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Preliminary modelling as the first stage of targeted organic synthesis

Ilya Yur'evich Titov 1, 2
Ilya Yur'evich Titov
, 
Viktor Sergeevich Stroylov 1, 2
Viktor Sergeevich Stroylov
, 
Polina Vladimirovna Rusina 1, 3
Polina Vladimirovna Rusina
, 
1 N.D. Zelinskii Institute of Organic Chemistry, Russian Academy of Sciences, Moscow
2 National Research University "Higher School of Economics", Moscow
3 Lomonosov Moscow State University, Faculty of Chemistry
Published 30 November 2020
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Titov I. Y. et al. Preliminary modelling as the first stage of targeted organic synthesis // Russian Chemical Reviews. 2020. Vol. 90. No. 7. pp. 831-867.
GOST all authors (up to 50)
Titov I. Y., Stroylov V. S., Rusina P. V., Svitanko I. Preliminary modelling as the first stage of targeted organic synthesis // Russian Chemical Reviews. 2020. Vol. 90. No. 7. pp. 831-867.
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TY - JOUR
DO - 10.1070/RCR5012
UR - https://rcr.colab.ws/publications/10.1070/RCR5012
TI - Preliminary modelling as the first stage of targeted organic synthesis
T2 - Russian Chemical Reviews
AU - Titov, Ilya Yur'evich
AU - Stroylov, Viktor Sergeevich
AU - Rusina, Polina Vladimirovna
AU - Svitanko, Igor
PY - 2020
DA - 2020/11/30
PB - ANO Editorial Board of the journal Uspekhi Khimii
SP - 831-867
IS - 7
VL - 90
ER -
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@article{2020_Titov,
author = {Ilya Yur'evich Titov and Viktor Sergeevich Stroylov and Polina Vladimirovna Rusina and Igor Svitanko},
title = {Preliminary modelling as the first stage of targeted organic synthesis},
journal = {Russian Chemical Reviews},
year = {2020},
volume = {90},
publisher = {ANO Editorial Board of the journal Uspekhi Khimii},
month = {Nov},
url = {https://rcr.colab.ws/publications/10.1070/RCR5012},
number = {7},
pages = {831--867},
doi = {10.1070/RCR5012}
}
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Cite this
MLA
Titov, Ilya Yur'evich, et al. “Preliminary modelling as the first stage of targeted organic synthesis.” Russian Chemical Reviews, vol. 90, no. 7, Nov. 2020, pp. 831-867. https://rcr.colab.ws/publications/10.1070/RCR5012.
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Keywords

3D-printing
catalysis
docking
drug design
FEP
Molecular modeling
QSAR
quantum chemistry
targeted synthesis

Abstract

The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target--ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target--ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.

The bibliography includes 353 references.

References

1.
Virtual high-throughput in silico screening
Seifert M.H., Wolf K., Vitt D.
BIOSILICO , 2003
4.
Molecular similarity concept and its use for predicting the properties of chemical compounds
Skvortsova M.I., Stankevich I.V., Palyulin V.A., Zefirov N.S.
Russian Chemical Reviews , 2006
8.
How far can virtual screening take us in drug discovery?
Kar S., Roy K.
Expert Opinion on Drug Discovery , 2013
9.
rcr_90_7_831bib9
10.
Reducing Time to Market by Innovative Development and Production Strategies
Grützner T., Schnider C., Zollinger D., Seyfang B.C., Künzle N.
Chemical Engineering and Technology , 2016
11.
Molecular Connectivity in Structure–activity Analysis
Unger S.H.
Journal of Pharmaceutical Sciences , 1987
12.
rcr_90_7_831bib12
Hall
1999
13.
rcr_90_7_831bib13
Madzhinov
2019
14.
QSAR without borders
Muratov E.N., Bajorath J., Sheridan R.P., Tetko I.V., Filimonov D., Poroikov V., Oprea T.I., Baskin I.I., Varnek A., Roitberg A., Isayev O., Curtalolo S., Fourches D., Cohen Y., Aspuru-Guzik A., et. al.
Chemical Society Reviews , 2020
15.
rcr_90_7_831bib15
Raevsky
2015
16.
A two-phase solution procedure using mixtures of algorithms in the structure–property problem
Prokhorov E.I., Svitan’ko I.V., Zakharenko A.L., Sukhanova M.V., Bekker A.V., Perevoznikov A.V., Kumskov M.I.
Pattern Recognition and Image Analysis , 2016
17.
Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR
Costa P.C., Evangelista J.S., Leal I., Miranda P.C.
Mathematics , 2020
18.
rcr_90_7_831bib18
19.
Automated docking of substrates to proteins by simulated annealing
Goodsell D.S., Olson A.J.
Proteins: Structure, Function and Genetics , 1990
21.
Software for molecular docking: a review
Pagadala N.S., Syed K., Tuszynski J.
Biophysical Reviews , 2017
22.
rcr_90_7_831bib22
Medvedev
2021
23.
rcr_90_7_831bib23
Muniz
PLoS ONE , 2017
25.
Folding and binding cascades: Dynamic landscapes and population shifts
Kumar S., Ma B., Tsai C., Sinha N., Nussinov R.
Protein Science , 2008
27.
Practical Aspects of Free-Energy Calculations: A Review
Hansen N., van Gunsteren W.F.
Journal of Chemical Theory and Computation , 2014
28.
rcr_90_7_831bib28
Wu
J. Chem. Phys. , 2010
29.
Alchemical FEP Calculations of Ligand Conformer Focusing in Explicit Solvent
Zeifman A.A., Stroylov V.V., Novikov F.N., Stroganov O.V., Kulkov V., Chilov G.G.
Journal of Chemical Theory and Computation , 2013
30.
Theoretical Approach To Predict the Stability of Supported Single-Atom Catalysts
Su Y., Wang Y., Liu J., Filot I.A., Alexopoulos K., Zhang L., Muravev V., Zijlstra B., Vlachos D.G., Hensen E.J.
ACS Catalysis , 2019
32.
Monitoring chemical reactions in liquid media using electron microscopy
33.
Selectivity control in thiol–yne click reactions via visible light induced associative electron upconversion†
Burykina J.V., Shlapakov N.S., Gordeev E.G., König B., Ananikov V.P.
Chemical Science , 2020
34.
Introducing tox-Profiles of Chemical Reactions.
Egorova K.S., Galushko A.S., Ananikov V.P.
Angewandte Chemie - International Edition , 2020
35.
Carbocatalysis: From Acetylene Trimerization to Modern Organic Synthesis. A Review
Pentsak E.O., Gordeev E.G., Ananikov V.P.
Doklady Physical Chemistry , 2020
37.
3D-QSAR in Drug Design - A Review
Verma J., Khedkar V., Coutinho E.
Current Topics in Medicinal Chemistry , 2010
39.
QSAR modeling: where have you been? Where are you going to?
