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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5440_Библиотеки_им_академика_М_И_Перельмана.pdf
8.6 ase Studies 183
8.6 Case Studies
8.6.1 Case 1
The X-linked inhibitor of apoptosis protein (XIAP) belongs to the inhibitor of apoptosis protein (IAP)
family and is responsible for counteracting the activity of caspases-3, caspases-7, and caspases-9. The
upregulation of the protein led to a reduction in the occurrence of apoptosis within the cellular envi-
ronment, thereby impeding the progression of cancer. Various classes of XIAP antagonists are com-
monly employed to rectify the impaired apoptotic pathway, hence facilitating the eradication of cancer
within organisms. The currently identified chemically synthesized substances that function as XIAP
inhibitors have been found to have adverse effects, hence posing challenges in the context of chemo-
therapy treatment. The project was undertaken to identify novel natural chemicals capable of induc-
ing apoptosis by activating caspases while exhibiting low toxicity. Therefore, to find natural compounds,
a structure-based pharmacophore model was constructed for the protein’s active site cavity. This was
followed by VS, molecular docking, and MDs simulation. In the initial stage, a total of seven hit com-
pounds were obtained. Subsequently, a molecular docking approach was employed to assess the
compounds, resulting in the selection of four compounds for subsequent examination. The stability of
the chosen drug candidate to the target protein was validated using the MDs simulation technique,
which successfully demonstrated the stability of all three compounds. According to the results, three
recently acquired compounds, specifically Caucasicoside A (ZINC77257307), Polygalaxanthone III
(ZINC247950187), and MCULE-9896837409 (ZINC107434573), have been identified as potential lead
compounds for combating XIAP-related cancer treatment. The compounds that were chosen for anal-
ysis exhibited a greater degree of binding affinity, with values ranging from −6.9 to −8.0 kcal/mol,
toward the XIAP protein of interest. The in silico toxicity test revealed a reduced level of toxicity, while
the ADME study indicated that the substance can be rapidly absorbed by the tissue site due to its high-
fat solubility. The study commenced by constructing a structure-based pharmacophore model, which
was subsequently employed for VS, molecular docking, ADMET analysis, and MD simulation. During
the final step of the MD simulation, four compounds were examined. However, it was seen that the
compound ZINC1070004335 exhibited unfavorable stability with the protein XIAP. As a result, this
compound was rejected. The A-to-Z VS technique may yield three prominent natural compounds that
have the potential to function as lead molecules in the battle against cancer [112]. All the details of this
case study are mainly depicted in Figures 8.4–8.6.
8.6.2 Case 2
The present study employed an innovative computational methodology that combined ligand-
based pharmacophore filtering and molecular docking approaches to discover prospective com-
pounds with the ability to simultaneously inhibit the tyrosine kinase activities of both EGFR and
VEGFR2. The results of the study revealed that six compounds demonstrated a strong alignment
with the characteristics of the designated pharmacophore models. These compounds also showed
higher docking scores when compared to erlotinib and axitinib, which were used as reference
medications. Moreover, a comprehensive examination of the binding mechanisms revealed that
these compounds exhibited analogous interactions with the reference medications. Nevertheless,
by conducting a thorough analysis of the stability of their binding modes during MDs simulations,
it was revealed that two distinct compounds, namely, ZINC16525481 and ZINC38484632, exhib-
ited consistent hydrogen bonding interactions (with an occupancy surpassing 50%) with essential
residues of both targets. Moreover, these compounds demonstrated favorable binding free ener-
gies, suggesting their capacity to strongly bind to the binding pockets of both EGFR and VEGFR2,
184
LEU292A
LEU307A
TRP310A
GLU314A
ASP309A
HOH523A
HOH565A
HOH556A
THR308A
F
HO
O
N
N
NH
N
N
O
MET248B
TRP323A
Figure 8.5 The pharmacophore modeling yielded a 2D representation highlighting the hydrophobic
interactions, depicted in yellow, involving specific amino acid residues within the XIAP protein. The
prevalent HBD properties engaged in ligand–protein interactions are delineated in green. The coloration
reflects the interactions between HBAs and the oxygen and nitrogen atoms of the benzene ring, along with
its diverse side chains. Notably, the depiction does not encapsulate the morphology and positioning of the
binding pocket, which are upheld by hydrogen atoms and delimited regions.
hence having a potent inhibitory impact. Consequently, the study proposed that ZINC16525481
and ZINC38484632 exhibit promise as prospective contenders for the simultaneous suppression of
EGFR and VEGFR2. This finding underscores the need for additional research and exploration in
future investigations [113]. All above explanations are depicted in Figures 8.7 and 8.8.
