi = 10.1016/j.cbpa.2009.08.003 }}</ref> In some cases, [[small molecule]]s will be designed to enhance or inhibit the target function in the specific disease modifying pathway. Small molecules (for example receptor [[agonist]]s, [[receptor antagonist|antagonists]], [[inverse agonist]]s, or [[selective receptor modulator|modulators]]; enzyme [[enzyme activator|activators]] or [[enzyme inhibitor|inhibitors]]; or ion channel [[channel opener|openers]] or [[channel blocker|blockers]])<ref>{{cite journal | vauthors = Imming P, Sinning C, Meyer A | title = Drugs, their targets and the nature and number of drug targets | journal = Nature Reviews. Drug Discovery | volume = 5 | issue = 10 | pages = 821–834 | date = October 2006 | pmid = 17016423 | doi = 10.1038/nrd2132 | s2cid = 8872470 }}</ref> will be designed that are complementary to the [[binding site]] of target.<ref>{{cite journal | vauthors = Anderson AC | title = The process of structure-based drug design | journal = Chemistry & Biology | volume = 10 | issue = 9 | pages = 787–797 | date = September 2003 | pmid = 14522049 | doi = 10.1016/j.chembiol.2003.09.002 | doi-access = free }}</ref> Small molecules (drugs) can be designed so as not to affect any other important "off-target" molecules (often referred to as [[antitarget]]s) since drug interactions with off-target molecules may lead to undesirable [[adverse effect|side effects]].<ref>{{cite journal | vauthors = Recanatini M, Bottegoni G, Cavalli A | title = In silico antitarget screening | journal = Drug Discovery Today: Technologies | volume = 1 | issue = 3 | pages = 209–215 | date = December 2004 | pmid = 24981487 | doi = 10.1016/j.ddtec.2004.10.004 }}</ref> Due to similarities in binding sites, closely related targets identified through [[sequence homology]] have the highest chance of cross reactivity and hence highest side effect potential.

Most commonly, drugs are [[organic compound|organic]] [[small molecule]]s produced through chemical synthesis, but biopolymer-based drugs (also known as [[biopharmaceutical]]s) produced through biological processes are becoming increasingly more common.<ref>{{cite book| vauthors = Wu-Pong S, Rojanasakul Y |title=Biopharmaceutical drug design and development|date=2008|publisher=Humana Press|location=Totowa, NJ Humana Press|isbn=978-1-59745-532-9|edition=2nd }}</ref> In addition, [[mRNA]]-based [[gene silencing]] technologies may have therapeutic applications.<ref>{{cite journal | vauthors = Scomparin A, Polyak D, Krivitsky A, Satchi-Fainaro R | title = Achieving successful delivery of oligonucleotides--From physico-chemical characterization to in vivo evaluation | journal = Biotechnology Advances | volume = 33 | issue = 6 Pt 3 | pages = 1294–1309 | date = November 2015 | pmid = 25916823 | doi = 10.1016/j.biotechadv.2015.04.008 }}</ref>  For example, nanomedicines based on mRNA can streamline and expedite the drug development process, enabling transient and localized expression of immunostimulatory molecules.<ref>{{cite journal | vauthors = Youssef M, Hitti C, Puppin Chaves Fulber J, Kamen AA | title = Enabling mRNA Therapeutics: Current Landscape and Challenges in Manufacturing | journal = Biomolecules | volume = 13 | issue = 10 | pages = 1497 | date = October 2023 | pmid = 37892179 | pmc = 10604719 | doi = 10.3390/biom13101497 | doi-access = free }}</ref> In vitro transcribed (IVT) mRNA allows for delivery to various accessible cell types via the blood or alternative pathways. The use of IVT mRNA serves to convey specific genetic information into a person's cells, with the primary objective of preventing or altering a particular disease.<ref>{{cite journal | vauthors = Sahin U, Karikó K, Türeci Ö | title = mRNA-based therapeutics--developing a new class of drugs | journal = Nature Reviews. Drug Discovery | volume = 13 | issue = 10 | pages = 759–780 | date = October 2014 | pmid = 25233993 | doi = 10.1038/nrd4278 | s2cid = 27454546 | doi-access = free }}</ref>