Cherkasov A., Muratov E.N., Fourches D., Varnek A., Baskin I.I., Cronin M., Dearden J., Gramatica P., Martin Y.C., Todeschini R., Consonni V., Kuz’min V.E., Cramer R., Benigni R., Yang C., et. al.
Journal of Medicinal Chemistry , 2014
40.
The inevitable QSAR renaissance
Cramer R.D.
Journal of Computer-Aided Molecular Design , 2011
42.
rcr_90_7_831bib42
Todeschini
2009
43.
Comparative molecular field analysis (CoMFA).  1.  Effect of shape on binding of steroids to carrier proteins
Cramer R.D., Patterson D.E., Bunce J.D.
Journal of the American Chemical Society , 1988
44.
rcr_90_7_831bib44
Wold
Analysis , 1984
47.
GRid-INdependent Descriptors (GRIND):  A Novel Class of Alignment-Independent Three-Dimensional Molecular Descriptors
Pastor M., Cruciani G., McLay I., Pickett S., Clementi S.
Journal of Medicinal Chemistry , 2000
50.
PHASE: A Novel Approach to Pharmacophore Modeling and 3D Database Searching
Dixon S.L., Smondyrev A.M., Rao S.N.
Chemical Biology and Drug Design , 2006
51.
Prediction of Drug Binding Affinities by Comparative Binding Energy Analysis
Ortiz A.R., Pisabarro M.T., Gago F., Wade R.C.
Journal of Medicinal Chemistry , 1995
55.
Iterative Refinement of a Binding Pocket Model: Active Computational Steering of Lead Optimization
Varela R., Walters W.P., Goldman B.B., Jain A.N.
Journal of Medicinal Chemistry , 2012
56.
ChEMBL: towards direct deposition of bioassay data
Mendez D., Gaulton A., Bento A.P., Chambers J., De Veij M., Félix E., Magariños M., Mosquera J., Mutowo P., Nowotka M., Gordillo-Marañón M., Hunter F., Junco L., Mugumbate G., Rodriguez-Lopez M., et. al.
Nucleic Acids Research , 2018
57.
rcr_90_7_831bib57
Kim
Nucleic Acids Res. , 2021
58.
Computer-aided prediction of biological activity spectra for organic compounds: the possibilities and limitations
Poroikov V.V., Filimonov D.A., Gloriozova T.A., Lagunin A.A., Druzhilovskiy D.S., Rudik A.V., Stolbov L.A., Dmitriev A.V., Tarasova O.A., Ivanov S.M., Pogodin P.V.
Russian Chemical Bulletin , 2019
59.
rcr_90_7_831bib59
61.
Prediction of the Biological Activity Spectra of Organic Compounds Using the Pass Online Web Resource
Filimonov D.A., Lagunin A.A., Gloriozova T.A., Rudik A.V., Druzhilovskii D.S., Pogodin P.V., Poroikov V.V.
Chemistry of Heterocyclic Compounds , 2014
62.
rcr_90_7_831bib62
63.
Enhanced taxonomy annotation of antiviral activity data from ChEMBL
Nikitina A.A., Orlov A.A., Kozlovskaya L.I., Palyulin V.A., Osolodkin D.I.
Database : the journal of biological databases and curation , 2019
65.
rcr_90_7_831bib65
Ariens
1964
66.
rcr_90_7_831bib66
Golender
1983
67.
Docking flexible ligands to macromolecular receptors by molecular shape
DesJarlais R.L., Sheridan R.P., Dixon J.S., Kuntz I.D., Venkataraghavan R.
Journal of Medicinal Chemistry , 1986
69.
Principles of protein–protein recognition
70.
Computer analysis of protein-protein interaction.
Wodak S.J., Janin J.
Journal of Molecular Biology , 1978
71.
A geometric approach to macromolecule-ligand interactions
Kuntz I.D., Blaney J.M., Oatley S.J., Langridge R., Ferrin T.E.
Journal of Molecular Biology , 1982
72.
Molecular similarity based on electrostatic potential and electric field
Hodgkin E.E., Richards W.G.
International Journal of Quantum Chemistry , 1987
75.
rcr_90_7_831bib75
Politzer
1981
76.
rcr_90_7_831bib76
Scrocco
Top. Curr. Chem. , 1973
77.
rcr_90_7_831bib77
dos Santos
2020
80.
rcr_90_7_831bib80
Prokhorov
2012
81.
Molecular shape comparison of angiotensin II receptor antagonists
Masek B.B., Merchant A., Matthew J.B.
Journal of Medicinal Chemistry , 1993
82.
Svitanko Igor, Kumskov Mikhail, Zyryanov Ivan Leonidovich, Suslov Ivan Aleksandrovich
Mendeleev Communications , 1994
83.
rcr_90_7_831bib83
Kumskov
Pattern Recognit. Image Anal. , 1995
86.
Calculation of the average properties of atoms in molecules. II
Biegler-könig F.W., Bader R.F., Tang T.
Journal of Computational Chemistry , 1982
88.
An approach to computing electrostatic charges for molecules
Singh U.C., Kollman P.A.
Journal of Computational Chemistry , 1984
90.
Multipole models of sulphur for accurate anisotropic electrostatic interactions within force fields
Shulga D.A., Titov O.I., Pisarev S.A., Palyulin V.A.
SAR and QSAR in Environmental Research , 2017
91.
Quadrupole Correction: From Molecular Electrostatic Potential to Free Energies of Halogen Bonding
Titov O.I., Shulga D.A., Palyulin V.A.
Journal of Chemical Theory and Computation , 2018
92.
93.
Is It Possible to Predict the Odor of a Molecule on the Basis of its Structure?
Genva M., Kenne Kemene T., Deleu M., Lins L., Fauconnier M.
International Journal of Molecular Sciences , 2019
95.
QSPR analysis for the retention index of flavors and fragrances on a OV-101 column
Rojas C., Duchowicz P.R., Tripaldi P., Diez R.P.
Chemometrics and Intelligent Laboratory Systems , 2015
96.
rcr_90_7_831bib96
Vella
Biochem. Ed. , 1993
97.
The taste ofl- andd-amino acids
Solms J., Vuataz L., Egli R.H.
Cellular and Molecular Life Sciences , 1965
101.
Thalidomide's chirality
Wnendt S., Zwingenberger K.
Nature , 1997
103.
On the odor of the enantiomers of Madrol�
Buchbauer G., Lebada P., Wiesinger L., Weiss-Greiler P., Wolschann P.
Chirality , 1997
104.
A Systematic Framework for Olfactory Bulb Signal Transformations
Cleland T.A., Borthakur A.
Frontiers in Computational Neuroscience , 2020
105.
On the Unpredictability of Odor
Sell C.S.
Angewandte Chemie - International Edition , 2006
106.
Sensory Perception of Non‐Deuterated and Deuterated Organic Compounds
Salthammer T., Monegel F., Schulz N., Uhde E., Grimme S., Seibert J., Hohm U., Palm W.
Chemistry - A European Journal , 2020
107.
