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132 4 Bioisosteric Replacement for Drug Discovery Supported by the SwissBioisostere Database
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Figure 4.19 Reducing aromaticity to increase metabolic stability. Replacing phenyl with
bicyclo[1.1.1]pentane bioisostere was successfully applied to reduce amide hydrolysis
liability of IDO1 inhibitors [67].
The amount of available data is much smaller for scaolds, but the trends
observed for linkers and side chains are again followed. 120 replacements lower
atness, involving a total of 51 aromatic moieties exchanged by 58 nonaromatic
rings. The most frequently replaced aromatic scaolds include phenyls, pyridines,
and thiophene (Figure 4.20a). The 1,3,4-phenyl and 1,3,5-phenyl replacements (in
Figure 4.20b, c, respectively) are purely carbon cycles and bridged systems, with the
exception of 2,5,5-trisubstituted oxane.
(a) (c)
Figure 4.20 Reducing aromaticity of scaffolds found in SwissBioisostere.(a)Aromatic
scaffolds most frequently replaced by nonaromatic cyclic fragments (number of occurrences
in parenthesis). (b) Most frequent replacements reducing aromaticity for 1,3,4-phenyl, with
number of occurrences in parenthesis. (c) Most frequent replacements reducing aromaticity
for 1,3,5-phenyl, with number of occurrences in parenthesis.
(b)
References 133
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4.5 Conclusive Remarks
SwissBioisostere consists in a knowledge and structural database of molecular
replacements built on highly curated data extracted from ChEMBL. The MMP
engine was used to dene molecular replacements in pairs of relevant small
molecules based on chemical structure and bioactivity only. Although not part
of the denition of replacements, other related informations have been stored in
the database, like molecular and physicochemical properties, which are important
to assist medicinal chemists and other drug discovery experts in their hit nding
and lead optimization endeavors. Because universal rules cannot be applied to
guarantee a replacement to be bioisosteric, well-dened chemical and biological
contexts of the replacement are critical to selecting the most appropriate molecular
exchanges for a distinct objective in each setting. With these practical considerations
in mind, a user-friendly web interface was developed and made accessible at www
.swissbioisostere.ch. This free website does not require any login information, and
the output is provided under CC-BY license to ensure anyone can benet from this
broad wealth of accurate information and to enable freedom to operate in various
environments, including for for-prot or commercial applications.
The examples and use cases described in this chapter illustrate how SwissBioisostere can support drug discovery complementarily to other web tools
developed under the umbrella of the SwissDrugDesign project (https://www
.molecular-modelling.ch/swiss-drug-design.html).
Acknowledgment
The authors are deeply grateful to SIB, Swiss Institute of Bioinformatics (www.sib
.swiss) for supporting the SwissDrugDesign project. We would like to thank Prof.
Olivier Michielin for the useful discussions as well as Wiley-VCH for the kind invitation to present our work. Chemaxon is acknowledged for the licensing agreement.
The backend of the SwissBioisostere website (www.swissbioisostere.ch) involves
MarvinJS and JChem microservices for some calculations (www.chemaxon.com).
References
1 Brown, N. (2012). Bioisosteres in Medicinal Chemistry. Wiley.
2 Meanwell, N.A. (2011). Synopsis of some recent tactical application of
bioisosteres in drug design. Journal of Medicinal Chemistry 54 (8): 2529–2591.
3 Patani, G.A. and LaVoie, E.J. (1996). Bioisosterism: a rational approach in drug
design. Chemical Reviews 96 (8): 3147–3176.
4 Lipinski, C.A. (1986). Chapter 27. bioisosterism in drug design. Annual Reports
in Medicinal Chemistry 21: 283–291.
5 Langmuir, I. (1919). The structure of atoms and the octet theory of valence.
PNAS 5 (7): 252–259.
134 4 Bioisosteric Replacement for Drug Discovery Supported by the SwissBioisostere Database
https://t.me/medicina_free
6 Erlenmeyer, H. and Leo, M. (1932). Über pseudoatome. Helvetica Chimica Acta
15 (1): 1171–1186.
7 Friedman, H. (1951). Inuence of isosteric replacements upon biological activity.
NASNRS 206: 295–358.