=== Drug discovery ===

====Phenotypic drug discovery====

[[Classical pharmacology|Phenotypic drug discovery]] is a traditional drug discovery method, also known as forward pharmacology or classical pharmacology. It uses the process of phenotypic screening on collections of synthetic small molecules, natural products, or extracts within chemical libraries to pinpoint substances exhibiting beneficial therapeutic effects. This method is to first discover the in vivo or in vitro functional activity of drugs (such as extract drugs or natural products), and then perform target identification. Phenotypic discovery uses a practical and target-independent approach to generate initial leads, aiming to discover pharmacologically active compounds and therapeutics that operate through novel drug mechanisms.<ref>{{cite journal | vauthors = Swinney DC, Lee JA | title = Recent advances in phenotypic drug discovery | journal = F1000Research | volume = 9 | pages = F1000 Faculty Rev–944 | date = 2020 | pmid = 32850117 | pmc = 7431967 | doi = 10.12688/f1000research.25813.1 | doi-access = free }}</ref> This method allows the exploration of disease phenotypes to find potential treatments for conditions with unknown, complex, or multifactorial origins, where the understanding of molecular targets is insufficient for effective intervention.<ref>{{cite journal | vauthors = Moffat JG, Vincent F, Lee JA, Eder J, Prunotto M | title = Opportunities and challenges in phenotypic drug discovery: an industry perspective | journal = Nature Reviews. Drug Discovery | volume = 16 | issue = 8 | pages = 531–543 | date = August 2017 | pmid = 28685762 | doi = 10.1038/nrd.2017.111 | s2cid = 6180139 | doi-access = free }}</ref>

====Rational drug discovery====

Rational drug design (also called [[reverse pharmacology]]) begins with a hypothesis that modulation of a specific biological target may have therapeutic value. In order for a biomolecule to be selected as a drug target, two essential pieces of information are required.  The first is evidence that modulation of the target will be disease modifying. This knowledge may come from, for example, disease linkage studies that show an association between mutations in the biological target and certain disease states.<ref>{{cite book | title = Introduction to Biological and Small Molecule Drug Research and Development: theory and case studies | vauthors = Ganellin CR, Jefferis R, Roberts SM | year = 2013 | publisher = Elsevier | chapter = The small molecule drug discovery process — from target selection to candidate selection | chapter-url = https://books.google.com/books?id=342JY314Fl4C&q=target+validation+disease+linkage&pg=PA83 | isbn = 9780123971760}}</ref> The second is that the target is capable of binding to a small molecule and that its activity can be modulated by the small molecule.<ref name="Yuan_2013">{{cite journal | vauthors = Yuan Y, Pei J, Lai L | title = Binding site detection and druggability prediction of protein targets for structure-based drug design | journal = Current Pharmaceutical Design | volume = 19 | issue = 12 | pages = 2326–2333 | date = Dec 2013 | pmid = 23082974 | doi = 10.2174/1381612811319120019 }}</ref>

Once a suitable target has been identified, the target is normally [[molecular cloning|cloned]] and [[protein production|produced]] and [[protein purification|purified]]. The purified protein is then used to establish a [[Drug discovery#Screening and design|screening assay]]. In addition, the three-dimensional structure of the target may be determined.

The search for small molecules that bind to the target is begun by screening libraries of potential drug compounds. This may be done by using the screening assay (a "wet screen"). In addition, if the structure of the target is available, a [[virtual screening|virtual screen]] may be performed of candidate drugs. Ideally, the candidate drug compounds should be "[[druglikeness|drug-like]]", that is they should possess properties that are predicted to lead to [[oral bioavailability]], adequate chemical and metabolic stability, and minimal toxic effects.<ref>{{cite journal | vauthors = Rishton GM | title = Nonleadlikeness and leadlikeness in biochemical screening | journal = Drug Discovery Today | volume = 8 | issue = 2 | pages = 86–96 | date = January 2003 | pmid = 12565011 | doi = 10.1016/s1359644602025722 }}</ref> Several methods are available to estimate druglikeness such as [[Lipinski's Rule of Five]] and a range of scoring methods such as [[lipophilic efficiency]].<ref>{{cite book | title = The Practice of Medicinal Chemistry | editor = Wermuth CG | vauthors = Hopkins AL | chapter = Chapter 25: Pharmacological space | pages = 521–527 | chapter-url = https://books.google.com/books?id=Qmt1_DQkCpEC&q=druggability+Lipinski%27s+Rule+of+Five+lipophilic+efficiency&pg=PA527 | isbn = 978-0-12-374194-3 | publisher = Academic Press | edition = 3 | year = 2011 }}</ref> Several methods for predicting drug metabolism have also been proposed in the scientific literature.<ref>{{cite book | title = Drug Metabolism Prediction | vauthors = Kirchmair J | isbn = 978-3-527-67301-8 | year = 2014 | publisher = Wiley-VCH | volume = 63 | series = Wiley's Methods and Principles in Medicinal Chemistry }}</ref>