Visualizing an Olfactory Sensory Map
Mombaerts P., Wang F., Dulac C., Chao S.K., Nemes A., Mendelsohn M., Edmondson J., Axel R.
Cell , 1996
109.
Combinatorial QSAR of Ambergris Fragrance Compounds
Kovatcheva A., Golbraikh A., Oloff S., Xiao Y., Zheng W., Wolschann P., Buchbauer G., Tropsha A.
Journal of Chemical Information and Computer Sciences , 2004
110.
rcr_90_7_831bib110
Bersuker
Nouv. J. Chim. , 1985
111.
rcr_90_7_831bib111
Politzer
2007
112.
Svitanko Igor, Devetyarov Dmitry Aleksandrovich, Chebukov Dmitry, Dolmat Maksim S, Zakharov Alexey Mikhailovich, Grigor'eva Svetlana S, Chichua Viktoriya Tarielevna, Ponomareva Lyudmila A, Kumskov Mikhail
Mendeleev Communications , 2007
113.
rcr_90_7_831bib113
Kheifits
1994
114.
rcr_90_7_831bib114
Svitanko
2018
116.
Progress in the chemistry of nitrogen-, oxygen- and sulfur-containing heterocyclic systems
Makhova N.N., Belen’kii L.I., Gazieva G.A., Dalinger I.L., Konstantinova L.S., Kuznetsov V.V., Kravchenko A.N., Krayushkin M.M., Rakitin O.A., Starosotnikov A.M., Fershtat L.L., Shevelev S.A., Shirinian V.Z., Yarovenko V.N.
Russian Chemical Reviews , 2020
117.
Baranov Vladimir Vladimirovich, Barsegyan Yana Arturovna, Strelenko Yurii Andreevich, Karnoukhova Valentina Aleksandrovna, Kravchenko Angelina Nikolaevna
Mendeleev Communications , 2020
118.
Baranov Vladimir Vladimirovich, Ivanova Aleksandra Borisovna, Kolotyrkina Natal'ya Georgievna, Kravchenko Angelina Nikolaevna
Mendeleev Communications , 2018
119.
Chemistry of bicyclic ureas. 5. Synthesis of 2,4,6,8-tetraazabicyclo[3.3.0]-3-octanone-7-thiones
Eres'ko V.A., Epishina L.V., Lebedev O.V., Povstyanoi M.V., Khmel'nitskii L.I., Novikov S.S.
Russian Chemical Bulletin , 1980
120.
Nootropic activity of N-(2-acetylaminoethyl)glycolurils
Anikina L.V., Gazieva G.A., Kravchenko A.N.
Russian Chemical Bulletin , 2020
121.
Svitanko Igor, Zyryanov Ivan Leonidovich, Kumskov Mikhail, Khmel'nitskii Lenor Ivanovich, Suvorova Lyudmila Ivanovna, Kravchenko Angelina Nikolaevna, Markova Tatyana Borisovna, Lebedev Oleg Vasilevich, Orekhova Galina A, Belova Svetlana V
Mendeleev Communications , 1995
122.
Gazieva Galina Anatol'evna, Vikharev Yurii Borisovich, Anikina Lada Vladimirovna, Karpova Tatyana B, Kravchenko Angelina Nikolaevna, Permyakov Evgenii Aleksandrovich, Svitanko Igor
Mendeleev Communications , 2013
123.
rcr_90_7_831bib123
Hugo
3D QSAR in Drug Design. Theory Methods and Applications. , 1994
124.
Recent Advances in Multidimensional QSAR (4D-6D): A Critical Review
Damale M., Harke S., Kalam Khan F., Shinde D., Sangshetti J.
Mini-Reviews in Medicinal Chemistry , 2014
125.
QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches
Vahedi N., Mohammadhosseini M., Nekoei M.
Current Analytical Chemistry , 2020
126.
rcr_90_7_831bib126
Tropsha
2003
127.
RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences
Burley S.K., Bhikadiya C., Bi C., Bittrich S., Chen L., Crichlow G.V., Christie C.H., Dalenberg K., Di Costanzo L., Duarte J.M., Dutta S., Feng Z., Ganesan S., Goodsell D.S., Ghosh S., et. al.
Nucleic Acids Research , 2020
128.
The role of human in the loop: lessons from D3R challenge 4
Stroganov O.V., Novikov F.N., Medvedev M.G., Dmitrienko A.O., Gerasimov I., Svitanko I.V., Chilov G.G.
Journal of Computer-Aided Molecular Design , 2020
129.
Docking Screens for Novel Ligands Conferring New Biology
Irwin J.J., Shoichet B.K.
Journal of Medicinal Chemistry , 2016
130.
rcr_90_7_831bib130
Sethi
2020
131.
Augmenting Hit Identification by Virtual Screening Techniques in Small Molecule Drug Discovery
Yan X.C., Sanders J.M., Gao Y., Tudor M., Haidle A.M., Klein D.J., Converso A., Lesburg C.A., Zang Y., Wood H.B.
Journal of Chemical Information and Modeling , 2020
132.
Virtual Compound Libraries in Computer-Assisted Drug Discovery
van Hilten N., Chevillard F., Kolb P.
Journal of Chemical Information and Modeling , 2019
133.
An open-source drug discovery platform enables ultra-large virtual screens
Gorgulla C., Boeszoermenyi A., Wang Z., Fischer P.D., Coote P.W., Padmanabha Das K.M., Malets Y.S., Radchenko D.S., Moroz Y.S., Scott D.A., Fackeldey K., Hoffmann M., Iavniuk I., Wagner G., Arthanari H., et. al.
Nature , 2020
134.
Ultra-large library docking for discovering new chemotypes
Lyu J., Wang S., Balius T.E., Singh I., Levit A., Moroz Y.S., O’Meara M.J., Che T., Algaa E., Tolmachova K., Tolmachev A.A., Shoichet B.K., Roth B.L., Irwin J.J.
Nature , 2019
135.
Pros and cons of virtual screening based on public “Big Data”: In silico mining for new bromodomain inhibitors
Casciuc I., Horvath D., Gryniukova A., Tolmachova K.A., Vasylchenko O.V., Borysko P., Moroz Y.S., Bajorath J., Varnek A.
European Journal of Medicinal Chemistry , 2019
137.
rcr_90_7_831bib137
Acharya
Sci. Rep. , 2019
138.
Rational design, molecular docking and synthesis of novel homopiperazine linked imidazo[1,2-a]pyrimidine derivatives as potent cytotoxic and antimicrobial agents
Mantipally M., Gangireddy M.R., Gundla R., Badavath V.N., Mandha S.R., Maddipati V.C.
Bioorganic and Medicinal Chemistry Letters , 2019
139.
P2X7 receptor antagonism: Implications in diabetic retinopathy
Platania C.B., Giurdanella G., Di Paola L., Leggio G.M., Drago F., Salomone S., Bucolo C.
Biochemical Pharmacology , 2017
141.
