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Current Radiopharmaceuticals


ISSN (Print): 1874-4710
ISSN (Online): 1874-4729

Review Article

Discovery and Design of Radiopharmaceuticals by In silico Methods

Author(s): Maryam Salahinejad, David A. Winkler* and Fereshteh Shiri

Volume 15, Issue 4, 2022

Published on: 19 September, 2022

Page: [271 - 319] Pages: 49

DOI: 10.2174/1874471015666220831091403

Price: $65


There has been impressive growth in the use of radiopharmaceuticals for therapy, selective toxic payload delivery, and noninvasive diagnostic imaging of disease. The increasing timeframes and costs involved in the discovery and development of new radiopharmaceuticals have driven the development of more efficient strategies for this process. Computer-Aided Drug Design (CADD) methods and Machine Learning (ML) have become more effective over the last two decades for drug and materials discovery and optimization. They are now fast, flexible, and sufficiently accurate to accelerate the discovery of new molecules and materials.

Radiopharmaceuticals have also started to benefit from rapid developments in computational methods. Here, we review the types of computational molecular design techniques that have been used for radiopharmaceuticals design. We also provide a thorough examination of success stories in the design of radiopharmaceuticals, and the strengths and weaknesses of the computational methods.

We begin by providing a brief overview of therapeutic and diagnostic radiopharmaceuticals and the steps involved in radiopharmaceuticals design and development. We then review the computational design methods used in radiopharmaceutical studies, including molecular mechanics, quantum mechanics, molecular dynamics, molecular docking, pharmacophore modelling, and datadriven ML. Finally, the difficulties and opportunities presented by radiopharmaceutical modelling are highlighted. The review emphasizes the potential of computational design methods to accelerate the production of these very useful clinical radiopharmaceutical agents and aims to raise awareness among radiopharmaceutical researchers about computational modelling and simulation methods that can be of benefit to this field.

Keywords: Radiopharmaceutical, PET, SPECT, computational chemistry, machine learning, computer-aided drug design.

Graphical Abstract
Blower, P.J. The future direction of radiopharmaceutical development. In: McCready, R.; Gnanasegaran, G.; Bomanji, J.; Eds. History of Radionuclide Studies in the UK, Springer: Cham, 2016; pp. 141-148.
Historical timeline: Important moments in the history of nuclear medicine: Society of nuclear medicine and molecular imaging. 2020. Available from: SNMMI/Co ntent.aspx?ItemNumber=4175
Dash, A. Targeted radionuclide therapy- An overview. Curr. Radiopharm., 2013, 6(3), 152-180.
[] [PMID: 24059327]
Lin, X.; Li, X.; Lin, X. A review on applications of computational methods in drug screening and design. Molecules, 2020, 25(6), 1375.
[] [PMID: 32197324]
Talip, Z.; Favaretto, C.; Geistlich, S.; Meulen, N.P. A step by step guide for the novel radiometal production for medical applications: Case studies with 68Ga, 44Sc, 177Lu and 161Tb. Molecules, 2020, 25(4), 966.
[] [PMID: 32093425]
Ahmad, M. Molybdenum-99/technetium-99m management: Race against time. Ann. Nucl. Med., 2011, 25(9), 677-679.
[] [PMID: 21728046]
Liu, S. Bifunctional coupling agents for radiolabeling of biomolecules and target-specific delivery of metallic radionuclides. Adv. Drug Deliv. Rev., 2008, 60(12), 1347-1370.
[] [PMID: 18538888]
Peltek, O.O.; Muslimov, A.R.; Zyuzin, M.V.; Timin, A.S. Current outlook on radionuclide delivery systems: From design consideration to translation into clinics. J. Nanobiotechnology, 2019, 17(1), 90.
[] [PMID: 31434562]
Drozdovitch, V.; Brill, A.B.; Callahan, R.J.; Clanton, J.A.; DePietro, A.; Goldsmith, S.J.; Greenspan, B.S.; Gross, M.D.; Hays, M.T.; Moore, S.C.; Ponto, J.A.; Shreeve, W.W.; Melo, D.R.; Linet, M.S.; Simon, S.L. Use of radiopharmaceuticals in diagnostic nuclear medicine in the United States: 1960-2010. Health Phys., 2015, 108(5), 520-537.
[] [PMID: 25811150]
Holly, T.A.; Abbott, B.G.; Al-Mallah, M.; Calnon, D.A.; Cohen, M.C.; DiFilippo, F.P.; Ficaro, E.P.; Freeman, M.R.; Hendel, R.C.; Jain, D.; Leonard, S.M.; Nichols, K.J.; Polk, D.M.; Soman, P. Single photon emission computed tomography. J. Nucl. Cardiol., 2010, 17(5), 941-973.
[] [PMID: 20552312]
Duatti, A. Review on 99mTc radiopharmaceuticals with emphasis on new advancements. Nucl. Med. Biol., 2020, 92, 202-216.
[PMID: 32475681]
Suzuki, M.; Koyama, H.; Ishii, H.; Kato, K. Ögren, M.; Doi, H. Green Process of Three-Component Prostaglandin Synthesis and Rapid 11c Labelings for Short-Lived Pet Tracers; IntechOpen: London 2018.
Shukla, A.K.; Kumar, U. Positron emission tomography: An overview. J. Med. Phys., 2006, 31(1), 13-21.
[] [PMID: 21206635]
Berger, A. Positron emission tomography. BMJ, 2003, 326(7404), 1449.
[] [PMID: 12829560]
Zhu, A.; Lee, D.; Shim, H. Metabolic positron emission tomography imaging in cancer detection and therapy response. Semin. Oncol., 2011, 38(1), 55-69.
[] [PMID: 21362516]
Lau, J.; Rousseau, E.; Kwon, D.; Lin, K.S.; Bénard, F.; Chen, X. Insight into the development of PET radiopharmaceuticals for oncology. Cancers, 2020, 12(5), 1312.
[] [PMID: 32455729]
Davidson, C.Q.; Phenix, C.P.; Tai, T.C.; Khaper, N.; Lees, S.J. Searching for novel PET radiotracers: Imaging cardiac perfusion, metabolism and inflammation. Am. J. Nucl. Med. Mol. Imaging, 2018, 8(3), 200-227.
[PMID: 30042871]
Lotan, E.; Friedman, K.P.; Davidson, T.; Shepherd, T.M. Brain 18F-FDG-PET: Utility in the diagnosis of dementia and epilepsy. Isr. Med. Assoc. J., 2020, 22(3), 178-184.
[PMID: 32147984]
Valotassiou, V.; Sifakis, N.; Papatriantafyllou, J.; Angelidis, G.; Georgoulias, P. The clinical use of SPECT and PET molecular imaging in Alzheimer’s disease. In: De La Monte, S., Ed.; The Clinical Spectrum of Alzheimer’s Disease: The Charge Toward Comprehensive Diagnostic and Therapeutic Strategies;, IntechOpen: London, UK, United States of America, 2011; pp. 181-219.
Ding, E.; Lu, D.; Wei, L.; Feng, X.; Shen, J.; Xu, W. Predicting tumor recurrence using metabolic indices of 18F FDG PET/CT prior to orthotopic liver transplantationfor hepatocellular carcinoma. Oncol. Lett., 2020, 20(2), 1245-1255.
[] [PMID: 32724365]
Schindler, T.H.; Schelbert, H.R.; Quercioli, A.; Dilsizian, V. Cardiac PET imaging for the detection and monitoring of coronary artery disease and microvascular health. JACC Cardiovasc. Imaging, 2010, 3(6), 623-640.
[] [PMID: 20541718]
Vaart, M.G.; Meerwaldt, R.; Slart, R.H.J.A.; Dam, G.M.; Tio, R.A.; Zeebregts, C.J. Application of PET/SPECT imaging in vascular disease. Eur. J. Vasc. Endovasc. Surg., 2008, 35(5), 507-513.
[] [PMID: 18180182]
Fischer, B.M.B.; Mortensen, J. Hّjgaard, L. Positron emission tomography in the diagnosis and staging of lung cancer: A systematic, quantitative review. Lancet Oncol., 2001, 2(11), 659-666.
[] [PMID: 11902536]
Volpi, S.; Ali, J.M.; Tasker, A.; Peryt, A.; Aresu, G.; Coonar, A.S. The role of positron emission tomography in the diagnosis, staging and response assessment of non-small cell lung cancer. Ann. Transl. Med., 2018, 6(5), 95.
[] [PMID: 29666818]
Jiemy, W.F.; Heeringa, P.; Kamps, J.A.A.M.; Laken, C.J.; Slart, R.H.J.A.; Brouwer, E. Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) imaging of macrophages in large vessel vasculitis: Current status and future prospects. Autoimmun. Rev., 2018, 17(7), 715-726.
[] [PMID: 29729443]
Bailey, D.L.; Karp, J.S.; Surti, S. Physics and instrumentation in PET. In: Valk, P.E.B.D.; Townsend, D.W.; Maisey, M.N., Eds.; Positron Emission Tomography: Basic Science and Clinical Practice., Springer-Verlag: London, 2005; pp. 13-39.
Khalil, M.M. Basic Science of PET Imaging; Springer: Cham, 2017, 1, pp. 619.
Mettler, F.A.; Guiberteau, M.J. Essentials of Nuclear Medicine and Molecular Imaging E-Book, 7th ed; Elsevier Health Sciences, 2018.
Knapp, F.R.; Dash, A. Introduction: Radiopharmaceuticals play an important role in both diagnostic and therapeutic nuclear medicine. In: Radiopharmaceuticals for Therapy; Springer: New Delhi, 2016, pp. 3-23.