8 Thornber, C.W. (1979). Isosterism and molecular modication in drug design.
Chemical Society Reviews 8 (4): 563.
9 Carini, D.J., Duncia, J.V., Aldrich, P.E. et al. (1991). Nonpeptide angiotensin II
receptor antagonists: the discovery of a series of N-(biphenylylmethyl)imidazoles
as potent, orally active antihypertensives. Journal of Medicinal Chemistry 34 (8):
2525–2547.
10 Arrowsmith, J.E., Campbell, S.F., Cross, P.E. et al. (1986). Long-acting dihydropy-
ridine calcium antagonists. 1. 2-Alkoxymethyl derivatives incorporating basic
substituents. Journal of Medicinal Chemistry 29 (9): 1696–1702.
11 Stepanovs, D. and Mishnev, A. (2012). Molecular and crystal structure of silde-
nal base. Zeitschrift für Naturforschung Part B 67 (5): 491–494.
12 Li, Y., Zhang, L., Yang, R. et al. (2022). Discovery of 3,4-dihydrobenzo[f][1,4]
oxazepin-5(2H)-one derivatives as a new class of selective TNIK inhibitors and
evaluation of their anti-colorectal cancer eects. Journal of Medicinal Chemistry
65 (3): 1786–1807.
13 Ballatore, C., Huryn, D.M., and Smith, A.B. (2013). Carboxylic acid (bio)isosteres
in drug design. ChemMedChem 8 (3): 385–395.
14 Lassalas, P., Gay, B., Lasfargeas, C. et al. (2016). Structure property relationships
of carboxylic acid isosteres. Journal of Medicinal Chemistry 59 (7): 3183–3203.
15 Plé, P.A., Green, T.P., Hennequin, L.F. et al. (2004). Discovery of a new class of
anilinoquinazoline inhibitors with high anity and specicity for the tyrosine
kinase domain of c-Src. Journal of Medicinal Chemistry 47 (4): 871–887.
16 Sehnal, D., Bittrich, S., Deshpande, M. et al. (2021). Mol* Viewer: modern web
app for 3D visualization and analysis of large biomolecular structures. Nucleic
Acids Research 49 (W1): W431–W437.
17 Bunch, L. (2013). Bioisosteres in medicinal chemistry. Edited by Nathan Brown.
ChemMedChem 8 (6): 1012–1012.
18 Langdon, S.R., Ertl, P., and Brown, N. (2010). Bioisosteric replacement and scaf-
fold hopping in lead generation and optimization. Molecular Informatics 29 (5):
366–385.
19 Hu, Y., Stumpfe, D., and Bajorath, J. (2017). Recent advances in scaold hopping.
Journal of Medicinal Chemistry 60 (4): 1238–1246.
20 Schneider, G., Neidhart, W., Giller, T., and Schmid, G. (1999).
“Scaold-Hopping” by topological pharmacophore search: a contribution to
virtual screening. Angewandte Chemie (International Ed. in English) 38 (19):
2894–2896.
21 Hughes, J.P., Rees, S., Kalindjian, S.B., and Philpott, K.L. (2011). Principles of
early drug discovery. British Journal of Pharmacology 162 (6): 1239–1249.
22 Mendez, D., Gaulton, A., Bento, A.P. et al. (2019). ChEMBL: towards direct depo-
sition of bioassay data. Nucleic Acids Research 47 (D1): D930–D940.
References 135
https://t.me/medicina_free
23 Gaulton, A., Hersey, A., Nowotka, M. et al. (2017). The ChEMBL database in
2017. Nucleic Acids Research 45 (D1): D945–D954.
24 Kenny, P.W. and Sadowski, J. (2005). Chemoinformatics in drug discovery. In:
Methods and Principles in Medicinal, 271–285. Weinheim: Wiley-VCH.
25 Dossetter, A.G., Grien, E.J., and Leach, A.G. (2013). Matched molecular pair
analysis in drug discovery. Drug Discovery Today 18 (15–16): 724–731.
26 Tyrchan, C. and Evertsson, E. (2017). Matched molecular pair analysis in short:
algorithms, applications and limitations. Computational and Structural Biotech-
nology Journal 15: 86–90.