(a) (b)
Figure 8.4 (a) The pharmacophore model of XIAP protein bound to the 46781908 ligands, derived from
the crystallographic structure of XIAP protein (PDB ID: 5OQW), is founded on its 3D conformation.
(b) Following intricate molecular interactions, multiple pharmacophore attributes are delineated by four
yellow spherical representations. The interaction between the protein–ligand complex is typified by the
presence of a hydrophobic interaction, depicted by a blue star shape indicating a positively ionizable group
with a tolerance of 2Å. Moreover, three red arrows and spherical representations signify HBAs with a
tolerance of 1.5Å. In addition, five HBDs are identified within the interaction, depicted by green spherical or
arrow shapes. Notably, the schematic illustration does not encompass the 15 exclusion volumes generated
during pharmacophore modeling.
8.6 ase Studies 185
115 hits
of 5209 total compounds
(10 actives, 5199 decoys)
AUC
1;5;10;100%
:0.98;1.00;1.00;0.54
EF
1;5;10;100%
: 10.0;4.5;4.5;4.5
100.0%
100.0%
80.0%
80.0%
60.0%
60.0%
40.0%
40.0%
1 - Specicity (% selected decoys)
20.0%
20.0%
Sensitivity (% selected ligands)
Figure 8.6 A receiver operating characteristic (ROC) curve was generated to evaluate the discriminative
capability of the active molecule in distinguishing decoy compounds, employing the structure-based
pharmacophore model. The validation of the pharmacophore model was conducted using a dataset
comprising 10 XIAP active compounds and 5199 decoy compounds.
100.0%
(a) (b)
80.0%
60.0%
40.0%
20.0%
Sensitivity (% selected ligands)
100.0%
80.0%
60.0%
40.0%
20.0%
Sensitivity (% selected ligands)
100.0%80.0%60.0%40.0%
1 - Specicity (% selected decoys)
20.0%
100.0%80.0%60.0%40.0%
1 - Specicity (% selected decoys)
20.0%
3406 hits851 hits
of 36241 total compounds
(830 actives, 35411 decoys)
of 25870 total compounds
(620 actives, 25250 decoys)
AUC
1;5;10;100%
:0.99;1.00;1.00;0.78
EF
1;5;10;100%
:34.9;24.1;24.1;24.1
AUC
1;5;10;100%
:0.82;0.82;0.80;0.73
EF
1;5;10;100%
:3.4;3.2;5.0;4.4
Figure 8.7 A receiver operating characteristic (ROC) curve was generated to evaluate the discriminative
capability of the active molecule in distinguishing decoy compounds, employing the structure-based
pharmacophore model. The validation of the pharmacophore model was conducted using a dataset
comprising 10 XIAP active compounds and (a) 35411 and (b) 25250 decoy compounds.
186
Aromatic ring
(a) (b)
Hydrophobic intraction Hydrogen bond acceptor Hydrogen bond donor
Figure 8.8 Erlotinib (a) and axitinib (b) are superimposed onto the selected pharmacophore model of
EGFR. These compounds serve as inhibitors of VEGFR2.
8.7 Challenges in Pharmacophore Modeling
Pharmacophore modeling encounters several challenges that require resolution to enhance mod-
eling quality [114].
One application area of pharmacophore modeling is VS using pharmacophores. However, effec-
tive scoring functions are deficient for this purpose [115]. Typically, the degree of alignment
between a ligand and the pharmacophore query is expressed through RMSD, which assesses the
similarity between the query patterns and the compound’s atoms. Unfortunately, this measure-
ment doesn’t consider the likeness to known inhibitors, making it incapable of estimating the
overall similarity with the receptor. Consequently, compounds matching the pharmacophore
query might differ from known inhibitors and contain functional groups unable to bind to the
receptor-binding site, rendering them inactive despite perfect matches [116].