Le, D. Radiopharmaceuticals for therapy. J. Nucl. Med., 2017, 58(9), 1526.
[] [PMID: 28546335]
Sgouros, G.; Bodei, L.; McDevitt, M.R.; Nedrow, J.R. Radiopharmaceutical therapy in cancer: Clinical advances and challenges. Nat. Rev. Drug Discov., 2020, 19(9), 589-608.
[] [PMID: 32728208]
Cutler, C.S.; Hennkens, H.M.; Sisay, N.; Huclier, M.S.; Jurisson, S.S. Radiometals for combined imaging and therapy. Chem. Rev., 2013, 113(2), 858-883.
[] [PMID: 23198879]
Herrmann, K.; Schwaiger, M.; Lewis, J.S.; Solomon, S.B.; McNeil, B.J.; Baumann, M.; Gambhir, S.S.; Hricak, H.; Weissleder, R. Radiotheranostics: A roadmap for future development. Lancet Oncol., 2020, 21(3), e146-e156.
[] [PMID: 32135118]
Schenone, M. Dančík, V.; Wagner, B.K.; Clemons, P.A. Target identification and mechanism of action in chemical biology and drug discovery. Nat. Chem. Biol., 2013, 9(4), 232-240.
[] [PMID: 23508189]
Lindsay, M.A. Target discovery. Nat. Rev. Drug Discov., 2003, 2(10), 831-838.
[] [PMID: 14526386]
Dimastromatteo, J.; Kelly, K.A. Target identification, lead discovery, and optimization. In: Lewis, J.W.A.; Zeglis, B., Eds.; Radiopharmaceutical Chemistry; Springer: Cham, 2019; pp. 555-567.
Gashaw, I.; Ellinghaus, P.; Sommer, A.; Asadullah, K. What makes a good drug target? Drug Discov. Today, 2011, 16(23-24), 1037-1043.
[] [PMID: 21945861]
Katsila, T.; Spyroulias, G.A.; Patrinos, G.P.; Matsoukas, M.T. Computational approaches in target identification and drug discovery. Comput. Struct. Biotechnol. J., 2016, 14, 177-184.
[] [PMID: 27293534]
Mathai, N.; Chen, Y.; Kirchmair, J. Validation strategies for target prediction methods. Brief. Bioinform., 2020, 21(3), 791-802.
[] [PMID: 31220208]
Hessler, G.; Grebner, C.; Matter, H. Computational approaches for target inference. In: Plowright, A.T., Ed.; Target Discovery and Validation: Methods and Strategies for Drug Discovery. Methods and Principles in Medicinal Chemistry; Wiley‐VCH Verlag GmbH & Co. KGaA: Weinheim, 2019; pp. 277-322.
Lee, Y.S. Radiopharmaceuticals for molecular imaging. Open Nucl. Med. J., 2010, 2(1), 178-185.
Vallabhajosula, S. Molecular Imaging: Radiopharmaceuticals for PET and SPECT; Springer: Berlin, Heidelberg, 2009, 1, pp. 372.
Tolmachev, V. Choice of radionuclides and radiolabelling techniques. In: Stigbrand, T.; Carlsson, J.; Adams, G.P., Eds. Targeted Radionuclide Tumor Therapy: Biological Aspects; Springer Netherlands: Dordrecht, 2008; pp. 145-174.
Chaturvedi, S.; Mishra, A.K. Vectors for the delivery of radiopharmaceuticals in cancer therapeutics. Ther. Deliv., 2014, 5(8), 893-912.
[] [PMID: 25337647]
Okoye, N.C.; Baumeister, J.E.; Najafi Khosroshahi, F.; Hennkens, H.M.; Jurisson, S.S. Chelators and metal complex stability for radiopharmaceutical applications. Radiochim. Acta, 2019, 107(9-11), 1087-1120.
Liu, S.; Edwards, D.S. Bifunctional chelators for therapeutic lanthanide radiopharmaceuticals. Bioconjug. Chem., 2001, 12(1), 7-34.
[] [PMID: 11170362]
Price, E.W.; Orvig, C. Matching chelators to radiometals for radiopharmaceuticals. Chem. Soc. Rev., 2014, 43(1), 260-290.
[] [PMID: 24173525]
Baranyai, Z. Tircsَ, G.; Rِsch, F. The use of the macrocyclic chelator DOTA in radiochemical separations. Eur. J. Inorg. Chem., 2020, 2020(1), 36-56.
Ge, J.; Zhang, Q.; Zeng, J.; Gu, Z.; Gao, M. Radiolabeling nanomaterials for multimodality imaging: New insights into nuclear medicine and cancer diagnosis. Biomaterials, 2020, 228, 119553.
[] [PMID: 31689672]
Pellico, J.; Gawne, P.J. T M de Rosales, R. Radiolabelling of nanomaterials for medical imaging and therapy. Chem. Soc. Rev., 2021, 50(5), 3355-3423.
[] [PMID: 33491714]
Shi, S.; Xu, C.; Yang, K.; Goel, S.; Valdovinos, H.F.; Luo, H.; Ehlerding, E.B.; England, C.G.; Cheng, L.; Chen, F.; Nickles, R.J.; Liu, Z.; Cai, W. Chelator-free radiolabeling of nanographene: Breaking the stereotype of chelation. Angew. Chem. Int. Ed., 2017, 56(11), 2889-2892.
[] [PMID: 28170126]
Neese, F.; Atanasov, M.; Bistoni, G.; Maganas, D.; Ye, S. Chemistry and quantum mechanics in 2019: Give us insight and numbers. J. Am. Chem. Soc., 2019, 141(7), 2814-2824.
[] [PMID: 30629883]
Comba, P.; Kerscher, M. Computation of structures and properties of transition metal compounds. Coord. Chem. Rev., 2009, 253(5-6), 564-574.
Chaube, S.; Goverapet Srinivasan, S.; Rai, B. Applied machine learning for predicting the lanthanide ligand binding affinities. Sci. Rep., 2020, 10(1), 14322.
[] [PMID: 31913322]
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, G.A.; Winkler, D.A.; Agrafiotis, D.; Cherkasov, A.; Tropsha, A. QSAR without borders. Chem. Soc. Rev., 2020, 49(11), 3525-3564.
[] [PMID: 32356548]
Kristensen, K. Nørbygaard, E. Safety and Efficacy of Radiopharmaceuticals; Martinus Nijhoff; Kluwer: Boston, 2012.
Kunos, C.A.; Howells, R.; Chauhan, A.; Myint, Z.W.; Bernard, M.E.; El Khouli, R.; Capala, J. Radiopharmaceutical validation for clinical use. Front. Oncol., 2021, 11, 630827.
[] [PMID: 33747951]
Sgouros, G.; Hobbs, R.F.; Abou, D.S. The role of preclinical models in radiopharmaceutical therapy. Am. Soc. Clin. Oncol. Educ. Book, 2014, (34), e121-e125.
[] [PMID: 24857091]
Pelkonen, O.; Turpeinen, M.; Raunio, H. In vivo-in vitro-in silico pharmacokinetic modelling in drug development: Current status and future directions. Clin. Pharmacokinet., 2011, 50(8), 483-491.
[] [PMID: 21740072]
Cheng, F.; Li, W.; Liu, G.; Tang, Y. In silico ADMET prediction: Recent advances, current challenges and future trends. Curr. Top. Med. Chem., 2013, 13(11), 1273-1289.
[] [PMID: 23675935]
Moroy, G.; Martiny, V.Y.; Vayer, P.; Villoutreix, B.O.; Miteva, M.A. Toward in silico structure based ADMET prediction in drug discovery. Drug Discov. Today, 2012, 17(1-2), 44-55.
[] [PMID: 22056716]
Tao, L.; Zhang, P.; Qin, C.; Chen, S.Y.; Zhang, C.; Chen, Z.; Zhu, F.; Yang, S.Y.; Wei, Y.Q.; Chen, Y.Z. Recent progresses in the exploration of machine learning methods as in silico ADME prediction tools. Adv. Drug Deliv. Rev., 2015, 86, 83-100.
[] [PMID: 26037068]
Kar, S.; Leszczynski, J. Open access in silico tools to predict the ADMET profiling of drug candidates. Expert Opin. Drug Discov., 2020, 15(12), 1473-1487.
[] [PMID: 32735147]
Acharya, C.; Coop, A.; Polli, J.E.; Mackerell, A.D., Jr Recent advances in ligand based drug design: Relevance and utility of the conformationally sampled pharmacophore approach. Curr. Computeraided Drug Des., 2011, 7(1), 10-22.
[] [PMID: 20807187]
Montfort, R.L.M.; Workman, P. Structure-based drug design: Aiming for a perfect fit. Essays Biochem., 2017, 61(5), 431-437.
[] [PMID: 29118091]
Batool, M.; Ahmad, B.; Choi, S. A structure based drug discovery paradigm. Int. J. Mol. Sci., 2019, 20(11), 2783.
[] [PMID: 31174387]
Schaduangrat, N.; Lampa, S.; Simeon, S.; Gleeson, M.P.; Spjuth, O.; Nantasenamat, C. Towards reproducible computational drug discovery. J. Cheminform., 2020, 12(1), 9.
[] [PMID: 33430992]
Schaduangrat, N.; Anuwongcharoen, N.; Phanus, U.C.; Sriwanichpoom, N.; Wikberg, J.E.; Nantasenamat, C. Proteochemometric modeling for drug repositioning. In: Roy, K., Ed.; In Silico Drug Design; Academic Press: Cambridge, Massachusetts, 2019; pp. 281-302.