Novel quinazoline-based EGFR kinase inhibitors: A review focussing on SAR and molecular docking studies (2015-2019)
Bhatia P., Sharma V., Alam O., Manaithiya A., Alam P., Kahksha, Alam M.T., Imran M.
European Journal of Medicinal Chemistry , 2020
142.
Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors
Bartuzi D., Kaczor A., Targowska-Duda K., Matosiuk D.
Molecules , 2017
144.
Novel hits for acetylcholinesterase inhibition derived by docking-based screening on ZINC database
Doytchinova I., Atanasova M., Valkova I., Stavrakov G., Philipova I., Zhivkova Z., Zheleva-Dimitrova D., Konstantinov S., Dimitrov I.
Journal of Enzyme Inhibition and Medicinal Chemistry , 2018
145.
rcr_90_7_831bib145
Türkan
2020
146.
Docking Finds GPCR Ligands in Dark Chemical Matter
Ballante F., Rudling A., Zeifman A., Luttens A., Vo D.D., Irwin J.J., Kihlberg J., Brea J., Loza M.I., Carlsson J.
Journal of Medicinal Chemistry , 2019
147.
Selectivity Challenges in Docking Screens for GPCR Targets and Antitargets
Weiss D.R., Karpiak J., Huang X., Sassano M.F., Lyu J., Roth B.L., Shoichet B.K.
Journal of Medicinal Chemistry , 2018
148.
Effective Virtual Screening Strategy toward heme-containing proteins: Identification of novel IDO1 inhibitors
Zou Y., Hu Y., Ge S., Zheng Y., Li Y., Liu W., Guo W., Zhang Y., Xu Q., Lai Y.
European Journal of Medicinal Chemistry , 2019
149.
Discovery of novel inhibitors of human galactokinase by virtual screening
Hu X., Zhang Y., Lee O.W., Liu L., Tang M., Lai K., Boxer M.B., Hall M.D., Shen M.
Journal of Computer-Aided Molecular Design , 2019
150.
Discovery of Novel Androgen Receptor Ligands by Structure-based Virtual Screening and Bioassays
Zhou W., Duan M., Fu W., Pang J., Tang Q., Sun H., Xu L., Chang S., Li D., Hou T.
Genomics, Proteomics and Bioinformatics , 2018
151.
Virtual discovery of melatonin receptor ligands to modulate circadian rhythms
Stein R.M., Kang H.J., McCorvy J.D., Glatfelter G.C., Jones A.J., Che T., Slocum S., Huang X., Savych O., Moroz Y.S., Stauch B., Johansson L.C., Cherezov V., Kenakin T., Irwin J.J., et. al.
Nature , 2020
152.
PF-114, a potent and selective inhibitor of native and mutated BCR/ABL is active against Philadelphia chromosome-positive (Ph+) leukemias harboring the T315I mutation
Mian A.A., Rafiei A., Haberbosch I., Zeifman A., Titov I., Stroylov V., Metodieva A., Stroganov O., Novikov F., Brill B., Chilov G., Hoelzer D., Ottmann O.G., Ruthardt M.
Leukemia , 2014
153.
rcr_90_7_831bib153
Ivanova
Int. J. Oncol. , 2019
154.
AMPA Receptors as Therapeutic Targets for Neurological Disorders
Lee K., Goodman L., Fourie C., Schenk S., Leitch B., Montgomery J.M.
Advances in Protein Chemistry and Structural Biology , 2016
155.
Radchenko Eugene V, Karlov Dmitry Sergeevich, Lavrov Mstislav Igorevich, Palyulin Vladimir Alexandrovich
Mendeleev Communications , 2017
156.
Lavrov Mstislav Igorevich, Karlov Dmitry Sergeevich, Palyulin Vladimir Alexandrovich, Grigoriev Vladimir Viktorovich, Zamoyski Vladimir Lollievich, Brkich Galina Èduardovna, Pyatigorskaya Natalia Valer'evna, Zapolskiy Maxim Èduardovich
Mendeleev Communications , 2018
157.
Bivalent AMPA receptor positive allosteric modulators of the bis(pyrimidine) series
Nazarova A.A., Sedenkova K.N., Karlov D.S., Lavrov M.I., Grishin Y.K., Kuznetsova T.S., Zamoyski V.L., Grigoriev V.V., Averina E.B., Palyulin V.A.
MedChemComm , 2019
158.
Novel Positive Allosteric Modulators of AMPA Receptors Based on 3,7-Diazabicyclo[3.3.1]nonane Scaffold
Lavrov M.I., Karlov D.S., Voronina T.A., Grigoriev V.V., Ustyugov A.A., Bachurin S.O., Palyulin V.A.
Molecular Neurobiology , 2019
159.
Lavrov Mstislav Igorevich, Stroganov Oleg Valentinovich, Zamoyski Vladimir Lollievich, Grigoriev Vladimir Viktorovich, Zapolskiy Maxim Èduardovich, Sysolyatin Sergey Viktorovich, Malykhin Valeriy Viktorovich, Surmachev Vladimir Nikolaevich, Palyulin Vladimir Alexandrovich
Mendeleev Communications , 2020
160.
Lavrov Mstislav Igorevich, Veremeeva Polina Nikolaevna, Karlov Dmitry Sergeevich, Zamoyski Vladimir Lollievich, Grigoriev Vladimir Viktorovich, Palyulin Vladimir Alexandrovich
Mendeleev Communications , 2019
162.
rcr_90_7_831bib162
Zhang
J. Mol. Graphics Modell. , 2020
163.
rcr_90_7_831bib163
Ghaleb
2020
164.
Pharmacophore modeling of JAK1: A target infested with activity-cliffs
Daoud S., Taha M.O.
Journal of Molecular Graphics and Modelling , 2020
166.
Prokhorov Evgenii Igorevich, Bekker Alexandra V, Perevoznikov Alexander V, Kumskov Mikhail, Svitanko Igor
Mendeleev Communications , 2015
167.
rcr_90_7_831bib167
Khamouli
2020
170.
Synthetic Analogues of Aminoadamantane as Influenza Viral Inhibitors—In Vitro, In Silico and QSAR Studies
Chayrov R., Parisis N.A., Chatziathanasiadou M.V., Vrontaki E., Moschovou K., Melagraki G., Sbirkova-Dimitrova H., Shivachev B., Schmidtke M., Mitrev Y., Sticha M., Mavromoustakos T., Tzakos A.G., Stankova I.
Molecules , 2020
171.
Combined QSAR and molecule docking studies on predicting P-glycoprotein inhibitors
Tan W., Mei H., Chao L., Liu T., Pan X., Shu M., Yang L.
Journal of Computer-Aided Molecular Design , 2013
174.
rcr_90_7_831bib174
Friesner
2020
175.
Adaptive BP-Dock: An Induced Fit Docking Approach for Full Receptor Flexibility
Bolia A., Ozkan S.B.
Journal of Chemical Information and Modeling , 2016
176.
Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R Grand Challenge
Ye Z., Baumgartner M.P., Wingert B.M., Camacho C.J.