Another formidable challenge in pharmacophore-based VS is the presence of higher “false
positive” rates, where virtual hit ligands may lack biological activity [70]. This limitation can
be attributed to insufficient hypothesis quality, the pharmacophore model’s accuracy, and devia-
tions from actual biological conditions. To overcome this drawback, it’s essential to incorporate
expertise, comprehensive validation, including relevant target information, and integration with
other computational methods [12].
Modeling ligand flexibility presents another significant challenge. To address this, structural
analysis based on predetermined structure databases or during the pharmacophore modeling pro-
cess may be employed. Notably, the method reliant on predetermined structural databases has
demonstrated superior performance [62]. However, limitations exist in VS by pharmacophore
using this database-dependent approach. These databases typically contain only a few low-energy
structures per molecule, potentially missing the structure of an active ligand. This is especially
relevant for structures with rotatable bonds in small molecular functional groups like hydroxyl,
where distinguishing between various rotations based on RMSD value differences during structure
generation can be challenging [117]. In general, pharmacophore search tools can account for bond
8.8 onclusion 187
rotations during the matching process to identify the correct directional conformations of small,
flexible, polar functional groups. Nevertheless, creating a pharmacophore query remains a
challenge without a clearly defined approach [118].
Similarly, in structure-based pharmacophore modeling, addressing protein flexibility and ligand
conformational flexibility poses major challenges. These challenges can be mitigated by generating
the pharmacophore model from a docked complex created through flexible docking or by generat-
ing and aligning models from protein-ligand MDs simulations simultaneously. Combining the
structure-based approach with flexible docking and MDs simulations may alleviate these limita-
tions [119]. In addition, the generation of pharmacophore models in the structure-based approach
is not straightforward, particularly when various combinations of features are possible. Each phar-
macophore model may lead to a different set of compounds [120].
Molecular alignment is a complex aspect of pharmacophore modeling and can be categorized as
point-based or feature-based approaches based on their fundamental nature. Point-based algo-
rithms overlap double atoms, fragments, or chemical pattern points using least squares matching.
However, a major drawback of this approach is the requirement for predetermined connection
points. Feature-based algorithms, on the other hand, employ molecular domain determinants,
often represented by Gaussian function sets, to create alignments. Ongoing developments aim to
introduce new alignment methods [42].
Another practical challenge lies in the selection of the appropriate training set molecules. While
nontechnical, this issue can perplex users, and the choice of ligand molecule type, dataset size, and
chemical diversity has been shown to significantly influence the final pharmacophore model
generated [70].
8.8 Conclusion
The term “pharmacophore” refers to a 3D configuration of chemical attributes that are essential
for the biological functionality of a given molecule. Different software applications are deployed in
the construction of pharmacophore models, which are then used to identify new compounds that
meet the required pharmacophoric criteria, showing potential for biological activity.
The application of pharmacophore modeling is widely beneficial throughout multiple stages of
the drug discovery process. VSs have gained significant popularity due to their ability to aid in the
discovery of chemicals that can produce the appropriate biological reactions. Pharmacophore
models are utilized as efficient tools to discriminate compounds that meet pharmacophoric criteria
before, during, and after docking simulations. Furthermore, these entities assume crucial func-
tions in several activities, such as drug target prediction, ligand screening, and the anticipation of
ADMET features.
The inclusion of modern computational tools has aided the overcoming of inherent problems in
pharmacophore modeling. Significantly, the combination of pharmacophore modeling and MDs
simulations has promise in addressing challenges arising from ligand flexibility. Furthermore, the
issue of inadequate scoring functions employed in VS through pharmacophore methods presents
a significant problem, which could potentially be mitigated by the application of machine learning
techniques. Therefore, current developments in pharmacophore modeling offer the potential to
generate models with enhanced characteristics.