Chahal, V.; Nirwan, S.; Kakkar, R. Combined approach of homology modelling, molecular dynamics, and docking: Computer aided drug discovery. Phys. Sci. Rev., 2019, 4, 20190066.
Cavasotto, C.N.; Phatak, S.S. Homology modeling in drug discovery: Current trends and applications. Drug Discov. Today, 2009, 14(13-14), 676-683.
[] [PMID: 19422931]
Krieger, E.; Nabuurs, S.B.; Vriend, G. Homology modeling. Methods Biochem. Anal., 2003, 44, 509-523.
[PMID: 12647402]
Haddad, Y.; Adam, V.; Heger, Z. Ten quick tips for homology modeling of high-resolution protein 3D structures. PLOS Comput. Biol., 2020, 16(4), e1007449.
[] [PMID: 32240155]
Lai, H.T.T.; Giorgetti, A.; Rossetti, G.; Nguyen, T.T.; Carloni, P.; Kranjc, A. The interplay of cholesterol and ligand binding in hTSPO from classical molecular dynamics simulations. Molecules, 2021, 26(5), 1250.
[] [PMID: 33652554]
Nodwell, M.B.; Yang, H.; Merkens, H.; Malik, N.; Čolović, M.; Björn Wagner, ; Martin, R.E.; Bénard, F.; Schaffer, P.; Britton, R. 18 F branched chain amino acids: Structure-activity relationships and PET imaging potential. J. Nucl. Med., 2019, 60(7), 1003-1009.
[] [PMID: 30683769]
Sowa, A.R.; Brooks, A.F.; Shao, X.; Henderson, B.D.; Sherman, P.; Arteaga, J.; Stauff, J.; Lee, A.C.; Koeppe, R.A.; Scott, P.J.H.; Kilbourn, M.R. Development of positron emission tomography radiotracers for the GABA transporter 1. ACS Chem. Neurosci., 2018, 9(11), 2767-2773.
[] [PMID: 29763549]
Ferreira, L.L.G.; Andricopulo, A.D. Editorial: Chemoinformatics approaches to structure and ligand based drug design. Front. Pharmacol., 2018, 9(1416), 1416.
[] [PMID: 30564124]
Arodola, O.A.; Soliman, M.E.S. Quantum mechanics implementation in drug-design workflows: Does it really help? Drug Des. Devel. Ther., 2017, 11, 2551-2564.
[] [PMID: 28919707]
Pissurlenkar, R.; Shaikh, M.; Iyer, R.; Coutinho, E. Molecular mechanics force fields and their applications in drug design. Antiinfect. Agents Med. Chem., 2009, 8(2), 128-150.
Bekono, B.D.; Sona, A.N.; Eni, D.B.; Owono, L.C.; Megnassan, E.; Ntie, K.F. Molecular mechanics approaches for rational drug design: Forcefields and solvation models. Phys. Sci. Rev., 2021, p.233-54.
Tuzun, R.E.; Noid, D.W.; Sumpter, B.G. Efficient treatment of out of plane bend and improper torsion interactions in MM2, MM3, and MM4 molecular mechanics calculations. J. Comput. Chem., 1997, 18(14), 1804-1811.
Wang, J.; Wolf, R.M.; Caldwell, J.W.; Kollman, P.A.; Case, D.A. Development and testing of a general amber force field. J. Comput. Chem., 2004, 25(9), 1157-1174.
[] [PMID: 15116359]
Huang, J.; Rauscher, S.; Nawrocki, G.; Ran, T.; Feig, M.; de Groot, B.L.; Grubmüller, H.; MacKerell, A.D., Jr CHARMM36m: An improved force field for folded and intrinsically disordered proteins. Nat. Methods, 2017, 14(1), 71-73.
[] [PMID: 27819658]
Abraham, M.J.; Murtola, T.; Schulz, R. Páll, S.; Smith, J.C.; Hess, B.; Lindahl, E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 2015, 1-2, 19-25.
Harder, E.; Damm, W.; Maple, J.; Wu, C.; Reboul, M.; Xiang, J.Y.; Wang, L.; Lupyan, D.; Dahlgren, M.K.; Knight, J.L.; Kaus, J.W.; Cerutti, D.S.; Krilov, G.; Jorgensen, W.L.; Abel, R.; Friesner, R.A. OPLS3: A force field providing broad coverage of drug-like small molecules and proteins. J. Chem. Theory Comput., 2016, 12(1), 281-296.
[] [PMID: 26584231]
Cutler, C.S.; Giron, M.C.; Reichert, D.E.; Snyder, A.Z.; Herrero, P.; Anderson, C.J.; Quarless, D.A.; Koch, S.A.; Welch, M.J. Evaluation of gallium-68 tris(2-mercaptobenzyl)amine: A complex with brain and myocardial uptake. Nucl. Med. Biol., 1999, 26(3), 305-316.
[] [PMID: 10363802]
Yoshizuka, K.; Pietzsch, H.J.; Seifert, S.; Stephan, H. Quantitative structure property relationship of logP for radiopharmaceutical technetium and rhenium complexes by using molecular dynamics calculations. Solvent Extr. Res. Dev. Jpn., 2013, 20(0), 15-27.
Wei, H.; Luo, S.; Liu, G.; Yang, Y.; Jiang, S. Study of QSAR for 153 Sm complexes as bone seeking agent. J. Nucl. Radiochem., 2003, 25(2), 81-85.
Comba, P.; Daubinet, A.; Martin, B.; Pietzsch, H.J.; Stephan, H. A new molecular mechanics force field for the design of oxotechnetium(V) and oxorhenium(V) radiopharmaceuticals. J. Organomet. Chem., 2006, 691(11), 2495-2502.
Santos, C.C.L.; Ferro, F.G.; Arteaga, M.C. Ramírez, F.M.; Luna, G.M.A.; Pedraza, L.M.; Garcيa, B.R.; Ordaz, R.D. Design, preparation, in vitro and in vivo evaluation of 99mTc-N2S2-Tat(49–57)-bombesin: A target-specific hybrid radiopharmaceutical. Int. J. Pharm., 2009, 375(1-2), 75-83.
[] [PMID: 19393305]
Smith, J.S.; Isayev, O.; Roitberg, A.E. ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci. (Camb.), 2017, 8(4), 3192-3203.
[] [PMID: 28507695]
Cavasotto, C.N.; Aucar, M.G. High-throughput docking using quantum mechanical scoring. Front Chem., 2020, 8, 246.
[] [PMID: 32373579]
Fuks, L.; Gniazdowska, E.; Kozminski, P.; Mieczkowski, J. Technetium (I) tricarbonyl complexed with the N-heterocyclic aldehyde thiosemicarbazones: Potential precursors of the radiopharmaceuticals. J. Radioanal. Nucl. Chem., 2012, 292(1), 255-259.
Holland, J.P. Predicting the thermodynamic stability of zirconium radiotracers. Inorg. Chem., 2020, 59(3), 2070-2082.
[] [PMID: 31940188]
Bruchertseifer, F.; Comba, P.; Martin, B.; Morgenstern, A.; Notni, J.; Starke, M.; Wadepohl, H. First-generation bispidine chelators for 213BiIII radiopharmaceutical applications. ChemMedChem, 2020, 15(16), 1591-1600.
[] [PMID: 32613737]
Holland, J.P.; Aigbirhio, F.I.; Betts, H.M.; Bonnitcha, P.D.; Burke, P.; Christlieb, M.; Churchill, G.C.; Cowley, A.R.; Dilworth, J.R.; Donnelly, P.S.; Green, J.C.; Peach, J.M.; Vasudevan, S.R.; Warren, J.E. Functionalized bis(thiosemicarbazonato) complexes of zinc and copper: Synthetic platforms toward site-specific radiopharmaceuticals. Inorg. Chem., 2007, 46(2), 465-485.
[] [PMID: 17279826]
Dickson, C.J.; Gee, A.D.; Bennacef, I.; Gould, I.R.; Rosso, L. Further evaluation of quantum chemical methods for the prediction of non-specific binding of positron emission tomography tracers. Phys. Chem. Chem. Phys., 2011, 13(48), 21552-21557.
[] [PMID: 22052158]
Hernández V.D.; Alberto, R.; Jáuregui, H.U. Quantum chemistry calculations of technetium and rhenium compounds with application in radiopharmacy : Review. RSC Advances, 2016, 6(108), 107127-107140. [Review
Vidossich, P.; Magistrato, A. QM/MM molecular dynamics studies of metal binding proteins. Biomolecules, 2014, 4(3), 616-645.
[] [PMID: 25006697]
Senn, H.M.; Thiel, W. QM/MM methods for biomolecular systems. Angew. Chem. Int. Ed., 2009, 48(7), 1198-1229.
[] [PMID: 19173328]
Ferro, F.G. Ramيrez, F.M.; Meléndez, A.L.; Murphy, C.A.; Pedraza, L.M. Molecular recognition and stability of 99mTc-UBI 29–41 based on experimental and semiempirical results. Appl. Radiat. Isot., 2004, 61(6), 1261-1268.
[] [PMID: 15388119]
Hansen, L.; Cini, R.; Taylor, A., Jr; Marzilli, L.G. Rhenium (V) oxo complexes relevant to technetium renal imaging agents derived from mercaptoacetylglycylglycylaminobenzoic acid isomers. Structural and molecular mechanics studies. Inorg. Chem., 1992, 31(13), 2801-2808.
Marzilli, L.G.; Banaszczyk, M.G.; Hansen, L.; Kuklenyik, Z.; Cini, R.; Taylor, A.J. Linking deprotonation and denticity of chelate ligands – rhenium (V) oxo analogs of Tc-99m radiopharmaceuticals containing N2S2 chelate ligands. Inorg. Chem., 1994, 33(22), 4850-4860.