Journal of Computer-Aided Molecular Design , 2016
177.
iMOLSDOCK: Induced-fit docking using mutually orthogonal Latin squares (MOLS)
Paul D.S., Gautham N.
Journal of Molecular Graphics and Modelling , 2017
178.
Binding Mode and Induced Fit Predictions for Prospective Computational Drug Design
Grebner C., Iegre J., Ulander J., Edman K., Hogner A., Tyrchan C.
Journal of Chemical Information and Modeling , 2016
179.
Novel Procedure for Modeling Ligand/Receptor Induced Fit Effects
Sherman W., Day T., Jacobson M.P., Friesner R.A., Farid R.
Journal of Medicinal Chemistry , 2005
180.
rcr_90_7_831bib180
Niu
RSC Adv. , 2019
181.
rcr_90_7_831bib181
Tang
PhysChemChemPhys , 2018
182.
Discovery of new MurA inhibitors using induced-fit simulation and docking
Rožman K., Lešnik S., Brus B., Hrast M., Sova M., Patin D., Barreteau H., Konc J., Janežič D., Gobec S.
Bioorganic and Medicinal Chemistry Letters , 2017
183.
Discovery of novel Mnk inhibitors using mutation‐based induced‐fit virtual high‐throughput screening
Mishra R.K., Clutter M.R., Blyth G.T., Kosciuczuk E.M., Blackburn A.Z., Beauchamp E.M., Schiltz G.E., Platanias L.C.
Chemical Biology and Drug Design , 2019
184.
Modeling Binding with Large Conformational Changes: Key Points in Ensemble-Docking Approaches
185.
Ensemble Docking in Drug Discovery.
Amaro R.E., Baudry J., Chodera J., Demir Ö., McCammon J.A., Miao Y., Smith J.C.
Biophysical Journal , 2018
186.
Ensemble-based docking: From hit discovery to metabolism and toxicity predictions.
Evangelista W., Weir R.L., Ellingson S.R., Harris J.B., Kapoor K., Smith J.C., Baudry J.
Bioorganic and Medicinal Chemistry , 2016
187.
Retrospective ensemble docking of allosteric modulators in an adenosine G-protein-coupled receptor
Bhattarai A., Wang J., Miao Y.
Biochimica et Biophysica Acta - General Subjects , 2020
188.
rcr_90_7_831bib188
Li
Chem. Biodivers. , 2019
189.
rcr_90_7_831bib189
Sharma
RSC Adv. , 2016
190.
Ensemble docking to difficult targets in early‐stage drug discovery: Methodology and application to fibroblast growth factor 23
Velazquez H.A., Riccardi D., Xiao Z., Quarles L.D., Yates C.R., Baudry J., Smith J.C.
Chemical Biology and Drug Design , 2017
191.
Discovery of Novel Nonactive Site Inhibitors of the Prothrombinase Enzyme Complex
Kapoor K., McGill N., Peterson C.B., Meyers H.V., Blackburn M.N., Baudry J.
Journal of Chemical Information and Modeling , 2016
192.
Polypharmacology and supercomputer-based docking: opportunities and challenges
193.
The compromise of virtual screening and its impact on drug discovery
Slater O., Kontoyianni M.
Expert Opinion on Drug Discovery , 2019
194.
A Spatial Model of the Glycine Site of the NR1 Subunit of NMDA-Receptor and Ligand Docking
Tikhonova I.G., Baskin I.I., Palyulin V.A., Zefirov N.S.
Doklady Biochemistry and Biophysics , 2002
195.
A Critical Assessment of Docking Programs and Scoring Functions
Warren G.L., Andrews C.W., Capelli A., Clarke B., LaLonde J., Lambert M.H., Lindvall M., Nevins N., Semus S.F., Senger S., Tedesco G., Wall I.D., Woolven J.M., Peishoff C.E., Head M.S., et. al.
Journal of Medicinal Chemistry , 2005
199.
Inhibition of Prion Propagation by 3,4-Dimethoxycinnamic Acid
Zanyatkin I., Stroylova Y., Tishina S., Stroylov V., Melnikova A., Haertle T., Muronetz V.
Phytotherapy Research , 2017
201.
rcr_90_7_831bib201
Lin
2020
202.
Disulfiram can inhibit MERS and SARS coronavirus papain-like proteases via different modes
Lin M., Moses D.C., Hsieh C., Cheng S., Chen Y., Sun C., Chou C.
Antiviral Research , 2018
203.
WScore: A Flexible and Accurate Treatment of Explicit Water Molecules in Ligand–Receptor Docking
Murphy R.B., Repasky M.P., Greenwood J.R., Tubert-Brohman I., Jerome S., Annabhimoju R., Boyles N.A., Schmitz C.D., Abel R., Farid R., Friesner R.A.
Journal of Medicinal Chemistry , 2016
204.
Molecular dynamics-driven drug discovery: leaping forward with confidence
205.
Improving Protein-Ligand Docking Results with High-Throughput Molecular Dynamics Simulations
206.
Molecular dynamics in drug design
Zhao H., Caflisch A.
European Journal of Medicinal Chemistry , 2015
207.
Molecular dynamics simulations and novel drug discovery
Liu X., Shi D., Zhou S., Liu H., Liu H., Yao X.
Expert Opinion on Drug Discovery , 2017
209.
Molecular Dynamics Simulation for All
Hollingsworth S.A., Dror R.O.
Neuron , 2018
210.
Molecular Dynamics
Sneha P., George Priya Doss C.
Advances in Protein Chemistry and Structural Biology , 2016
211.
Importance of protein dynamics in the structure-based drug discovery of class A G protein-coupled receptors (GPCRs)
212.
Advanced molecular dynamics simulation methods for kinase drug discovery
Aci-Sèche S., Ziada S., Braka A., Arora R., Bonnet P.
Future Medicinal Chemistry , 2016
215.
rcr_90_7_831bib215
Godoy-Castillo
2020
216.
rcr_90_7_831bib216
Kong
PhusChemChemPhys , 2018
218.
Targeted free energy estimation via learned mappings
Wirnsberger P., Ballard A.J., Papamakarios G., Abercrombie S., Racanière S., Pritzel A., Jimenez Rezende D., Blundell C.
Journal of Chemical Physics , 2020
219.
BRIDGE: An Open Platform for Reproducible High-Throughput Free Energy Simulations
Senapathi T., Suruzhon M., Barnett C.B., Essex J., Naidoo K.J.
Journal of Chemical Information and Modeling , 2020
220.
Boosting Free-Energy Perturbation Calculations with GPU-Accelerated NAMD
Chen H., Maia J.D., Radak B.K., Hardy D.J., Cai W., Chipot C., Tajkhorshid E.
Journal of Chemical Information and Modeling , 2020
221.
QligFEP: an automated workflow for small molecule free energy calculations in Q
Jespers W., Esguerra M., Åqvist J., Gutiérrez-de-Terán H.
Journal of Cheminformatics , 2019
222.