In conclusion, pharmacophore modeling plays a crucial role in the field of drug discovery. The
improvement of computing capabilities, increased access to data, integration with other computa-
tional approaches, and the use of advanced algorithms have all contributed to the enhancement of
188
the quality of pharmacophore models that are developed. As the development and refinement of
these models progress, their capacity to enhance drug discovery is positioned for additional growth.
Therefore, it is crucial to continue the efforts in improving pharmacophore models to further drug
development activities.
Acknowledgments
The authors are thankful to the Department of Pharmaceutical Sciences and Technology, Birla
Institute of Technology, Mesra, Ranchi, Jharkhand, India, for facilitating the required infrastruc-
ture, and computing facilities to carry out the chemical stimulations.
References
1 Deore, A.B., Dhumane, J.R., Wagh, R., and Sonawane, R. (2019). The stages of drug discovery and
development process. Asian Journal of Pharmaceutical Research and Development 7 (6): 62–67.
2 Sliwoski, G., Kothiwale, S., Meiler, J., and Lowe, E.W. (2014). Computational methods in drug
discovery. Pharmacological Reviews 66 (1): 334–395.
3 Le, A.V., Phan, T.C.Q., and Nguyen, T.H. (2015). In silico drug design: prospective for drug lead
discovery. International Journal of Engineering and Science Invention 4 (10): 2319–6726.
4 Macalino, S.J.Y., Gosu, V., Hong, S., and Choi, S. (2015). Role of computer-aided drug design in
modern drug discovery. Archives of Pharmacal Research 38: 1686–1701.
5 Prachayasittikul, V., Worachartcheewan, A., Shoombuatong, W. et al. (2015). Computer-aided drug
design of bioactive natural products. Current Topics in Medicinal Chemistry 15 (18): 1780–1800.
6 Langer, T. and Hoffmann, R.D. (2006). Pharmacophore modelling: applications in drug discovery.
Expert Opinion on Drug Discovery 1 (3): 261–267.
7 Mason, J.S., Good, A.C., and Martin, E.J. (2001). 3-D pharmacophores in drug discovery. Current
Pharmaceutical Design 7 (7): 567–597.
8 Guner, O.F. (2002). History and evolution of the pharmacophore concept in computer-aided drug
design. Current Topics in Medicinal Chemistry 2 (12): 1321–1332.
9 Kaalia, R., Kumar, A., Srinivasan, A., and Ghosh, I. (2015). An ab initio method for designing
multi-target specific pharmacophores using complementary interaction field of aspartic proteases.
Molecular Informatics 34 (6–7): 380–393.
10 McGregor, M.J. and Muskal, S.M. (1999). Pharmacophore fingerprinting. 1. Application to QSAR
and focused library design. Journal of Chemical Information and Computer Sciences 39 (3): 569–574.
11 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.
12 Bradshaw, R.A. and Stahl, P.D. (2015). Encyclopedia of Cell Biology. Academic Press.
13 Khedkar, S.A., Malde, A.K., Coutinho, E.C., and Srivastava, S. (2007). Pharmacophore modeling in
drug discovery and development: an overview. Medicinal Chemistry (Los Angeles) 3 (2): 187–197.
14 Ansari, F., Ghasemi, J.B., and Niazi, A. (2020). Three dimensional quantitative structure activity
relationship and pharmacophore modeling of tacrine derivatives as acetylcholinesterase inhibitors
in Alzheimer’s treatment. Medicinal Chemistry (Los Angeles) 16 (2): 155–168.
15 Muegge, I. (2002). Pharmacophore features of potential drugs. Chemistry – A European Journal
8 (9): 1976–1981.
References 189
16 Sakkiah, S., Thangapandian, S., John, S. et al. (2010). 3D QSAR pharmacophore based virtual
screening and molecular docking for identification of potential HSP90 inhibitors. European
Journal of Medicinal Chemistry 45 (6): 2132–2140.
17 Schwab, C.H. (2010). Conformations and 3D pharmacophore searching. Drug Discovery Today:
Technologies 7 (4): e245–e253.