Meléndez, A.L. Ramírez, F.M.; Ferro, F.G.; de Murphy, C.A.; Pedraza, L.M.; Hnatowich, D.J. Lys and Arg in UBI: A specific site for a stable Tc-99m complex? Nucl. Med. Biol., 2003, 30(6), 605-615.
[] [PMID: 12900286]
Al-Hokbany, N.S. Synthesis and characterization of a ReO3+ complex with S- and N-donor ligands and of its 99m Tc analog. Radiochemistry, 2012, 54(3), 284-290.
Brink, A.; Kroon, R.E.; Visser, H.G.; van Rensburg, C.E.J.; Roodt, A. Designing model imino bifunctional chelators for radiopharmaceuticals - in vitro antitumor activity, photoluminescence and structural analysis. New J. Chem., 2018, 42(7), 5193-5203.
Hayes, T.R.; Bottorff, S.C.; Slocumb, W.S.; Barnes, C.L.; Clark, A.E.; Benny, P.D. Influence of bidentate ligand donor types on the formation and stability in 2 + 1 fac-[MI (CO) 3] + (M = Re, 99m Tc) complexes. Dalton Trans., 2017, 46(4), 1134-1144.
[] [PMID: 28045466]
Lipowska, M.; Cini, R.; Tamasi, G.; Xu, X.; Taylor, A.T.; Marzilli, L.G. Complexes having the fac-[M(CO)3]+ core (M=Tc, Re) useful in radiopharmaceuticals: X-ray and NMR structural characterization and density functional calculations of species containing two sp3 N donors and one sp3 O donor. Inorg. Chem., 2004, 43(24), 7774-7783.
[] [PMID: 15554642]
Safi, B.; Mertens, J.; Kersemans, K.; Geerlings, P. A critical quantum chemical and experimental study of the potentiality of direct labeling of the CN group with [99mTc(CO)3]+ or [186/188Re(CO)3]+ in CN containing biomolecules. Nucl. Med. Biol., 2008, 35(7), 747-753.
[] [PMID: 18848659]
Belyanin, M.L.; Stepanova, E.V.; Minin, S.M.; Lyshmanov, Y.B.; Filimonov, V.D. Methods of synthesis of radiopharmaceuticals based on fatty acids marked with 99mTc and perspectives of their application. Adv. Mat. Res., 2015, 1084, 400-405.
Kirby, R.A.; Pollak, A. A computer-aided radiopharmaceutical drug design study using ab initio and molecular mechanics methods. J. Mol. Model., 1997, 3(8), 294-300.
Boudreau, R.J.; Mertz, J.E. The prediction of the structure of technetium (V) complexes using density functional techniques. Nucl. Med. Biol., 1997, 24(5), 395-398.
[] [PMID: 9290073]
Neves, M.; Fausto, R. Prediction of 99mtc-biguanide complex structures and their interactions with biological molecules by molecular mechanics calculations. Nucl. Med. Biol., 1999, 26(1), 85-89.
[] [PMID: 10096506]
Thipyapong, K.; Uehara, T.; Suzuki, K.; Arano, Y.; Ruangpornvisuti, V. IR spectroscopic and DFT investigations on molecular conformations of thio-free oxo technetium (V) benzamidoxime complexes. J. Mol. Struct., 2011, 990(1-3), 152-157.
Su, J.; Xu, W.H.; Xu, C.F.; Schwarz, W.H.E.; Li, J. Theoretical studies on the photoelectron and absorption spectra of MnO4(-) and TcO4. Inorg. Chem., 2013, 52(17), 9867-9874.
[] [PMID: 23957772]
Schibli, R.; Marti, N.; Maurer, P.; Spingler, B.; Lehaire, M.L.; Gramlich, V.; Barnes, C.L. Syntheses and characterization of dicarbonyl-nitrosyl complexes of technetium(I) and rhenium(I) in aqueous media: Spectroscopic, structural, and DFT analyses. Inorg. Chem., 2005, 44(3), 683-690.
[] [PMID: 15679403]
Shi, S.; Yao, L.; Li, L.; Wu, Z.; Zha, Z.; Kung, H.F.; Zhu, L.; Fang, D.C. Synthesis of novel technetium-99m tricarbonyl-HBED-CC complexes and structural prediction in solution by density functional theory calculation. R. Soc. Open Sci., 2019, 6(11), 191247.
[] [PMID: 31827858]
Qiu, L.; Lin, J.; Ju, X.; Gong, X.; Luo, S. Structural investigation of technetium-diphosphonate complex 99mTc-MDP. Chin. J. Chem. Phys., 2011, 24(3), 295-304.
Qiu, L.; Lin, J.G.; Gong, X.D.; Cheng, W.; Luo, S.N. Substituent effect on the structure and biological property of 99m Tc-labeled diphosphonates: Theoretical studies. Bull. Korean Chem. Soc., 2012, 33(12), 4084-4092.
Nabati, M.; Sabahnoo, H.; Bodaghi, N.V. Molecular structure determination and stability parameters study of Tc-99m-MDP (Technetium 99m Methylene Diphosphonate) cold kit and analysis of its binding to osteocalcin receptor as a bone scan agent. Chem. Methodol., 2020, 4(3), 297-310.
Nabati, M. Insight into the stability, reactivity, structural and spectral properties of the anti, syn-endo and syn-exo isomers of bis(N-ethoxy-N-ethyl-dithiocarbamato)nitrido technetium-99m Tc-99m-N(NOEt)(2). Radiopharmaceutical. Chem. Methodol., 2018, 2(3), 223-238.
Mancini, D.T.; Souza, E.F.; Caetano, M.S.; Ramalho, T.C. 99Tc NMR as a promising technique for structural investigation of biomolecules: Theoretical studies on the solvent and thermal effects of phenylbenzothiazole complex. Magn. Reson. Chem., 2014, 52(4), 129-137.
[] [PMID: 24446055]
Li, Y.; Ma, L.; Gaddam, V.; Gallazzi, F.; Hennkens, H.M.; Harmata, M.; Lewis, M.R.; Deakyne, C.A.; Jurisson, S.S. Synthesis, characterization, and in vitro evaluation of new 99mTc/Re(V)-cyclized octreotide analogues: An experimental and computational approach. Inorg. Chem., 2016, 55(3), 1124-1133.
[] [PMID: 26789775]
Moura, C.; Fernandes, C.; Gano, L.; Paulo, A.; Santos, I.C.; Santos, I.; Calhorda, M.J. Influence of the ligand donor atoms on the in vitro stability of rhenium(I) and technetium (I)-99m complexes with pyrazole-containing chelators: Experimental and DFT studies. J. Organomet. Chem., 2009, 694(6), 950-958.
Jang, K.S.; Lee, S.S.; Oh, Y.H.; Lee, S.H.; Kim, S.E.; Kim, D.W. Control of reactivity and selectivity of guanidinyliodonium salts toward F-18-labeling by monitoring of protecting groups: Experiment and theory. J. Fluor. Chem., 2019, 227, 109387.
Lee, Y.S. Hodošček, M.; Chun, J.H.; Pike, V.W. Conformational structure and energetics of 2-methylphenyl(2′-methoxyph enyl)iodonium chloride: Evidence for solution clusters. Chemistry, 2010, 16(34), 10418-10423.
[] [PMID: 20632418]
Lee, S.S.; Jang, K.S.; Lee, B.C.; Oh, Y.H.; Park, S.W.; Kim, D.W.; Jang, G.H.; Lee, S. Origin of difference in the reactivity of aliphatic and aromatic guanidine-containing pharmaceuticals toward [18F]fluorination: Coulombic forces and hydrogen bonding. Bull. Korean Chem. Soc., 2019, 40(9), 894-897.
Popkov, A.; Breza, M. Why is monoalkylation versus bis-alkylation of the Ni(II) complex of the Schiff base of (S)-N-(2-benzoylphenyl)-1-benzylpyrrolidine-2-carboxamide and glycine so selective? MP2 modelling and topological QTAIM analysis of chiral metallocomplex synthons of α-amino acids used for the preparation of radiopharmaceuticals for positron emission tomography. J. Radioanal. Nucl. Chem., 2010, 286(3), 829-833.
[] [PMID: 26224905]
Chai, J.Y.; Cha, H.; Lee, S.S.; Oh, Y.H.; Lee, S.; Chi, D.Y. Mechanistic study of nucleophilic fluorination for the synthesis of fluorine-18 labeled fluoroform with high molar activity from N -difluoromethyltriazolium triflate. RSC Advances, 2021, 11(11), 6099-6106.
[] [PMID: 35423150]
Choi, H.; Oh, Y.H. Effects of protecting group and counter&#8208;anion on fluorination, bromination, and intramolecular cyclization of phenethylamine diaryliodonium salts: Quantum chemical analysis. J. Phys. Org. Chem., 2021, 34(5), e4177.
Denk, C.; Svatunek, D.; Filip, T.; Wanek, T.; Lumpi, D. Fröhlich, J.; Kuntner, C.; Mikula, H. Development of a (18) F-labeled tetrazine with favorable pharmacokinetics for bioorthogonal PET imaging. Angew. Chem. Int. Ed., 2014, 53(36), 9655-9659.
[] [PMID: 24989029]
Dialer, L.O.; Selivanova, S.V.; Müller, C.J.; Müller, A.; Stellfeld, T.; Graham, K.; Dinkelborg, L.M. Krämer, S.D.; Schibli, R.; Reiher, M.; Ametamey, S.M. Studies toward the development of new silicon containing building blocks for the direct (18)F-labeling of peptides. J. Med. Chem., 2013, 56(19), 7552-7563.