Q6: A comprehensive toolkit for empirical valence bond and related free energy calculations
Bauer P., Barrozo A., Purg M., Amrein B.A., Esguerra M., Wilson P., Major D.T., Åqvist J., Kamerlin S.C.
SoftwareX , 2018
224.
Impact of Protein Preparation on Resulting Accuracy of FEP Calculations
Shih A.Y., Hack M., Mirzadegan T.
Journal of Chemical Information and Modeling , 2020
226.
Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects
Schindler C.E., Baumann H., Blum A., Böse D., Buchstaller H., Burgdorf L., Cappel D., Chekler E., Czodrowski P., Dorsch D., Eguida M.K., Follows B., Fuchß T., Grädler U., Gunera J., et. al.
Journal of Chemical Information and Modeling , 2020
227.
OPLS3e: Extending Force Field Coverage for Drug-Like Small Molecules
Roos K., Wu C., Damm W., Reboul M., Stevenson J.M., Lu C., Dahlgren M.K., Mondal S., Chen W., Wang L., Abel R., Friesner R.A., Harder E.D.
Journal of Chemical Theory and Computation , 2019
228.
Application of Free Energy Perturbation for the Design of BACE1 Inhibitors
Ciordia M., Pérez-Benito L., Delgado F., Trabanco A.A., Tresadern G.
Journal of Chemical Information and Modeling , 2016
229.
Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations
Cournia Z., Allen B., Sherman W.
Journal of Chemical Information and Modeling , 2017
230.
rcr_90_7_831bib230
Li
Proc. Natl Acad. Sci. USA , 2020
231.
Absolute Binding Free Energy Calculation and Design of a Subnanomolar Inhibitor of Phosphodiesterase-10
Li Z., Huang Y., Wu Y., Chen J., Wu D., Zhan C., Luo H.
Journal of Medicinal Chemistry , 2019
233.
Computationally Empowered Workflow Identifies Novel Covalent Allosteric Binders for KRAS G12C
Mortier J., Friberg A., Badock V., Moosmayer D., Schroeder J., Steigemann P., Siegel F., Gradl S., Bauser M., Hillig R.C., Briem H., Eis K., Bader B., Nguyen D., Christ C.D., et. al.
ChemMedChem , 2020
234.
Combining in silico and in vitro approaches to identification of potent inhibitor against phospholipase A2 (PLA2)
Chinnasamy S., Selvaraj G., Selvaraj C., Kaushik A.C., Kaliamurthi S., Khan A., Singh S.K., Wei D.
International Journal of Biological Macromolecules , 2020
236.
Cyclin dependent kinase (CDK) inhibitors as anticancer drugs: Recent advances (2015-2019).
Sánchez-Martínez C., Lallena M.J., Sanfeliciano S.G., de Dios A.
Bioorganic and Medicinal Chemistry Letters , 2019
237.
Rusina Polina Vladimirovna, Titov Ilya Yur'evich, Panova Maria Vyacheslavovna, Stroylov Viktor Sergeevich, Abdyusheva Yana Rasil'evna, Murlatova Elizaveta Yu, Svitanko Igor, Novikov Fedor Nikolaevich
Mendeleev Communications , 2020
238.
Tubulin: an example of targeted chemotherapy
Seligmann J., Twelves C.
Future Medicinal Chemistry , 2013
239.
Tsyganov Dmitry Vadimovich, Semenova Marina Nikolaevna, Konyushkin Leonid Dmitrievich, Ushkarov Vladimir Igorevich, Raihstat Mikhail Mikhailovich, Semenov Victor Vladimirovich
Mendeleev Communications , 2019
241.
rcr_90_7_831bib241
Mirzaei
Iran. J. Basic. Med. Sci. , 2020
242.
New series of isoxazole derivatives targeting EGFR-TK: Synthesis, molecular modeling and antitumor evaluation
Warda E.T., Shehata I.A., El-Ashmawy M.B., El-Gohary N.S.
Bioorganic and Medicinal Chemistry , 2020
243.
Computational modeling and target synthesis of monomethoxy-substituted o-diphenylisoxazoles with unexpectedly high antimitotic microtubule destabilizing activity
Stroylov V.S., Svitanko I.V., Maksimenko A.S., Kislyi V.P., Semenova M.N., Semenov V.V.
Bioorganic and Medicinal Chemistry Letters , 2020
244.
CL-FEP: An End-State Free Energy Perturbation Approach
Ruiz-Blanco Y.B., Sanchez-Garcia E.
Journal of Chemical Theory and Computation , 2020
245.
Computational Fluorine Scanning Using Free-Energy Perturbation
Wade A.D., Rizzi A., Wang Y., Huggins D.J.
Journal of Chemical Information and Modeling , 2019
246.
Combined Linear Interaction Energy and Alchemical Solvation Free-Energy Approach for Protein-Binding Affinity Computation
Rifai E.A., Ferrario V., Pleiss J., Geerke D.P.
Journal of Chemical Theory and Computation , 2020
247.
Optimization of Protein–Ligand Electrostatic Interactions Using an Alchemical Free-Energy Method
248.
Computer-aided molecular design: An introduction and review of tools, applications, and solution techniques
Austin N.D., Sahinidis N.V., Trahan D.W.
Chemical Engineering Research and Design , 2016
249.
rcr_90_7_831bib249
Papadopoulos
2018
250.
The application of quantum mechanics in structure-based drug design
254.
Ankudinov Nikita Mikhailovich, Perekalin Dmitrii Sergeevich
Mendeleev Communications , 2019
255.
An explicit account of solvation is essential for modeling Suzuki–Miyaura coupling in protic solvents
Zeifman A.A., Novikov F.N., Stroylov V.S., Stroganov O.V., Svitanko I.V., Chilov G.G.
Dalton Transactions , 2015
261.
Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis
Amar Y., Schweidtmann A., Deutsch P., Cao L., Lapkin A.
Chemical Science , 2019
262.
rcr_90_7_831bib262
Henle
JACS , 2020
265.
Asymmetric Catalysis in the Pharmaceutical Industry
Hawkins J.M., Watson T.J.
Angewandte Chemie - International Edition , 2004
266.
rcr_90_7_831bib267
Reid
JACS , 2019
268.
Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning
Zahrt A.F., Henle J.J., Rose B.T., Wang Y., Darrow W.T., Denmark S.E.
Science , 2019
269.
Palladium-Catalyzed Atroposelective Coupling–Cyclization of 2-Isocyanobenzamides to Construct Axially Chiral 2-Aryl- and 2,3-Diarylquinazolinones
Teng F., Yu T., Peng Y., Hu W., Hu H., He Y., Luo S., Zhu Q.
Journal of the American Chemical Society , 2021
270.
Kolos Andrey Vladimirovich, Perekalin Dmitrii Sergeevich
Mendeleev Communications , 2020
272.
Effective Chirality Transfer in Ionic Liquids through Ion-Pairing Effects
Schulz P., Müller N., Bösmann A., Wasserscheid P.