27 Hussain, J. and Rea, C. (2010). Computationally ecient algorithm to identify
matched molecular pairs (MMPs) in large data sets. Journal of Chemical Infor-
mation and Computer Sciences 50 (3): 339–348.
28 Cuozzo, A., Daina, A., Perez, M.A.S. et al. (2021). SwissBioisostere 2021: updated
structural, bioactivityand physicochemical data delivered by a reshapedweb interface. Nucleic Acids Research 50 (D1): D1382–D1390.
29 Wirth, M., Zoete, V., Michielin, O., and Sauer, W.H.B. (2013). SwissBioisostere: a
database of molecular replacements for ligand design. Nucleic Acids Research 41
(D1): D1137–D1143.
30 Pinto, D.J.P., Orwat, M.J., Quan, M.L. et al. (2006). 1-[3-Aminobenzisoxazol-
′
-yl]-3-triuoromethyl-6-[2′-(3-(R)-hydroxy-N-pyrrolidinyl)methyl-[1,1′]-
5
biphen-4-yl]-1,4,5,6-tetrahydropyrazolo-[3,4-c]-pyridin-7-one (BMS-740808)
a highly potent, selective, ecacious, and orally bioavailable inhibitor of
blood coagulation factor Xa. Bioorganic & Medicinal Chemistry Letters 16 (15):
4141–4147.
31 Bragina, M.E., Daina, A., Perez, M.A.S. et al. (2022). The SwissSimilarity 2021
web tool: novel chemical libraries and additional methods for an enhanced
ligand-based virtual screening experience. International Journal of Molecular
Sciences 23 (2): 811.
32 Daina, A., Michielin, O., and Zoete, V. (2019). SwissTargetPrediction: updated
data and new features for ecient prediction of protein targets of small
molecules. Nucleic Acids Research 47 (W1): W357–W364.
33 Daina, A., Michielin, O., and Zoete, V. (2017). SwissADME: a free web tool to
evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of
small molecules. Scientic Reports 7: 42717.
34 Thamilselvan, G., Sarveswari, H.B., Vasudevan, S. et al. (2021). Development
of an antibiotic resistance breaker to resensitize drug-resistant Staphylococcus
aureus: in silico and in vitro approach. Frontiers in Cellular and Infection Microbiology 11: 700198.
35 Chen, M., Li, Z., Shao, X., and Maiensch, P. (2022). Bioisosteric-replacement-
driven lead optimization of tyclopyrazoor. Journal of Agricultural and Food
Chemistry https://doi.org/10.1002/cbdv.200790032.
36 Turner, S.R., Strohbach, J.W., Tommasi, R.A. et al. (1998). Tipranavir
(PNU-140690): a potent, orally bioavailable nonpeptidic HIV protease inhibitor
136 4 Bioisosteric Replacement for Drug Discovery Supported by the SwissBioisostere Database
https://t.me/medicina_free
of the 5,6-Dihydro-4-hydroxy-2-pyrone sulfonamide class. Journal of Medicinal
Chemistry 41 (18): 3467–3476.
37 Lovering, F., Bikker, J., and Humblet, C. (2009). Escape from atland: increasing
saturation as an approach to improving clinical success. Journal of Medicinal
Chemistry 52 (21): 6752–6756.
38 Hernandes, M.Z., Cavalcanti, S.M.T., Moreira, D.R.M. et al. (2010). Halogen
atoms in the modern medicinal chemistry: hints for the drug design. Current
Drug Targets 11 (3): 303–314.
39 Meanwell, N.A. (2018). Fluorine and uorinated motifs in the design and appli-
cation of bioisosteres for drug design. Journal of Medicinal Chemistry 61 (14):
5822–5880.
40 Sauer, W.H.B. and Schwarz, M.K. (2003). Size doesn’t matter: scaold diversity,
shape diversity and biological activity of combinatorial libraries. Chimia 57 (5):
276.
41 Sauer, W.H.B. and Schwarz, M.K. (2003). Molecular shape diversity of com-
binatorial libraries: a prerequisite for broad bioactivity †. Journal of Chemical
Information and Computer Sciences 43 (3): 987–1003.