[] [PMID: 23992105]
Schirrmacher, E. Wängler, B.; Cypryk, M.; Bradtmöller, G.; Schäfer, M.; Eisenhut, M.; Jurkschat, K.; Schirrmacher, R. Synthesis of p-(di-tert-butyl[(18)F]fluorosilyl)benzaldehyde ([(18)F]Si FA-A) with high specific activity by isotopic exchange: A convenient labeling synthon for the (18)F-labeling of N-amino-oxy derivatized peptides. Bioconjug. Chem., 2007, 18(6), 2085-2089.
[] [PMID: 18030993]
Rotstein, B.H.; Wang, L.; Liu, R.Y.; Patteson, J.; Kwan, E.E.; Vasdev, N.; Liang, S.H. Mechanistic studies and radiofluorination of structurally diverse pharmaceuticals with spirocyclic iodonium (III) ylides. Chem. Sci. (Camb.), 2016, 7(7), 4407-4417.
[] [PMID: 27540460]
Roslin, S.; Brandt, P.; Nordeman, P.; Larhed, M.; Odell, L.; Eriksson, J. Synthesis of 11C-labelled ureas by palladium (II)-mediated oxidative carbonylation. Molecules, 2017, 22(10), 1688.
[] [PMID: 28994734]
Moustapha, M.E.; Geesi, M.H.; Farag, Z.R.; Anouar, E.H. Electrophilic aromatic synthesis of radioiodinated aripiprazole: Experimental and DFT investigations. Curr. Org. Synth., 2020, 17(4), 295-303.
[] [PMID: 32271696]
Castle, T.C.; Maurer, R.I.; Sowrey, F.E.; Went, M.J.; Reynolds, C.A.; McInnes, E.J.L.; Blower, P.J. Hypoxia-targeting copper bis(selenosemicarbazone) complexes: Comparison with their sulfur analogues. J. Am. Chem. Soc., 2003, 125(33), 10040-10049.
[] [PMID: 12914467]
Holland, J.P.; Barnard, P.J.; Collison, D.; Dilworth, J.R.; Edge, R.; Green, J.C.; McInnes, E.J.L. Spectroelectrochemical and computational studies on the mechanism of hypoxia selectivity of copper radiopharmaceuticals. Chemistry, 2008, 14(19), 5890-5907.
[] [PMID: 18494010]
Maurer, R.I.; Blower, P.J.; Dilworth, J.R.; Reynolds, C.A.; Zheng, Y.; Mullen, G.E.D. Studies on the mechanism of hypoxic selectivity in copper bis(thiosemicarbazone) radiopharmaceuticals. J. Med. Chem., 2002, 45(7), 1420-1431.
[] [PMID: 11906283]
Betts, H.M.; Pascu, S.I.; Buchard, A.; Bonnitcha, P.D.; Dilworth, J.R. One-pot synthesis, characterisation and kinetic stability of novel side-bridged pentaazamacrocyclic copper (ii) complexes. RSC Advances, 2014, 4(25), 12964-12970.
Bodio, E.; Boujtita, M.; Julienne, K.; Le Saec, P.; Gouin, S.G.; Hamon, J.; Renault, E.; Deniaud, D. Synthesis and characterization of a stable copper (I) complex for radiopharmaceutical applications. ChemPlusChem, 2014, 79(9), 1284-1293.
Guillou, A.; Lima, L.M.P. Esteban-Gómez, D.; Le Poul, N.; Bartholomä, M.D.; Platas, I.C.; Delgado, R.; Patinec, V.; Tripier, R. Methylthiazolyl tacn ligands for copper complexation and their bifunctional chelating agent derivatives for bioconjugation and copper-64 radiolabeling: An example with bombesin. Inorg. Chem., 2019, 58(4), 2669-2685.
[] [PMID: 30689368]
Motekaitis, R.J.; Rogers, B.E.; Reichert, D.E.; Martell, A.E.; Welch, M.J. Stability and structure of activated macrocycles. Ligands with biological applications. Inorg. Chem., 1996, 35(13), 3821-3827.
[] [PMID: 11666570]
Holland, J.P.; Barnard, P.J.; Collison, D.; Dilworth, J.R.; Edge, R.; Green, J.C.; Heslop, J.M.; McInnes, E.J.L.; Salzmann, C.G.; Thompson, A.L. Synthesis, X-ray crystallography, spectroelectrochemistry and computational studies on potential copper-based radiopharmaceuticals. Eur. J. Inorg. Chem., 2008, 2008(22), 3549-3560.
Adeowo, F.Y.; Honarparvar, B.; Skelton, A.A. Density functional theory study on the complexation of NOTA as a bifunctional chelator with radiometal ions. J. Phys. Chem. A, 2017, 121(32), 6054-6062.
[] [PMID: 28737914]
Shuvaev, S.; Suturina, E.A.; Rotile, N.J.; Astashkin, A.; Ziegler, C.J.; Ross, A.W.; Walker, T.L.; Caravan, P.; Taschner, I.S. Revisiting dithiadiaza macrocyclic chelators for copper-64 PET imaging. Dalton Trans., 2020, 49(40), 14088-14098.
[] [PMID: 32970072]
Holland, J.P.; Fisher, V.; Hickin, J.A.; Peach, J.M. Pyrene-functionalised copper complexes as potential dual-modality imaging agents. Eur. J. Inorg. Chem., 2010, 2010(1), 48-58.
Anderson, C.J.; John, C.S.; Li, Y.J.; Hancock, R.D.; Mccarthy, T.J.; Martell, A.E.; Welch, M.J. N,N′-Ethylene-di-l-Cysteine (EC) complexes of Ga(III) and In(III): Molecular modeling, thermodynamic stability and in vivo studies. Nucl. Med. Biol., 1995, 22(2), 165-173.
[] [PMID: 7767309]
Asti, M.; Ferrari, E.; Croci, S.; Atti, G.; Rubagotti, S.; Iori, M.; Capponi, P.C.; Zerbini, A.; Saladini, M.; Versari, A. Synthesis and characterization of (68) Ga-labeled curcumin and curcuminoid complexes as potential radiotracers for imaging of cancer and Alzheimer’s disease. Inorg. Chem., 2014, 53(10), 4922-4933.
[] [PMID: 24766626]
Grieve, M.L.; Davey, P.R.W.J.; Forsyth, C.M.; Paterson, B.M. The synthesis of a bis(thiosemicarbazone) macrocyclic ligand and the Mn(II), Co(II), Zn(II) and 68Ga(III) complexes. Molecules, 2021, 26(12), 3646.
[] [PMID: 34203751]
Gai, Y.; Sun, L.; Lan, X.; Zeng, D.; Xiang, G.; Ma, X. Synthesis and evaluation of new bifunctional chelators with phosphonic acid arms for gallium-68 based PET imaging in melanoma. Bioconjug. Chem., 2018, 29(10), 3483-3494.
[] [PMID: 30205001]
Cundari, T.R.; Moody, E.W.; Sommerer, S.O. Computer aided design of metallopharmaceuticals: A molecular mechanics force field for gadolinium complexes. Inorg. Chem., 1995, 34(24), 5989-5999.
Šimeček, J.; Schulz, M.; Notni, J.; Plutnar, J.; Kubíček, V.; Havlíčková, J.; Hermann, P. Complexation of metal ions with TRAP (1,4,7-triazacyclononane phosphinic acid) ligands and 1,4,7-triazacyclononane-1,4,7-triacetic acid: Phosphinate-containing ligands as unique chelators for trivalent gallium. Inorg. Chem., 2012, 51(1), 577-590.
[] [PMID: 22221285]
Schmidtke, A. Läppchen, T.; Weinmann, C.; Bier, S.L.; Keller, M.; Kiefer, Y.; Holland, J.P.; Bartholomن, M.D. Gallium complexation, stability, and bioconjugation of 1,4,7-triazacyclononane derived chelators with azaheterocyclic arms. Inorg. Chem., 2017, 56(15), 9097-9110.
[] [PMID: 28742337]
Lau, E.Y.; Lightstone, F.C.; Colvin, M.E. Environmental effects on the structure of metal ion-DOTA complexes: An ab initio study of radiopharmaceutical metals. Inorg. Chem., 2006, 45(23), 9225-9232.
[] [PMID: 17083220]
Kostelnik, T.I.; Wang, X.; Southcott, L.; Wagner, H.K.; Kubeil, M.; Stephan, H.; Jaraquemada, P.M.G.; Orvig, C. Rapid thermodynamically stable complex formation of [ nat/111 In]In 3+, [nat/90Y]Y 3+, and [ nat/177Lu]Lu 3+ with H 6 dappa. Inorg. Chem., 2020, 59(10), 7238-7251.
[] [PMID: 32337985]
Price, E.W.; Zeglis, B.M.; Cawthray, J.F.; Lewis, J.S.; Adam, M.J.; Orvig, C. What a difference a carbon makes: H₄octapa vs. H₄C3octapa, ligands for In-111 and Lu-177 radiochemistry. Inorg. Chem., 2014, 53(19), 10412-10431.
[] [PMID: 25192223]
Fuks, L.; Gniazdowska, E.; Mieczkowski, J.; Sadlej, S.N. Structural features of tricarbonyl(N-methyl-2-pyridinecarboxyamide)chlo ro-rhenium(I)-potential precursor of radiopharmaceuticals. Polyhedron, 2008, 27(5), 1353-1360.
Arteaga, M.C.; Pedraza, L.M.; Ferro, F.G.; Murphy, S.E. Chávez, M.L.; Ascencio, J.A.; García, S.L.; Hernández, G.S. Uptake of 188Re-β-naphthyl-peptide in cervical carcinoma tumours in athymic mice. Nucl. Med. Biol., 2001, 28(3), 319-326.