Due to the large number of drug properties that must be simultaneously optimized during the design process, [[multi-objective optimization]] techniques are sometimes employed.<ref>{{cite journal | vauthors = Nicolaou CA, Brown N | title = Multi-objective optimization methods in drug design | journal = Drug Discovery Today: Technologies | volume = 10 | issue = 3 | pages = e427–e435 | date = September 2013 | pmid = 24050140 | doi = 10.1016/j.ddtec.2013.02.001 }}</ref>  Finally because of the limitations in the current methods for prediction of activity, drug design is still very much reliant on [[Serendipity#Pharmacology|serendipity]]<ref>{{cite journal | vauthors = Ban TA | title = The role of serendipity in drug discovery | journal = Dialogues in Clinical Neuroscience | volume = 8 | issue = 3 | pages = 335–344 | year = 2006 | pmid = 17117615 | pmc = 3181823 | doi = 10.31887/DCNS.2006.8.3/tban }}</ref> and [[bounded rationality]].<ref>{{cite journal | title = Bounded Rationality and the Search for Organizational Architecture: An Evolutionary Perspective on the Design of Organizations and Their Evolvability | vauthors = Ethiraj SK, Levinthal D | journal = Administrative Science Quarterly | volume = 49 | issue = 3 | date = Sep 2004 | pages = 404–437 | publisher = Sage Publications, Inc. on behalf of the Johnson Graduate School of Management, Cornell University | doi = 10.2307/4131441 | jstor = 4131441 | ssrn = 604123 | s2cid = 142910916 | url = https://repository.upenn.edu/mgmt_papers/76 }}</ref>

== 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.

== 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>

== Examples ==

A particular example of rational drug design involves the use of three-dimensional information about biomolecules obtained from such techniques as X-ray crystallography and NMR spectroscopy. Computer-aided drug design in particular becomes much more tractable when there is a high-resolution structure of a target protein bound to a potent ligand.  This approach to drug discovery is sometimes referred to as structure-based drug design.   The first unequivocal example of the application of [[QSAR|structure-based drug design]] leading to an approved drug is the carbonic anhydrase inhibitor [[dorzolamide]], which was approved in 1995.<ref name="pmid8164249">{{cite journal | vauthors = Greer J, Erickson JW, Baldwin JJ, Varney MD | title = Application of the three-dimensional structures of protein target molecules in structure-based drug design | journal = Journal of Medicinal Chemistry | volume = 37 | issue = 8 | pages = 1035–1054 | date = April 1994 | pmid = 8164249 | doi = 10.1021/jm00034a001 }}</ref><ref name="isbn3-527-29343-4">{{cite book | vauthors = Timmerman H, Gubernator K, Böhm HJ, Mannhold R, Kubinyi H | title = Structure-based Ligand Design (Methods and Principles in Medicinal Chemistry) | publisher = Wiley-VCH | location = Weinheim | year = 1998 | isbn = 978-3-527-29343-8 | name-list-style = vanc }}</ref>

Another case study in rational drug design is [[imatinib]], a [[tyrosine kinase]] inhibitor designed specifically for the ''bcr-abl'' fusion protein that is characteristic for [[Philadelphia chromosome]]-positive [[leukemia]]s ([[chronic myelogenous leukemia]] and occasionally [[acute lymphocytic leukemia]]). Imatinib is substantially different from previous drugs for [[cancer]], as most agents of [[chemotherapy]] simply target rapidly dividing cells, not differentiating between cancer cells and other tissues.<ref>{{cite journal | vauthors = Capdeville R, Buchdunger E, Zimmermann J, Matter A | title = Glivec (STI571, imatinib), a rationally developed, targeted anticancer drug | journal = Nature Reviews. Drug Discovery | volume = 1 | issue = 7 | pages = 493–502 | date = July 2002 | pmid = 12120256 | doi = 10.1038/nrd839 | s2cid = 2728341 }}</ref>

Additional examples include:

{{div col|colwidth=20em}}
* Many of the [[atypical antipsychotic]]s
* [[Cimetidine]], the prototypical [[H2-receptor antagonist|H<sub>2</sub>-receptor antagonist]] from which the later members of the class were developed
* Selective [[Cyclooxygenase|COX-2]] inhibitor [[NSAID]]s
* [[Enfuvirtide]], a peptide HIV entry inhibitor
* [[Nonbenzodiazepines]] like [[zolpidem]]  and [[zopiclone]]
* [[Raltegravir]], an [[HIV integrase]] inhibitor<ref name="url_AutoDock_Integrase_Inhibitor">{{cite web | url = http://autodock.scripps.edu/news/autodocks-role-in-developing-the-first-clinically-approved-hiv-integrase-inhibitor | title = AutoDock's role in Developing the First Clinically-Approved HIV Integrase Inhibitor | date = 2007-12-17 | work = Press Release | publisher = The Scripps Research Institute }}</ref>
* [[Selective serotonin reuptake inhibitor|SSRIs]] (selective serotonin reuptake inhibitors), a class of [[antidepressant]]s
* [[Zanamivir]], an [[antiviral drug]]
{{Div col end}}