18 Acharya, C., Coop, A., Polli, J.E., and MacKerell, A.D. (2011). Recent advances in ligand-based drug
design: relevance and utility of the conformationally sampled pharmacophore approach. Current
Computer-Aided Drug Design 7 (1): 10–22.
19 Lu, X., Yang, H., Chen, Y. et al. (2018). The development of pharmacophore Modeling: generation
and recent applications in drug discovery. Current Pharmaceutical Design 24 (29): 3424–3439.
20 McGregor, M.J. and Muskal, S.M. (2000). Pharmacophore fingerprinting. 2. Application to primary
library design. Journal of Chemical Information and Computer Sciences 40 (1): 117–125.
21 Fourches, D., Muratov, E., and Tropsha, A. (2010). Trust, but verify: on the importance of chemical
structure curation in cheminformatics and QSAR modeling research. Journal of Chemical
Information and Modeling 50 (7): 1189.
22 Güner, O.F. (2000). Pharmacophore Perception, Development, and Use in Drug Design, vol. 2.
Internat’l University Line.
23 Tyagi, R., Singh, A., Chaudhary, K.K., and Yadav, M.K. (2022). Pharmacophore modeling and its
applications. In: Bioinformatics, 269–289. Elsevier.
24 Sadowski, J. (1997). A hybrid approach for addressing ring flexibility in 3D database Searching.
Journal of Computer-Aided Molecular Design 11: 53–60.
25 Gasteiger, J. (2012). Software Development in Chemistry 4: Proceedings of the 4th Workshop
“Computers in Chemistry” Hochfilzen, Tyrol, November 22–24, 1989, Springer Science & Business
Media.
26 Wipke, W.T. and Hahn, M.A. (1988). AIMB: analogy and intelligence in model building. System
description and performance characteristics. Tetrahedron Computer Methodology 1 (2): 141–167.
27 Dolata, D.P., Leach, A.R., and Prout, K. (1987). WIZARD: AI in conformational analysis. Journal of
Computer-Aided Molecular Design 1: 73–85.
28 van Drie, J.H. (2003). Pharmacophore discovery-lessons learned. Current Pharmaceutical Design
9 (20): 1649–1664.
29 Sheridan, R.P., Nilakantan, R., Rusinko, A. III et al. (1989). 3DSEARCH: a system for three
dimensional substructure searching. Journal of Chemical Information and Computer Sciences
29 (4): 255–260.
30 Fisanick, W., Cross, K.P., and Rusinko, A. III (1992). Similarity searching on CAS registry
substances. 1. Global molecular property and generic atom triangle geometric Searching. Journal
of Chemical Information and Computer Sciences 32 (6): 664–674.
31 Kuntz, I.D. (1992). Structure-based strategies for drug design and discovery. Science (1979)
257 (5073): 1078–1082.
32 Mayer, D., Naylor, C.B., Motoc, I., and Marshall, G.R. (1987). A unique geometry of the active site
of angiotensin-converting enzyme consistent with structure-activity studies. Journal of Computer-
Aided Molecular Design 1: 3–16.
33 Kuster, D.J. and Marshall, G.R. (2005). Validated ligand mapping of ACE active site. Journal of
Computer-Aided Molecular Design 19: 609–615.
34 Murrall, N.W. and Davies, E.K. (1990). Conformational freedom in 3-D databases. 1. Techniques.
Journal of Chemical Information and Computer Sciences 30 (3): 312–316.
35 Hurst, T. (1994). Flexible 3D searching: the directed tweak technique. Journal of Chemical
Information and Computer Sciences 34 (1): 190–196.
190
36 Barnum, D., Greene, J., Smellie, A., and Sprague, P. (1996). Identification of common functional
configurations among molecules. Journal of Chemical Information and Computer Sciences 36 (3):
563–571.
37 Jiménez, J., Doerr, S., Martínez-Rosell, G. et al. (2017). DeepSite: protein-binding site predictor
using 3D-convolutional neural networks. Bioinformatics 33 (19): 3036–3042.
38 Jones, G., Willett, P., and Glen, R.C. (1995). A genetic algorithm for flexible molecular overlay and
pharmacophore elucidation. Journal of Computer-Aided Molecular Design 9: 532–549.