[] [PMID: 11323244]
Fuks, L.; Gniazdowska, E. Koźmiński, P. Tricarbonylrhenium(I) complexes with anionic ligands containing S and O donor atoms - potential radiopharmaceutical precursors. Polyhedron, 2010, 29(1), 634-638.
Fuks, L.; Gniazdowska, E.; Sadlej-Sosnowska, N. Tricarbonyltechnetium(I) and tricarbonylrhenium(I) complexed with N-methyl-2-pyridinecarboxyamide as potential radiopharmaceuticals: A computational study. Struct. Chem., 2010, 21(4), 827-835.
Lipowska, M.; Hansen, L.; Cini, R.; Xu, X.; Choi, H.; Taylor, A.T.; Marzilli, L.G. Synthesis of new N2S2 ligands and Re(V)O(N2S2) analogues of 99mTc renal imaging agents. Characterization by NMR spectroscopy, molecular mechanics calculations, and X-ray crystallography. Inorg. Chim. Acta, 2002, 339, 327-340.
Eychenne, R.; Guizani, S.; Wang, J.H.; Picard, C.; Malek, N.; Fabre, P.L.; Wolff, M.; Machura, B.; Saffon, N.; Lepareur, N.; Benoist, E. Rhenium complexes based on an N2O tridentate click scaffold: From Synthesis, structural and theoretical characterization to a radiolabelling study. Eur. J. Inorg. Chem., 2017, 2017(1), 69-81.
Li, L.; Kuo, H.T.; Wang, X.; Merkens, H.; Colpo, N.; Radchenko, V.; Schaffer, P.; Lin, K.S.; Bénard, F.; Orvig, C. tBu4 octapa-alkyl-NHS for metalloradiopeptide preparation. Dalton Trans., 2020, 49(22), 7605-7619.
[] [PMID: 32459231]
Martin, S. Tönnesmann, R.; Hierlmeier, I.; Maus, S.; Rosar, F.; Ruf, J.; Holland, J.P.; Ezziddin, S.; Bartholomä, M.D. Identification, characterization, and suppression of side products formed during the synthesis of [ 177Lu]Lu-PSMA-617. J. Med. Chem., 2021, 64(8), 4960-4971.
[] [PMID: 33826320]
Chong, H.S.; Chen, Y.W.; Kang, C.S.; Sin, I.; Zhang, S.Y.; Wang, H.X. Pyridine-containing octadentate ligand NE3TA-PY for formation of neutral complex with Lu-177(III) and Y-90(III) for radiopharmaceutical applications: Synthesis, DFT calculation, radiolabeling, and in vitro complex stability. J. Inorg. Biochem., 2021, 221, 111436.
Vaughn, B.A.; Koller, A.J.; Chen, Z.; Ahn, S.H.; Loveless, C.S.; Cingoranelli, S.J.; Yang, Y.; Cirri, A.; Johnson, C.J.; Lapi, S.E.; Chapman, K.W.; Boros, E. Homologous structural, chemical, and biological behavior of Sc and Lu complexes of the picaga bifunctional chelator: Toward development of matched theranostic pairs for radiopharmaceutical applications. Bioconjug. Chem., 2021, 32(7), 1232-1241.
[] [PMID: 33284001]
Li, L.; Guadalupe, J.P.M.; Aluicio, S.E.; Wang, X.; Barnhart, T.E.; Cai, W.; Radchenko, V.; Schaffer, P.; Engle, J.W.; Orvig, C. Coordination chemistry of [Y(pypa)]− and comparison immuno-PET imaging of [ 44 Sc]Sc- and [ 86 Y]Y-pypa-phenyl-TRC105. Dalton Trans., 2020, 49(17), 5547-5562.
[] [PMID: 32270167]
Li, L.; Jaraquemada, P.M.G.; Aluicio, S.E.; Wang, X.; Jiang, D.; Sakheie, M.; Kuo, H.T.; Barnhart, T.E.; Cai, W.; Radchenko, V.; Schaffer, P.; Lin, K.S.; Engle, J.W.; Bénard, F.; Orvig, C. [nat/44 Sc(pypa)]−: Thermodynamic stability, radiolabeling, and biodistribution of a prostate-specific-membrane-antigen-targeting conjugate. Inorg. Chem., 2020, 59(3), 1985-1995.
[] [PMID: 31976659]
Price, E.W.; Cawthray, J.F.; Adam, M.J.; Orvig, C. Modular syntheses of H 4 octapa and H2 dedpa, and yttrium coordination chemistry relevant to 86Y/90Y radiopharmaceuticals. Dalton Trans., 2014, 43(19), 7176-7190.
[] [PMID: 24676528]
Gogoi, S.; Saikia, M.D. Adsorptive interaction of 90Y and 90Sr with diglycolamide based resin: A density functional theory. J. Radioanal. Nucl. Chem., 2017, 311(1), 663-671.
Frimpong, E.; Skelton, A.A.; Honarparvar, B. DFT study of the interaction between DOTA chelator and competitive alkali metal ions. J. Mol. Graph. Model., 2017, 76, 70-76.
[] [PMID: 28711759]
Summers, K.L.; Sarbisheh, E.K.; Zimmerling, A.; Cotelesage, J.J.H.; Pickering, I.J.; George, G.N.; Price, E.W. Structural characterization of the solution chemistry of zirconium(IV) desferrioxamine: A coordination sphere completed by hydroxides. Inorg. Chem., 2020, 59(23), 17443-17452.
[] [PMID: 33183002]
Guérard, F.; Beyler, M.; Lee, Y.S.; Tripier, R.; Gestin, J.F.; Brechbiel, M.W. Investigation of the complexation of nat Zr(IV) and 89 Zr(IV) by hydroxypyridinones for the development of chelators for PET imaging applications. Dalton Trans., 2017, 46(14), 4749-4758.
[] [PMID: 28338136]
Adams, C.J.; Wilson, J.J.; Boros, E. Multifunctional desferrichrome analogues as versatile 89Zr(IV) chelators for immunoPET probe development. Mol. Pharm., 2017, 14(8), 2831-2842.
[] [PMID: 28665620]
Alnahwi, A.H.; Ait-Mohand, S.; Dumulon, P.V.; Dory, Y.L.; Guérin, B. Promising performance of 4HMS, a new zirconium-89 octadendate chelator. ACS Omega, 2020, 5(19), 10731-10739.
[] [PMID: 32455192]
Holland, J.P.; Divilov, V.; Bander, N.H.; Smith, J.P.M.; Larson, S.M.; Lewis, J.S. 89Zr-DFO-J591 for immunoPET of prostate-specific membrane antigen expression in vivo. J. Nucl. Med., 2010, 51(8), 1293-1300.
[] [PMID: 20660376]
Gao, Y.; Grover, P.; Schreckenbach, G. Stabilization of hydrated Ac III cation: The role of superatom states in actinium-water bonding. Chem. Sci. (Camb.), 2021, 12(7), 2655-2666.
[] [PMID: 34164034]
Stein, B.W.; Morgenstern, A.; Batista, E.R.; Birnbaum, E.R.; Bone, S.E.; Cary, S.K.; Ferrier, M.G.; John, K.D.; Pacheco, J.L.; Kozimor, S.A.; Mocko, V.; Scott, B.L.; Yang, P. Advancing chelation chemistry for actinium and other +3 f-elements, Am, Cm, and La. J. Am. Chem. Soc., 2019, 141(49), 19404-19414.
[] [PMID: 31794205]
Morgenstern, A.; Lilley, L.M.; Stein, B.W.; Kozimor, S.A.; Batista, E.R.; Yang, P. Computer assisted design of macrocyclic chelators for Actinium-225 radiotherapeutics. Inorg. Chem., 2021, 60(2), 623-632.
[] [PMID: 33213142]
Al-Hokbany, N.S.; Mahfouz, R.M. Preparation and characterization of new Sm(III) complexes with some bidentate ligands contains (N,N), (S,N), (O,O) and (N,O) donor atoms. J. Saudi Chem. Soc., 2010, 14(4), 391-398.
Neves, M.; Gano, L.; Pereira, N.; Costa, M.C.; Costa, M.R.; Chandia, M.; Rosado, M.; Fausto, R. Synthesis, characterization and biodistribution of bisphosphonates Sm-153 complexes: Correlation with molecular modeling interaction studies. Nucl. Med. Biol., 2002, 29(3), 329-338.
[] [PMID: 11929703]
Edwards, A.C.; Wagner, C.; Geist, A.; Burton, N.A.; Sharrad, C.A.; Adams, R.W.; Pritchard, R.G.; Panak, P.J.; Whitehead, R.C.; Harwood, L.M. Exploring electronic effects on the partitioning of actinides(III) from lanthanides(III) using functionalised bis-triazinyl phenanthroline ligands. Dalton Trans., 2016, 45(45), 18102-18112.
[] [PMID: 27488559]
Yang, Y.; Pushie, M.J.; Cooper, D.M.L.; Doschak, M.R. Structural characterization of Sm III (EDTMP). Mol. Pharm., 2015, 12(11), 4108-4114.
[] [PMID: 26437889]
Tosato, M.; Asti, M.; Dalla Tiezza, M.; Orian, L. Häussinger, D.; Vogel, R.; Köster, U.; Jensen, M.; Andrighetto, A.; Pastore, P.; Marco, V.D. Highly stable silver(I) complexes with cyclen-based ligands bearing sulfide arms: A step toward silver-111 labeled radiopharmaceuticals. Inorg. Chem., 2020, 59(15), 10907-10919.