42 Irwin, J.J., Tang, K.G., Young, J. et al. (2020). ZINC20-A free ultralarge-scale
chemical database for ligand discovery. Journal of Chemical Information and
Modeling 60 (12): 6065–6073.
43 Kumari, S., Carmona, A.V., Tiwari, A.K., and Trippier, P.C. (2020). Amide bond
bioisosteres: strategies, synthesis, and successes. Journal of Medicinal Chemistry
63 (21): 12290–12358.
44 Pinto, D.J.P., Orwat, M.J., Koch, S. et al. (2007). Discovery of
1-(4-methoxyphenyl)-7-oxo-6-(4-(2-oxopiperidin-1-yl)phenyl)-4,5,6,7-tetrahydro1H-pyrazolo[3,4-c]pyridine-3-carboxamide (apixaban, BMS-562247), a highly
potent, selective, ecacious, and orally bioavailable inhibitor of blood coagulation factor Xa. Journal of Medicinal Chemistry 50 (22): 5339–5356.
45 Zoete, V., Daina, A., Bovigny, C., and Michielin, O. (2016). SwissSimilarity: a web
tool for low to ultra high throughput ligand-based virtual screening. Journal of
Chemical Information and Computer Sciences 56 (8): 1399–1404.
46 Daina, A. and Zoete, V. (2019). Application of the SwissDrugDesign online
resources in virtual screening. International Journal of Molecular Sciences,
Vol. 13, Pages 1805-1831 20 (18): 4612.
47 Testa, B. and Krämer, S.D. (2007). The biochemistry of drug metabolism – an
introduction. Chemistry & Biodiversity 4 (3): 257–405.
48 Brenk, R., Schipani, A., James, D. et al. (2008). Lessons learnt from assembling
screening libraries for drug discovery for neglected diseases. ChemMedChem 3
(3): 435–444.
49 Bruns, R.F. and Watson, I.A. (2012). Rules for identifying potentially reactive or
promiscuous compounds. Journal of Medicinal Chemistry 55 (22): 9763–9772.
50 Baell, J.B. and Holloway, G.A. (2010). New substructure lters for removal of
pan assay interference compounds (PAINS) from screening libraries and for their
exclusion in bioassays. Journal of Medicinal Chemistry 53 (7): 2719–2740.
References 137
https://t.me/medicina_free
51 Wan, H. and Holmen, A. (2009). High throughput screening of physicochemical
properties and in vitro ADME proling in drug discovery. Combinatorial Chem-
istry and High Throughput Screening 12 (3): 315–329.
52 Hop, C.E.C.A. (2011) Encyclopedia of Drug Metabolism and Interactions. 1–43.
https://doi.org/10.1002/9780470921920.edm049
53 Daina, A. and Zoete, V. (2016). A BOILED-Egg to predict gastrointestinal
absorption and brain penetration of small molecules. ChemMedChem 11 (11):
1117–1121.
54 Huang, W.-S., Metcalf, C.A., Sundaramoorthi, R. et al. (2010). Discovery of
3-[2-(imidazo[1,2-b]pyridazin-3-yl)ethynyl]-4-methyl-N-{4-[(4-methylpiperazin1-yl)methyl]-3-(triuoromethyl)phenyl}benzamide (AP24534), a potent, orally
active pan-inhibitor of breakpoint cluster region-abelson (BCR-ABL) kinase
including the T315I gatekeeper mutant. Journal of Medicinal Chemistry 53 (12):
4701–4719.
55 Wildman, S.A. and Crippen, G.M. (1999). Prediction of physicochemical param-
eters by atomic contributions. Journal of Chemical Information and Computer
Sciences 39 (5): 868–873.
56 Ertl, P., Rohde, B., and Selzer, P. (2000). Fast calculation of molecular polar sur-
face area as a sum of fragment-based contributions and its application to the
prediction of drug transport properties. Journal of Medicinal Chemistry 43 (20):
3714–3717.
57 Veber, D.F., Johnson, S.R., Cheng, H.-Y. et al. (2002). Molecular properties that
inuence the oral bioavailability of drug candidates. Journal of Medicinal Chem-
istry 45 (12): 2615–2623.