39 Cramer, R.D.I., Patterson, D.E., and Bunce, J.D. (1988). Comparative molecular field analysis
(CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. Journal of the American
Chemical Society 110 (18): 5959–5967.
40 Li, H., Sutter, J., and Hoffmann, R. (2000). HypoGen: an automated system for generating 3D predictive
pharmacophore models. Pharmacophore Perception, Development, and Use in Drug Design 2: 171.
41 Kurogi, Y. and Guner, O.F. (2001). Pharmacophore modeling and three-dimensional database
searching for drug design using catalyst. Current Medicinal Chemistry 8 (9): 1035–1055.
42 Dror, O., Shulman-Peleg, A., Nussinov, R., and Wolfson, H.J. (2004). Predicting molecular
interactions in silico: I. A guide to pharmacophore identification and its applications to drug
design. Current Medicinal Chemistry 11 (1): 71–90.
43 Glennon, R.A., Dukat, M., and Liao, L. (2004). Musings on Α4β2 nicotinic acetylcholine (NACh)
receptor pharmacophore models. Current Topics in Medicinal Chemistry 4 (6): 631–644.
44 Patel, Y., Gillet, V.J., Bravi, G., and Leach, A.R. (2002). A comparison of the pharmacophore
identification programs: catalyst, DISCO and GASP. Journal of Computer-Aided Molecular Design
16: 653–681.
45 Wolber, G. and Langer, T. (2005). LigandScout: 3-D pharmacophores derived from protein-bound
ligands and their use as virtual screening filters. Journal of Chemical Information and Modeling
45 (1): 160–169.
46 Richmond, N.J., Abrams, C.A., Wolohan, P.R.N. et al. (2006). GALAHAD: 1. Pharmacophore
identification by hypermolecular alignment of ligands in 3D. Journal of Computer-Aided Molecular
Design 20: 567–587.
47 Vilar, S., Cozza, G., and Moro, S. (2008). Medicinal chemistry and the molecular operating
environment (MOE): application of QSAR and molecular docking to drug discovery. Current Topics
in Medicinal Chemistry 8 (18): 1555–1572.
48 Dixon, S.L., Smondyrev, A.M., and Rao, S.N. (2006). PHASE: a novel approach to pharmacophore
modeling and 3D database searching. Chemical Biology & Drug Design 67 (5): 370–372.
49 Schneidman-Duhovny, D., Dror, O., Inbar, Y. et al. (2008). PharmaGist: a webserver for ligand
based pharmacophore detection. Nucleic Acids Research 36 (suppl_2): W223–W228.
50 Koes, D.R. and Camacho, C.J. (2011). Pharmer: efficient and exact pharmacophore search. Journal
of Chemical Information and Modeling 51 (6): 1307–1314.
51 Liu, X., Ouyang, S., Yu, B. et al. (2010). PharmMapper server: a web server for potential drug target
identification using pharmacophore mapping approach. Nucleic Acids Research 38 (suppl_2):
W609–W614.
52 Gaulton, A., Bellis, L.J., Bento, A.P. et al. (2012). ChEMBL: a large-scale bioactivity database for
drug discovery. Nucleic Acids Research 40 (D1): D1100–D1107.
53 Maho, N. (2015). The PubChemQC project: a large chemical database from the first principle
calculations. AIP Conference Proceedings, AIP Publishing, Vol. 1702.
54 Leach, A.R., Gillet, V.J., Lewis, R.A., and Taylor, R. (2010). Three-dimensional pharmacophore
methods in drug discovery. Journal of Medicinal Chemistry 53 (2): 539–558.
References 191
55 Seidel, T., Ibis, G., Bendix, F., and Wolber, G. (2010). Strategies for 3D pharmacophore-based
virtual screening. Drug Discovery Today: Technologies 7 (4): e221–e228.
56 Huang, N., Shoichet, B.K., and Irwin, J.J. (2006). Benchmarking sets for molecular docking.
Journal of Medicinal Chemistry 49 (23): 6789–6801.