Angewandte Chemie - International Edition , 2007
273.
Zeifman Alexey A, Stroylov Viktor Sergeevich, Titov Ilya Yur'evich, Novikov Fedor Nikolaevich, Stroganov Oleg Valentinovich, Svitanko Igor, Chilov Ghermes Grigor'evich
Mendeleev Communications , 2015
274.
A DFT Perspective on Diels–Alder Organocatalysts Based on Substituted Phosphoramides
Wåhlander J., Amedjkouh M., Balcells D.
European Journal of Organic Chemistry , 2018
275.
rcr_90_7_831bib276
Tokunaga
JACS , 2004
277.
Ananikov Valentin Pavlovich, Khokhlova Elena Aleksandrovna, Egorov Mikhail Petrovich, Sakharov Alexey Mikhailovich, Zlotin Sergei Grigorievich, Kucherov Alexei Viktorovich, Kustov Leonid Modestovich, Gening Marina Leonidovna, Nifantiev Nikolay Eduardovich
Mendeleev Communications , 2015
278.
A Chiral Solvent Effect in Asymmetric Organocatalysis
North M., Villuendas P.
Organic Letters , 2010
279.
Kochetkov Sergei Victorovich, Kucherenko Alexander Sergeevich, Zlotin Sergei Grigorievich
Mendeleev Communications , 2015
280.
Asymmetric hydrogenation catalysis via ion-pairing in chiral ionic liquids
Wagner V., Schulz P.S., Wasserscheid P.
Journal of Molecular Liquids , 2014
281.
Synthetic and computational assessment of a chiral metal–organic framework catalyst for predictive asymmetric transformation
Canivet J., Bernoud E., Bonnefoy J., Legrand A., Todorova T.K., Quadrelli E.A., Mellot-Draznieks C.
Chemical Science , 2020
282.
Accurate Diels-Alder Energies and Endo Selectivity in Ionic Liquids Using the OPLS-VSIL Force Field
Velez C., Doherty B., Acevedo O.
International Journal of Molecular Sciences , 2020
284.
Enzyme-catalysed [4+2] cycloaddition is a key step in the biosynthesis of spinosyn A
285.
The structure of SpnF, a standalone enzyme that catalyzes [4 + 2] cycloaddition
Fage C.D., Isiorho E.A., Liu Y., Wagner D.T., Liu H., Keatinge-Clay A.T.
Nature Chemical Biology , 2015
286.
Current developments and challenges in the search for a naturally selected Diels-Alderase
Kim H.J., Ruszczycky M.W., Liu H.
Current Opinion in Chemical Biology , 2012
288.
Impact of scaffold rigidity on the design and evolution of an artificial Diels-Alderase
Preiswerk N., Beck T., Schulz J.D., Milovník P., Mayer C., Siegel J.B., Baker D., Hilvert D.
Proceedings of the National Academy of Sciences of the United States of America , 2014
291.
Dynamically Complex [6+4] and [4+2] Cycloadditions in the Biosynthesis of Spinosyn A
Patel A., Chen Z., Yang Z., Gutiérrez O., Liu H., Houk K.N., Singleton D.A.
Journal of the American Chemical Society , 2016
292.
Natural population analysis
Reed A.E., Weinstock R.B., Weinhold F.
Journal of Chemical Physics , 1985
293.
Quantifying Possible Routes for SpnF-Catalyzed Formal Diels–Alder Cycloaddition
Medvedev M.G., Zeifman A.A., Novikov F.N., Bushmarinov I.S., Stroganov O.V., Titov I.Y., Chilov G.G., Svitanko I.V.
Journal of the American Chemical Society , 2017
294.
Dynamical Trajectory Study of the Transannular [6+4] and Ambimodal Cycloaddition in the Biosynthesis of Heronamides
295.
Expanding the Frontiers of Higher-Order Cycloadditions
McLeod D., Thøgersen M.K., Jessen N.I., Jørgensen K.A., Jamieson C.S., Xue X., Houk K.N., Liu F., Hoffmann R.
Accounts of Chemical Research , 2019
296.
Biocatalytic Strategies towards [4+2] Cycloadditions.
Lichman B.R., O'Connor S.E., Kries H.
Chemistry - A European Journal , 2019
297.
Enzyme-catalysed [6+4] cycloadditions in the biosynthesis of natural products
Zhang B., Wang K.B., Wang W., Wang X., Liu F., Zhu J., Shi J., Li L.Y., Han H., Xu K., Qiao H.Y., Zhang X., Jiao R.H., Houk K.N., Liang Y., et. al.
Nature , 2019
298.
Kuznetsov Nikolai Yur'evich, Malishev Vadim I, Medvedev Michael G, Bubnov Yurii Nikolaevich
Mendeleev Communications , 2019
299.
Investigation of the mechanism of the SpnF-catalyzed [4+2]-cycloaddition reaction in the biosynthesis of spinosyn A
Jeon B., Ruszczycky M.W., Russell W.K., Lin G., Kim N., Choi S., Wang S., Liu Y., Patrick J.W., Russell D.H., Liu H.
Proceedings of the National Academy of Sciences of the United States of America , 2017
300.
rcr_90_7_831bib301
Yu
JACS , 2015
301.
Design of Protein Catalysts
Hilvert D.
Annual Review of Biochemistry , 2013
302.
Computational Enzyme Design
Kiss G., Çelebi-Ölçüm N., Moretti R., Baker D., Houk K.N.
Angewandte Chemie - International Edition , 2013
303.
rcr_90_7_831bib304
Zheng
J. Org. Chem. , 2017
304.
Genome-Mined Diels–Alderase Catalyzes Formation of the cis-Octahydrodecalins of Varicidin A and B
Tan D., Jamieson C.S., Ohashi M., Tang M., Houk K.N., Tang Y.
Journal of the American Chemical Society , 2019
306.
Quantum chemical methods in charge density studies from X-ray diffraction data
Korlyukov A.A., Nelyubina Y.V.
Russian Chemical Reviews , 2019
308.
The NMR contribution to protein–protein networking in Fe–S protein maturation
Banci L., Camponeschi F., Ciofi-Baffoni S., Piccioli M.
Journal of Biological Inorganic Chemistry , 2018
309.
NMR screening and studies of target - Ligand interactions
Polshakov V.I., Batuev E.A., Mantsyzov A.B.
Russian Chemical Reviews , 2019
311.
Semenov Valentin Alexandrovich, Rusakov Yury Yur'evich, Samultsev Dmitriy Olegovich, Krivdin Leonid Borisovich
Mendeleev Communications , 2019
312.
Computational Multinuclear NMR of Platinum Complexes: A Relativistic Four-Component Study
Semenov V.A., Samultsev D.O., Rusakova I.L., Krivdin L.B.
Journal of Physical Chemistry A , 2019
313.
Semenov Valentin Alexandrovich, Samultsev Dmitriy Olegovich, Krivdin Leonid Borisovich
Mendeleev Communications , 2020
314.