58 Muegge, I., Heald, S.L., and Brittelli, D. (2001). Simple selection criteria
for drug-like chemical matter. Journal of Medicinal Chemistry 44 (12):
1841–1846.
59 Ritchie, T.J., Ertl, P., and Lewis, R. (2011). The graphical representation of
ADME-related molecule properties for medicinal chemists. Drug Discovery Today
16 (1–2): 65–72.
60 Venkatesan, A.M., Davis, J.M., Grosu, G.T. et al. (2004). Synthesis and
structure−activity relationships of 4-alkynyloxy phenyl sulfanyl, sulnyl, and
sulfonyl alkyl hydroxamates as tumor necrosis factor-α converting enzyme and
matrix metalloproteinase inhibitors. Journal of Medicinal Chemistry 47 (25):
6255–6269.
61 Ritchie, T.J. and Macdonald, S.J.F. (2009). The impact of aromatic ring count on
compound developability–are too many aromatic rings a liability in drug design?
Drug Discovery Today 14 (21–22): 1011–1020.
62 Joubert, J., Geldenhuys, W.J., Van der Schyf, C.J. et al. (2012). Polycyclic cage
structures as lipophilic scaolds for neuroactive drugs. ChemMedChem 7(3):
375–384.
63 Tse, E.G., Houston, S.D., Williams, C.M. et al. (2020). Nonclassical phenyl
bioisosteres as eective replacements in a series of novel open-source antimalarials. Journal of Medicinal Chemistry 63 (20): 11585–11601.
138 4 Bioisosteric Replacement for Drug Discovery Supported by the SwissBioisostere Database
https://t.me/medicina_free
64 Wilkinson, S.M., Gunosewoyo, H., Barron, M.L. et al. (2014). The rst
CNS-Active carborane: a novel P2X 7 receptor antagonist with antidepressant
activity. ACS Chemical Neuroscience 5 (5): 335–339.
65 Measom, N.D., Down, K.D., Hirst, D.J. et al. (2016). Investigation of a bicy-
clo[1.1.1]pentane as a phenyl replacement within an LpPLA 2 inhibitor. ACS
Medicinal Chemistry Letters 8 (1): 43–48.
66 Zhao, J.-X., Chang, Y.-X., He, C. et al. (2021). 1,2-Difunctionalized bicy-
clo[1.1.1]pentanes: long–sought-after mimetics for ortho/meta-substituted arenes.
Proceedings of the National academy of Sciences 118 (28): e2108881118.
67 Pu, Q., Zhang, H., Guo, L. et al. (2020). Discovery of potent and orally available
Bicyclo[1.1.1]pentane-derived indoleamine-2,3-dioxygenase 1 (IDO1) inhibitors.
ACS Medicinal Chemistry Letters 11 (8): 1548–1554.
Part II
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Macromolecular Targets and Diseases
139
5
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The Protein Data Bank (PDB) and Macromolecular Structure
Data Supporting Computer-Aided Drug Design
David Armstrong1, John Berrisford2, Preeti Choudhary1, Lukas Pravda3,James
Tol c h ard
1
Protein Data Bank in Europe, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
2
AstraZeneca, AstraZeneca Academy House, 136 Hills Rd, Cambridge, CB2 8PA, UK
3
Exscientia, The Schrodinger Building, Oxford Science Park, Oxford, Oxfordshire, United Kingdom, OX4 4GE
4
Centre de RMN à Très Hauts Champs de Lyon, Claude Bernard University Lyon 1, 5 rue de la Doua, 69100
Villeurbanne, France
4
, Mihaly Varadi1, and Sameer Velankar
1
5.1 Introduction
The Protein Data Bank (PDB) [1], managed by the global Worldwide PDB (wwPDB)
consortium [2], is one of the oldest scientic databases in life sciences. The PDB
archives structural models of biological macromolecules, derived from experimental
data. The wwPDB partners, the Research Collaboratory for Structural Bioinformatics (RCSB) [3], Protein Data Bank in Europe (PDBe) [4], Protein Data Bank Japan
(PDBj) [5], Electron Microscopy Data Bank (EMDB) [6], and Bio Mag Res Bank
(BMRB) [7], manage the PDB archive based on the FAIR principles [8], ensuring
structural biology data are Findable, Accessible, Interoperable and Reusable.