57 Caporuscio, F. and Tafi, A. (2011). Pharmacophore modelling: a forty year old approach and its
modern synergies. Current Medicinal Chemistry 18 (17): 2543–2553.
58 Griffith, R., Luu, T.T.T., Garner, J., and Keller, P.A. (2005). Combining structure-based drug design
and pharmacophores. Journal of Molecular Graphics & Modelling 23 (5): 439–446.
59 Patel, S., Modi, P., and Chhabria, M. (2018). Rational approach to identify newer
caspase-1 inhibitors using pharmacophore based virtual screening, docking and molecular
dynamic simulation studies. Journal of Molecular Graphics & Modelling 81: 106–115.
60 Lin, X., Li, X., and Lin, X. (2020). A review on applications of computational methods in drug
screening and design. Molecules 25 (6): 1375.
61 Poptodorov, K., Luu, T., and Hoffmann, R.D. (2006). Pharmacophore model generation software
tools. Pharmacophores and Pharmacophore Searches 32: 15–47.
62 Wolber, G., Seidel, T., Bendix, F., and Langer, T. (2008). Molecule-pharmacophore superpositioning
and pattern matching in computational drug design. Drug Discovery Today 13 (1–2): 23–29.
63 Baroni, M., Cruciani, G., Sciabola, S. et al. (2007). A common reference framework for Analyzing/
comparing proteins and ligands. Fingerprints for ligands and proteins (FLAP): theory and
application. Journal of Chemical Information and Modeling 47 (2): 279–294.
64 Nettles, J.H., Jenkins, J.L., Williams, C. et al. (2007). Flexible 3D pharmacophores as descriptors of
dynamic biological space. Journal of Molecular Graphics & Modelling 26 (3): 622–633.
65 Totrov, M. (2008). Atomic property fields: generalized 3D pharmacophoric potential for automated
ligand superposition, pharmacophore elucidation and 3D QSAR. Chemical Biology & Drug Design
71 (1): 15–27.
66 Bandyopadhyay, D. and Agrafiotis, D.K. (2008). A self-organizing algorithm for molecular
alignment and pharmacophore development. Journal of Computational Chemistry 29 (6): 965–982.
67 Hecker, E.A., Duraiswami, C., Andrea, T.A., and Diller, D.J. (2002). Use of catalyst pharmacophore
models for screening of large combinatorial libraries. Journal of Chemical Information and
Computer Sciences 42 (5): 1204–1211.
68 Toba, S., Srinivasan, J., Maynard, A.J., and Sutter, J. (2006). Using pharmacophore models to gain
insight into structural binding and virtual screening: an application study with CDK2 and human
DHFR. Journal of Chemical Information and Modeling 46 (2): 728–735.
69 Vadivelan, S., Sinha, B.N., Irudayam, S.J., and Jagarlapudi, S.A.R.P. (2007). Virtual screening
studies to design potent CDK2-cyclin A inhibitors. Journal of Chemical Information and Modeling
47 (4): 1526–1535.
70 Yang, S.-Y. (2010). Pharmacophore Modeling and applications in drug discovery: challenges and
recent advances. Drug Discovery Today 15 (11–12): 444–450.
71 Zou, J., Xie, H.-Z., Yang, S.-Y. et al. (2008). Towards more accurate pharmacophore modeling:
multicomplex-based comprehensive pharmacophore map and most-frequent-feature
pharmacophore model of CDK2. Journal of Molecular Graphics & Modelling 27 (4): 430–438.
72 Schaller, D., Šribar, D., Noonan, T. et al. (2020). Next generation 3D pharmacophore modeling.
Wiley Interdisciplinary Reviews: Computational Molecular Science 10 (4): e1468.
73 Bajorath, J. (2002). Integration of virtual and high-throughput screening. Nature Reviews. Drug
Discovery 1 (11): 882–894.
74 Tanrikulu, Y., Krüger, B., and Proschak, E. (2013). The holistic integration of virtual screening in
drug discovery. Drug Discovery Today 18 (7–8): 358–364.