5-Fluorouracil: mechanisms of action and clinical strategies
Longley D.B., Harkin D.P., Johnston P.G.
Nature Reviews Cancer , 2003
315.
Vogt Natalja Yuryevna, Ksenafontov Denis Nikolaevich, Savelev Denis Sergeevich, Rykov Anatoly Nikolaevich
Mendeleev Communications , 2020
316.
Addressing Reversibility of R-NHC Coupling on Palladium: Is Nano-to-Molecular Transition Possible for the Pd/NHC System?
Denisova E.A., Eremin D.B., Gordeev E.G., Tsedilin A.M., Ananikov V.P.
Inorganic Chemistry , 2019
317.
Electron microscopy dataset for the recognition of nanoscale ordering effects and location of nanoparticles
319.
Liquid-Phase Hydrogenation of Internal and Terminal Alkynes on Pd–Ag/Al2O3 Catalyst
Rassolov A.V., Bragina G.O., Baeva G.N., Smirnova N.S., Kazakov A.V., Mashkovsky I.S., Stakheev A.Y.
Kinetics and Catalysis , 2019
320.
Rassolov Alexander Viktorovich, Krivoruchenko Dmitry Sergeevich, Medvedev Michael G, Mashkovsky Igor' Sergeevich, Stakheev Aleksandr Yurevich, Svitanko Igor
Mendeleev Communications , 2017
321.
PdAg Alloy Nanocatalysts: Toward Economically Viable Nitrite Reduction in Drinking Water
Troutman J.P., Li H., Haddix A.M., Kienzle B.A., Henkelman G., Humphrey S.M., Werth C.J.
ACS Catalysis , 2020
323.
Rassolov Alexander Viktorovich, Baeva Galina Nikolaevna, Mashkovsky Igor' Sergeevich, Stakheev Aleksandr Yurevich
Mendeleev Communications , 2018
325.
Tuning strain effect and surface composition in PdAu hollow nanospheres as highly efficient ORR electrocatalysts and SERS substrates
Jiao W., Chen C., You W., Chen G., Xue S., Zhang J., Liu J., Feng Y., Wang P., Wang Y., Wen H., Che R.
Applied Catalysis B: Environmental , 2020
326.
Highly selective and stable PdNi catalyst derived from layered double hydroxides for partial hydrogenation of acetylene
327.
Mashkovsky Igor' Sergeevich, Markov Pavel Viktorovich, Bragina Galina Olegovna, Baeva Galina Nikolaevna, Rassolov Alexander Viktorovich, Bukhtiyarov Andrey Valerievich, Prosvirin Igor P, Bukhtiyarov Valerii Ivanovich, Stakheev Aleksandr Yurevich
Mendeleev Communications , 2018
328.
QSAR model for ethylene polymerisation catalysed by supported bis(imino)pyridine iron complexes
Cruz V.L., Martinez J., Martinez-Salazar J., Ramos J., Reyes M.L., Toro-Labbe A., Gutierrez-Oliva S.
Polymer , 2007
331.
rcr_90_7_831bib332
Fan
ACSCatal. , 2020
332.
3D printing in chemical engineering and catalytic technology: structured catalysts, mixers and reactors
Parra-Cabrera C., Achille C., Kuhn S., Ameloot R.
Chemical Society Reviews , 2018
333.
Stereolithography 3D-Printed Catalytically Active Devices in Organic Synthesis
Rossi S., Puglisi A., Raimondi L.M., Benaglia M.
Catalysts , 2020
334.
Design and Structure-Function Characterization of 3D Printed Synthetic Porous Biomaterials for Tissue Engineering
Kelly C.N., Miller A.T., Hollister S.J., Guldberg R.E., Gall K.
Advanced healthcare materials , 2017
335.
3D printing of a heterogeneous copper-based catalyst
Tubío C.R., Azuaje J., Escalante L., Coelho A., Guitián F., Sotelo E., Gil A.
Journal of Catalysis , 2016
336.
3D Printed Palladium Catalyst for Suzuki‐Miyaura Cross‐coupling Reactions
Bulatov E., Lahtinen E., Kivijärvi L., Hey‐Hawkins E., Haukka M.
ChemCatChem , 2020
337.
Formulation of Metal–Organic Framework Inks for the 3D Printing of Robust Microporous Solids toward High-Pressure Gas Storage and Separation
338.
3D-Printed Acrylonitrile Butadiene Styrene-Metal Organic Framework Composite Materials and Their Gas Storage Properties
Bible M., Sefa M., Fedchak J.A., Scherschligt J., Natarajan B., Ahmed Z., Hartings M.R.
3D Printing and Additive Manufacturing , 2018
340.
3D-Printing of Pure Metal–Organic Framework Monoliths
Lim G.J., Wu Y., Shah B.B., Koh J.J., Liu C.K., Zhao D., Cheetham A.K., Wang J., Ding J.
ACS Materials Letters , 2019
341.
rcr_90_7_831bib342
Thakkar
ACS Appl. Mater. Inter. , 2017
342.
Multiscale investigation of adsorption properties of novel 3D printed UTSA-16 structures
Grande C.A., Blom R., Middelkoop V., Matras D., Vamvakeros A., Jacques S.D., Beale A.M., Di Michiel M., Anne Andreassen K., Bouzga A.M.
Chemical Engineering Journal , 2020
343.
UTSA-16 as an efficient microporous catalyst for CO2 conversion to cyclic carbonates
Song L., Zhang X., Chen C., Liu X., Zhang N.
Microporous and Mesoporous Materials , 2017
344.
rcr_90_7_831bib345
Jiang
Chem. Eng. J. , 2020
345.
Macroscale Control of Reactivity using 3D Printed Materials with Intrinsic Catalytic Properties
Manzano J.S., Wang H., Qi L., Slowing I.I.
Applied Catalysis A: General , 2020
346.
Chemistry from 3D printed objects
Hartings M.R., Ahmed Z.
Nature Reviews Chemistry , 2019
347.
Combining additive manufacturing and catalysis: a review
Hurt C., Brandt M., Priya S.S., Bhatelia T., Patel J., Selvakannan P., Bhargava S.
Catalysis Science and Technology , 2017
348.
Direct 3D Printing of Catalytically Active Structures
Manzano J.S., Weinstein Z.B., Sadow A.D., Slowing I.I.
ACS Catalysis , 2017
349.
Porphyrinic Zirconium Metal–Organic Frameworks (MOFs) as Heterogeneous Photocatalysts for PET‐RAFT Polymerization and Stereolithography
Zhang L., Shi X., Zhang Z., Kuchel R.P., Namivandi‐Zangeneh R., Corrigan N., Jung K., Liang K., Boyer C.
Angewandte Chemie - International Edition , 2021
351.
Computational Approach to Molecular Catalysis by 3d Transition Metals: Challenges and Opportunities
Vogiatzis K.D., Polynski M.V., Kirkland J.K., Townsend J., Hashemi A., Liu C., Pidko E.A.
Chemical Reviews , 2018