The PDB archive contains over 200,000 structures of proteins, DNA, and RNA, and
their complexes with small molecules, such as cofactors and inhibitors. This data,
along with related metadata and experimental data, are stored in the PDB archive in
PDBx/mmCIF formatted les [9]. Since 2019, it has been mandatory for X-ray crystallographic structures to be deposited in the PDBx/mmCIF format [10], ensuring
maximum capture and validation of metadata for these structures.
The PDB is a vital resource for computer-aided drug design and contains structural information for large numbers of drugs, with 5494 unique ligands in the PDB
mapped to entries in DrugBank [11]. Additionally, there are numerous potential
drug targets as well as structures containing macromolecular drug targets. This data
are of paramount importance in determination of key drug binding sites in macromolecules [12], and in understanding existing binding modes to support designs of
novel compounds to target these binding sites.
In addition to the three-dimensional (3D) coordinate information archived in
the PDB, standardization of polymer sequences and chemical identiers in PDB
entries facilitates the integration of additional metadata from external resources
141
Open Access Databases and Datasets for Drug Discovery, First Edition.
Edited by Antoine Daina, Michael Przewosny, and Vincent Zoete.
© 2024 WILEY-VCH GmbH. Published 2024 by WILEY-VCH GmbH.
142 5 The Protein Data Bank (PDB) and Macromolecular Structure Data Supporting Computer
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[13, 14]. This additional biological and chemical information is vitally important in
understanding the scientic context of the structures in the PDB, which is required
to determine functional information.
As part of the European Bioinformatics Institute (EMBL-EBI), the PDBe [4] is
uniquely placed to integrate macromolecular structure data from partner resources
at EBI and beyond. This allows the integration of relevant biological and chemical
data to better demonstrate the functional importance of structures in the PDB. The
PDBe-Knowledge Base (PDBe-KB) [15] builds upon this, with partnerships extending across the structural bioinformatics community, allowing the integration of even
more data related to macromolecular structure and function. This includes a large
number of partners involved in cheminformatics who provide data relating to small
molecules and their macromolecular binding sites, including canSAR [16], 3DLigandSite [17], P2Rank [18], and many more, detailed at pdbe-kb.org/partners.
The wwPDB, by adhering to the FAIR principles, ensures that all PDB data are provided freely, with no limits upon its use. All PDBe and PDBe-KB tools and resources
are also free to use, with scripts and software pipelines made open source wherever
possible. In addition to access through the PDBe [4] and PDBe-KB [15] websites, all
PDBe data are made available through publicly accessible APIs [19, 20], while much
of this data is also available via a distributed PDBe knowledge graph [20].
This chapter will introduce the type of data available in the PDB, how it is organized and curated, and how it can be used to support drug design. It will also give
an overview of the tools and resources at PDBe and PDBe-KB, highlighting how
these can support understanding of drug binding and function in PDB structures to
improve research within this area.
5.2 Small Molecule Data in Protein Data Bank (PDB)
Entries
5.2.1 What Data are in the PDB Archive?
The PDB is the single global archive of experimentally determined 3D structure
data of biological macromolecules. Each PDB structure must contain a polymeric
entity, i.e. protein or nucleic acid, however, information can also be included for all
non-polymeric ligands within the structure.
The atomic coordinates of a PDB entry are built with a specic hierarchy, ensuring
that each molecule and its constituent parts can be clearly identied. The smallest
component of the hierarchy is at the atomic level, with each separate coordinate line
in the archive le describing the specic position of an atom in 3D space. Each atom
is dened as part of a larger chemical component or residue, dened by a 3-character
ID code.
These chemical components can be either individual bound ligands in the structure or individual residues within a polymeric molecule, with each dened by a
unique identier (asym or chain ID) and numbering for clear identication. Beyond
each specic polymeric chain ID, each unique and individual molecule in the structure is also given a specic “entity” identier, which is used to link all the metadata
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