192
75 Schuster, D., Spetea, M., Music, M. et al. (2010). Morphinans and Isoquinolines:
acetylcholinesterase inhibition, pharmacophore modeling, and interaction with opioid receptors.
Bioorganic & Medicinal Chemistry 18 (14): 5071–5080.
76 Polgár, T., Baki, A., Szendrei, G.I., and Keserűu, G.M. (2005). Comparative virtual and
experimental high-throughput screening for glycogen synthase kinase-3β inhibitors. Journal of
Medicinal Chemistry 48 (25): 7946–7959.
77 Doman, T.N., McGovern, S.L., Witherbee, B.J. et al. (2002). Molecular docking and high-
throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. Journal of Medicinal
Chemistry 45 (11): 2213–2221.
78 Vuorinen, A. and Schuster, D. (2015). Methods for generating and applying pharmacophore
models as virtual screening filters and for bioactivity profiling. Methods 71: 113–134.
79 Rollinger, J.M., Schuster, D., Danzl, B. et al. (2009). In silico target fishing for rationalized ligand
discovery exemplified on constituents of Ruta Graveolens. Planta Medica 75 (03): 195–204.
80 Wu, B., Gao, J., and Wang, M. (2005). Development of a complex scintillation proximity assay for
high throughput screening of PPARγ modulators. Acta Pharmacologica Sinica 26 (3): 339–344.
81 Murgueitio, M.S., Henneke, P., Glossmann, H. et al. (2014). Prospective virtual screening in
a sparse data scenario: design of small-molecule TLR2 antagonists. ChemMedChem 9 (4):
813–822.
82 Krautscheid, Y., Senning, C.J.Å., Sartori, S.B. et al. (2014). Pharmacophore modeling, virtual
screening, and in vitro testing reveal haloperidol, eprazinone, and fenbutrazate as neurokinin
receptors ligands. Journal of Chemical Information and Modeling 54 (6): 1747–1757.
83 Joung, J.Y., Lee, H.Y., Park, J. et al. (2014). Identification of novel Rab27a/melanophilin blockers
by pharmacophore-based virtual screening. Applied Biochemistry and Biotechnology 172:
1882–1897.
84 Lu, P., Wang, Y., Kai Ouyang, P. et al. (2015). 3d-QSAR based pharmacophore modeling and virtual
screening for identification of novel G protein-coupled receptor40 agonists. Current Computer-
Aided Drug Design 11 (1): 51–56.
85 Singh, N., Tiwari, S., Srivastava, K.K., and Siddiqi, M.I. (2015). Identification of novel inhibitors of
mycobacterium tuberculosis PknG using pharmacophore based virtual screening, docking,
molecular dynamics simulation, and their biological evaluation. Journal of Chemical Information
and Modeling 55 (6): 1120–1129.
86 Temml, V., Voss, C.V., Dirsch, V.M., and Schuster, D. (2014). Discovery of new liver X receptor
agonists by pharmacophore modeling and shape-based virtual screening. Journal of Chemical
Information and Modeling 54 (2): 367–371.
87 Lepailleur, A., Freret, T., Lemaître, S. et al. (2014). Dual histamine H3R/serotonin 5-HT4R ligands
with antiamnesic properties: pharmacophore-based virtual screening and polypharmacology.
Journal of Chemical Information and Modeling 54 (6): 1773–1784.
88 Ferreira, R.J., dos Santos, D.J.V.A., Ferreira, M.-J.U., and Guedes, R.C. (2011). Toward a better
pharmacophore description of P-glycoprotein modulators, based on macrocyclic diterpenes from
euphorbia species. Journal of Chemical Information and Modeling 51 (6): 1315–1324.
89 Flohr, S., Kurz, M., Kostenis, E. et al. (2002). Identification of nonpeptidic urotensin II receptor
antagonists by virtual screening based on a pharmacophore model derived from structure–activity
relationships and nuclear magnetic resonance studies on urotensin II. Journal of Medicinal
Chemistry 45 (9): 1799–1805.
90 Hessler, G. and Baringhaus, K.-H. (2010). The scaffold hopping potential of pharmacophores. Drug
Discovery Today: Technologies 7 (4): e263–e269.