[] [PMID: 32658468]
Teze, D.; Sergentu, D.C.; Kalichuk, V.; Barbet, J.; Deniaud, D.; Galland, N.; Maurice, R.; Montavon, G. Targeted radionuclide therapy with astatine-211: Oxidative dehalogenation of astatobenzoate conjugates. Sci. Rep., 2017, 7(1), 2579.
[] [PMID: 28566709]
Rosso, L.; Gee, A.D.; Gould, I.R. Ab initio computational study of positron emission tomography ligands interacting with lipid molecule for the prediction of nonspecific binding. J. Comput. Chem., 2008, 29(14), 2397-2405.
[] [PMID: 18442082]
Abdolmaleki, A.; Shiri, F.; Ghasemi, J.B. Use of molecular docking as a decision-making tool in drug discovery. In: Coumar, M.S.; Ed. Molecular Docking for Computer-Aided Drug Design., Elsevier: Amsterdam, Netherlands, 2021; pp. 229-243.
Fu, D.Y.; Meiler, J. Predictive power of different types of experimental restraints in small molecule docking: A review. J. Chem. Inf. Model., 2018, 58(2), 225-233.
[] [PMID: 29286651]
Forli, S.; Huey, R.; Pique, M.E.; Sanner, M.F.; Goodsell, D.S.; Olson, A.J. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc., 2016, 11(5), 905-919.
[] [PMID: 27077332]
Li, J.; Fu, A.; Zhang, L. An overview of scoring functions used for protein-ligand interactions in molecular docking. Interdiscip. Sci., 2019, 11(2), 320-328.
[] [PMID: 30877639]
Kitchen, D.B.; Decornez, H.; Furr, J.R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat. Rev. Drug Discov., 2004, 3(11), 935-949.
[] [PMID: 15520816]
Liu, J.; Wang, R. Classification of current scoring functions. J. Chem. Inf. Model., 2015, 55(3), 475-482.
[] [PMID: 25647463]
Wَjcikowski, M.; Ballester, P.J.; Siedlecki, P. Performance of machine learning scoring functions in structure based virtual screening. Sci. Rep., 2017, 7(1), 46710.
[] [PMID: 28127051]
Winkler, D.A. Ligand entropy is hard but should not be ignored. J. Chem. Inf. Model., 2020, 60(10), 4421-4423.
[] [PMID: 33100015]
Guterres, H.; Im, W. Improving protein ligand docking results with high-throughput molecular dynamics simulations. J. Chem. Inf. Model., 2020, 60(4), 2189-2198.
[] [PMID: 32227880]
Meng, X.Y.; Zhang, H.X.; Mezei, M.; Cui, M. Molecular docking: A powerful approach for structure based drug discovery. Curr. Computeraided Drug Des., 2011, 7(2), 146-157.
[] [PMID: 21534921]
Brooijmans, N.; Kuntz, I.D. Molecular recognition and docking algorithms. Annu. Rev. Biophys. Biomol. Struct., 2003, 32(1), 335-373.
[] [PMID: 12574069]
Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30(16), 2785-2791.
[] [PMID: 19399780]
Abagyan, R.; Totrov, M.; Kuznetsov, D. ICM? A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. J. Comput. Chem., 1994, 15(5), 488-506.
Sandak, B.; Wolfson, H.J.; Nussinov, R. Flexible docking allowing induced fit in proteins: Insights from an open to closed conformational isomers. Proteins, 1998, 32(2), 159-174.
[<159:AID-PROT3>3.0.CO;2-G] [PMID: 9714156]
Zhao, H.; Caflisch, A. Molecular dynamics in drug design. Eur. J. Med. Chem., 2015, 91, 4-14.
[] [PMID: 25108504]
Huang, S.Y.; Zou, X. Advances and challenges in protein ligand docking. Int. J. Mol. Sci., 2010, 11(8), 3016-3034.
[] [PMID: 21152288]
Lavecchia, A.; Giovanni, C. Virtual screening strategies in drug discovery: A critical review. Curr. Med. Chem., 2013, 20(23), 2839-2860.
[] [PMID: 23651302]
Kontoyianni, M. Docking and virtual screening in drug discovery. Methods Mol. Biol., 2017, 1647, 255-266.
Abdolmaleki, A.; Shiri, F.; Ghasemi, J.B. Computational multi-target drug design. In: Multi-Target Drug Design Using Chem-Bioinformatic Approaches; Roy, K., Ed.; Humana Press: New York, NY, 2018; pp. 51-90.
Piplani, S.; Singh, P.K.; Winkler, D.A.; Petrovsky, N. Computationally repurposed drugs and natural products against RNA dependent RNA polymerase as potential COVID-19 therapies. Molecular Biomedicine, 2021, 2(1), 28.
[] [PMID: 34766004]
Chen, Y.Z.; Ung, C.Y. Prediction of potential toxicity and side effect protein targets of a small molecule by a ligand-protein inverse docking approach. J. Mol. Graph. Model., 2001, 20(3), 199-218.
[] [PMID: 11766046]
Kharkar, P.S.; Warrier, S.; Gaud, R.S. Reverse docking: A powerful tool for drug repositioning and drug rescue. Future Med. Chem., 2014, 6(3), 333-342.
[] [PMID: 24575968]
Lee, A.; Kim, D. CRDS: Consensus reverse docking system for target fishing. Bioinformatics, 2020, 36(3), 959-960.
[PMID: 31432077]
Nettles, J.H.; Jenkins, J.L.; Bender, A.; Deng, Z.; Davies, J.W.; Glick, M. Bridging chemical and biological space: “Target fishing” using 2D and 3D molecular descriptors. J. Med. Chem., 2006, 49(23), 6802-6810.
[] [PMID: 17154510]
Aubé, J. Drug repurposing and the medicinal chemist. ACS Med. Chem. Lett., 2012, 3(6), 442-444.
[] [PMID: 24900492]
Sciortino, G. Rodríguez, P.G.J.; Lledós, A.; Garribba, E.; Maréchal, J.D. Prediction of the interaction of metallic moieties with proteins: An update for protein-ligand docking techniques. J. Comput. Chem., 2018, 39(1), 42-51.
[] [PMID: 29076256]
Chen, D.; Menche, G.; Power, T.D.; Sower, L.; Peterson, J.W.; Schein, C.H. Accounting for ligand-bound metal ions in docking small molecules on adenylyl cyclase toxins. Proteins, 2007, 67(3), 593-605.
[] [PMID: 17311351]
Fine, J.; Konc, J.; Samudrala, R.; Chopra, G. CANDOCK: Chemical atomic network-based hierarchical flexible docking algorithm using generalized statistical potentials. J. Chem. Inf. Model., 2020, 60(3), 1509-1527.
[] [PMID: 32069042]
Chen, K.; Al Aowad, A.F.; Adelstein, S.J.; Kassis, A.I. Molecular-docking-guided design, synthesis, and biologic evaluation of radioiodinated quinazolinone prodrugs. J. Med. Chem., 2007, 50(4), 663-673.
[] [PMID: 17256924]
Chemlal, L.; Akachar, J.; Makram, S.; Zoubir, B.; Cherrah, Y.; Eljaoudi, R.; Ibrahimi, A.; Faouzi, M.A. The displacement study of 99m Tc-DTPA-Human serum albumin binding in presence of furosemide and metformin by using equilibrium dialysis and molecular docking. IUBMB Life, 2019, 71(12), 2003-2009.
[] [PMID: 31633875]
Mori, D.; Kimura, H.; Kawashima, H.; Yagi, Y.; Arimitsu, K.; Ono, M.; Saji, H. Development of 99mTc radiolabeled A85380 derivatives targeting cerebral nicotinic acetylcholine receptor: Novel radiopharmaceutical ligand 99mTc-A-YN-IDA-C4. Bioorg. Med. Chem., 2019, 27(18), 4200-4210.
[] [PMID: 31401009]
Chadha, N.; Sinha, D.; Tiwari, A.K.; Chuttani, K.; Mishra, A.K. Synthesis, biological evaluation and molecular docking studies of high-affinity bone targeting N,N(') -bis (alendronate) diethylenetriamene-N,N′-triacetic acid: A bifunctional bone scintigraphy agent. Chem. Biol. Drug Des., 2013, 82(4), 468-476.
[] [PMID: 23906022]
Erfani, M.; Malek, H.; Sadat Ebrahimi, S.E.; Hassanzadeh, L. New 99mTc(CO)3 -radiolabeled arylpiperazine pharmacophore as potent 5HT1A serotonin receptor radiotracer: Docking studies, chemical synthesis, radiolabeling, and biological evaluation. J. Labelled Comp. Radiopharm., 2019, 62(4), 166-177.
[] [PMID: 30663099]
Gniazdowska, E. Koźmiński, P.; Halik, P.; Bajda, M.; Czarnecka, K.; Mikiciuk-Olasik, E.; Mas&#322;owska, K.; Rogulski, Z.; Cheda, Ł,; Kilian, K.; Szymański, P. Synthesis, physicochemical and biological evaluation of tacrine derivative labeled with technetium-99m and gallium-68 as a prospective diagnostic tool for early diagnosis of Alzheimer’s disease. Bioorg. Chem., 2019, 91, 103136.
[] [PMID: 31374521]
Hosseini Balef, S.S.; Piramoon, M.; Hosseinimehr, S.J.; Irannejad, H. In vitro and in silico evaluation of P-glycoprotein inhibition through 99mTc-methoxyisobutylisonitrile uptake. Chem. Biol. Drug Des., 2019, 93(3), 283-289.