== Drug screening ==
Types of drug screening include [[phenotypic screening]], [[high-throughput screening]], and [[virtual screening]]. Phenotypic screening is characterized by the process of screening drugs using cellular or animal disease models to identify compounds that alter the phenotype and produce beneficial disease-related effects.<ref>{{cite journal | vauthors = Prior M, Chiruta C, Currais A, Goldberg J, Ramsey J, Dargusch R, Maher PA, Schubert D | display-authors = 6 | title = Back to the future with phenotypic screening | journal = ACS Chemical Neuroscience | volume = 5 | issue = 7 | pages = 503–513 | date = July 2014 | pmid = 24902068 | pmc = 4102969 | doi = 10.1021/cn500051h }}</ref><ref>{{cite journal | vauthors = Kotz J |date=April 2012 |title=Phenotypic screening, take two |journal=Science-Business EXchange |language=en |volume=5 |issue=15 |pages=380 |doi=10.1038/scibx.2012.380 |s2cid=72519717 |issn=1945-3477|doi-access=free }}</ref> Emerging technologies in high-throughput screening substantially enhance processing speed and decrease the required detection volume.<ref>{{cite journal | vauthors = Hertzberg RP, Pope AJ | title = High-throughput screening: new technology for the 21st century | journal = Current Opinion in Chemical Biology | volume = 4 | issue = 4 | pages = 445–451 | date = August 2000 | pmid = 10959774 | doi = 10.1016/S1367-5931(00)00110-1 }}</ref> Virtual screening is completed by computer, enabling a large number of molecules can be screened with a short cycle and low cost. Virtual screening uses a range of computational methods that empower chemists to reduce extensive virtual libraries into more manageable sizes.<ref>{{cite journal | vauthors = Walters WP, Stahl MT, Murcko MA |date=April 1998 |title=Virtual screening—an overview |journal=Drug Discovery Today |language=en |volume=3 |issue=4 |pages=160–178 |doi=10.1016/S1359-6446(97)01163-X}}</ref>

==Case studies==
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* [[5-HT3 antagonist#5-HT3 antagonists drug design|5-HT3 antagonists]]
* [[Nicotinic agonist|Acetylcholine receptor agonists]]
* [[Discovery and development of angiotensin receptor blockers|Angiotensin receptor antagonists]]
* [[Bcr-Abl tyrosine-kinase inhibitor]]s
* [[Cannabinoid receptor antagonist#Drug design|Cannabinoid receptor antagonists]]
* [[CCR5 receptor antagonist]]s
* [[Discovery and development of cyclooxygenase 2 inhibitors|Cyclooxygenase 2 inhibitors]]
* [[Development of dipeptidyl peptidase-4 inhibitors|Dipeptidyl peptidase-4 inhibitors]]
* [[Discovery and development of HIV protease inhibitors|HIV protease inhibitors]]
* [[NK1 receptor antagonist#Drug discovery and development|NK1 receptor antagonists]]
* [[Discovery and development of non-nucleoside reverse transcriptase inhibitors|Non-nucleoside reverse transcriptase inhibitors]]
* [[Discovery and development of nucleoside and nucleotide reverse-transcriptase inhibitors|Nucleoside and nucleotide reverse transcriptase inhibitors]]
* [[Discovery and development of phosphodiesterase 5 inhibitors|PDE5 inhibitors]]
* [[Discovery and development of proton pump inhibitors|Proton pump inhibitors]]
* [[Discovery and Development of Renin Inhibitors|Renin inhibitors]]
* [[Discovery and development of triptans|Triptans]]
* [[Discovery and development of TRPV1 antagonists|TRPV1 antagonists]]
* [[c-Met inhibitors]]
{{Div col end}}

== Criticism ==
It has been argued that the highly rigid and focused nature of rational drug design suppresses serendipity in drug discovery.<ref name="pmid18319418">{{cite journal | vauthors = Klein DF | title = The loss of serendipity in psychopharmacology | journal = JAMA | volume = 299 | issue = 9 | pages = 1063–1065 | date = March 2008 | pmid = 18319418 | doi = 10.1001/jama.299.9.1063 }}</ref>

== See also ==
{{div col|colwidth=20em}}
* [[Bioisostere]]
* [[Bioinformatics]]
* [[Cheminformatics]]
* [[Drug development]]
* [[Drug discovery]]
* [[List of pharmaceutical companies]]
* [[Medicinal chemistry]]
* [[Molecular design software]]
* [[Molecular modification]]
* [[Retrometabolic drug design]]
{{Div col end}}

== References ==
{{Reflist|colwidth=35em}}

== External links ==
* {{MeshName|Drug+Design}}
* [Drug Design Org](https://www.drugdesign.org/chapters/drug-design/)

{{Drug design}}
{{Design}}
{{Medicinal chemistry}}

[[Category:Design of experiments]]
[[Category:Drug discovery]]
[[Category:Medicinal chemistry]]