[] [PMID: 30270513]
Khedr, M.A.; Rashed, H.M.; Farag, H.; Sakr, T.M. Rational design of some substituted phenyl azanediyl (bis) methylene phosphonic acid derivatives as potential anticancer agents and imaging probes: Computational inputs, chemical synthesis, radiolabeling, biodistribution and gamma scintigraphy. Bioorg. Chem., 2019, 92, 103282.
[] [PMID: 31541801]
Kurniawan, F.; Kartasasmita, R.E.; Yoshioka, N.; Mutalib, A.; Tjahjono, D.H. Computational study of imidazolylporphyrin derivatives as a radiopharmaceutical ligand for melanoma. Curr. Computeraided Drug Des., 2018, 14(3), 191-199.
[] [PMID: 29663897]
Motaleb, M.A.; Ibrahim, I.T.; Sarhan, M.O.; Zaghary, W.A. Radioiodination and biological distribution of a new s-triazine derivative for tumor uptake evaluation. J. Labelled Comp. Radiopharm., 2018, 61(14), 1058-1068.
[] [PMID: 30193401]
Şahin, A.; Şentürk, M.; Salmas, R.E.; Durdagi, S.; Ayan, A.; Karagölge, A. Investigation of inhibition of human glucose 6-phosphate dehydrogenase by some 99mTc chelators by in silico and in vitro methods. J Enz. Inhib. Med. Chem., 2016, 31(1), 141-147.
Singh, P.; Kumar, V.; Aggarwal, S.; Tiwari, A.K.; Chuttani, K.; Pratap, R.; Mishra, A.K. Design, synthesis, and biological evaluation of catecholamine vehicle for studying dopaminergic system. Chem. Biol. Drug Des., 2013, 82(2), 226-232.
[] [PMID: 23601203]
Tiwari, A.K.; Rathore, V.S.; Sinha, D.; Datta, A.; Sehgal, N.; Chuttani, K. Synthesis, radiolabelling and initial biological characterisation of F-18-labelled xanthine derivatives for PET imaging of Eph receptors. Org. Biomol. Chem., 2012, 18(16), 3104-3116.
Chaturvedi, S.; Kaul, A.; Yadav, N.; Singh, B.; Mishra, A.K. Synthesis, docking and preliminary in vivo evaluation of serotonin dithiocarbamate as novel SPECT neuroimaging agent. MedChemComm, 2013, 4(6), 1006-1014.
Al-wabli, R.I.; Khedr, M.A.; Kadi, A.A.; Motaleb, M.A.; Al-rashood, K.A.; Zaghary, W.A. Synthesis, molecular docking and antibacterial evaluation of various quinoline schiff bases: Labeling and biodistribution of 99mTc-2-(p-hydroxybenzylidene)-1-(quinolin-4-yl) hydrazine. Med. Chem. Res., 2014, 23(9), 4011-4020.
Aboumanei, M.H.; Abdelbary, A.A.; Ibrahim, I.T.; Tadros, M.I.; El-Kolaly, M.T. Improved targeting and tumor retention of a newly synthesized antineoplaston A10 derivative by intratumoral administration: Molecular docking, technetium 99m radiolabeling, and in vivo biodistribution studies. Cancer Biother. Radiopharm., 2018, 33(6), 221-232.
[] [PMID: 29894210]
Srivastava, P.; Kakkar, D.; Kumar, P.; Tiwari, A.K. Modified benzoxazolone (ABO-AA) based Single Photon Emission Computed Tomography (SPECT) probes for 18 kDa translocator protein. Drug Dev. Res., 2019, 80(6), 741-749.
[] [PMID: 31184784]
Kaul, A.; Tiwari, A.K.; Varshney, R.; Mishra, A.K. Synthesis, in silico screening and preclinical evaluation studies of a hexapeptide analogue for its antimicrobial efficacy. RSC Advances, 2015, 5(118), 97180-97186.
Motaleb, M.A.; El-Safoury, D.M.; Abd-Alla, W.H.; Awad, G.A.S.; Sakr, T.M. Radiosynthesis, molecular modeling studies and biological evaluation of 99m Tc-Ifosfamide complex as a novel probe for solid tumor imaging. Int. J. Radiat. Biol., 2018, 94(12), 1134-1141.
[] [PMID: 30373490]
Ávila, S.M.; Ferro, F.G.; Jiménez, M.N.; Ocampo, G.B.; Bravo, V.G.; Luna, G.M.; Santos, C.C.; Azorín, V.E.; Aranda, L.L.; Isaac, O.K.; Melendez, A.L. Synthesis and preclinical evaluation of the 99mTc-/177Lu-CXCR4-L theranostic pair for in vivo chemokine-4 receptor-specific targeting. J. Radioanal. Nucl. Chem., 2020, 324(1), 21-32.
El-kawy, O.A.; Abdel, R.A.S.; Sayed, M.S. Radiolabeling, biological evaluation and molecular docking of delafloxacin: A novel methicillin-resistant Staphylococcus aureus infection radiotracer. J. Radioanal. Nucl. Chem., 2016, 308(3), 1081-1088.
El-Kawy, O.A. García-Horsman, J.A.; Tuominen, R.K. Labelling, molecular modelling and biological evaluation of vardenafil: A potential agent for diagnostic evaluation of erectile dysfunction. Appl. Radiat. Isot., 2016, 118, 258-265.
[] [PMID: 27693738]
El-Kawy, O.A.; Sanad, M.H.; Marzook, F. 99mTc-Mesalamine as potential agent for diagnosis and monitoring of ulcerative colitis: Labelling, characterisation and biological evaluation. J. Radioanal. Nucl. Chem., 2016, 308(1), 279-286.
Essa, B.M.; Sakr, T.M.; Khedr, M.A.; El-Essawy, F.A.; El-Mohty, A.A. 99mTc-amitrole as a novel selective imaging probe for solid tumor: In silico and preclinical pharmacological study. Eur. J. Pharm. Sci., 2015, 76, 102-109.
[] [PMID: 25956074]
Garnuszek, P.; Karczmarczyk, U.; Maurin, M.; Sikora, A.; Zaborniak, J.; Pijarowska-Kruszyna, J. Jaroń A.; Wyczółkowska, M.; Wojdowska, W.; Pawlak, D.; Lipiński, P.F.J.; Mikołajczak, R. PSMA-D4 radioligand for targeted therapy of prostate cancer: Synthesis, characteristics and preliminary assessment of biological properties. Int. J. Mol. Sci., 2021, 22(5), 2731.
[] [PMID: 33800517]
Khurana, H.; Meena, V.K.; Prakash, S.; Chuttani, K.; Chadha, N.; Jaswal, A.; Dhawan, D.K.; Mishra, A.K.; Hazari, P.P. Preclinical evaluation of a potential GSH ester based PET/SPECT imaging probe DT(GSHMe)2 to detect gamma glutamyl transferase over expressing tumors. PLoS One, 2015, 10(7), e0134281.
[] [PMID: 26221728]
Pereira, E.; do Quental, L.; Palma, E.; Oliveira, M.C.; Mendes, F.; Raposinho, P.; Correia, I.; Lavrado, J.; Di Maria, S.; Belchior, A.; Vaz, P.; Santos, I.; Paulo, A. Evaluation of acridine orange derivatives as DNA-targeted radiopharmaceuticals for auger therapy: Influence of the radionuclide and distance to DNA. Sci. Rep., 2017, 7(1), 42544.
Sakr, T.; Khedr, M.; Rashed, H.; Mohamed, M. In silico-based repositioning of phosphinothricin as a novel technetium-99m imaging probe with potential anti-cancer activity. Molecules, 2018, 23(2), 496.
[] [PMID: 29473879]
Sanad, M.H.; Sakr, T.M.; Abdel-Hamid, W.H.A.; Marzook, E.A. In silico study and biological evaluation of 99mTc-tricabonyl oxiracetam as a selective imaging probe for AMPA receptors. J. Radioanal. Nucl. Chem., 2017, 314(3), 1505-1515.
Shukla, J.; Arora, G.; Kotwal, P.P.; Kumar, R.; Malhotra, A.; Bandopadhyaya, G.P. Radiolabeled oligosaccharides nanoprobes for infection imaging. Hell. J. Nucl. Med., 2010, 13(3), 218-223.
[PMID: 21193873]
Srivastava, P.; Kaul, A.; Ojha, H.; Kumar, P.; Tiwari, A.K. Design, synthesis and biological evaluation of methyl-2-(2-(5-bromo benzoxazolone)acetamido)-3-(1H-indol-3-yl)propanoate: TSPO ligand for SPECT. RSC Advances, 2016, 6(115), 114491-114499.
Talaat, H.M.; Ibrahim, I.T.; Bayomy, N.A.; Farouk, N. Synthesis of 99mTc-radiolabeled uridine as a potential tumor imaging agent. Radiochemistry, 2018, 60(1), 51-57.
Yang, Y.; Zhu, L.; Chen, X.; Zhang, H. Binding research on flavones as ligands of β-amyloid aggregates by fluorescence and their 3D-QSAR, docking studies. J. Mol. Graph. Model., 2010, 29(4), 538-545.
[] [PMID: 21094069]
Kumar, D.; Lisok, A.; Dahmane, E.; McCoy, M.; Shelake, S.; Chatterjee, S.; Allaj, V.; Sysa, S.P.; Wharram, B.; Lesniak, W.G.; Tully, E.; Gabrielson, E.; Jaffee, E.M.; Poirier, J.T.; Rudin, C.M.; Gobburu, J.V.S.; Pomper, M.G.; Nimmagadda, S. Peptide-based PET quantifies target engagement of PD-L1 therapeutics. J. Clin. Invest., 2019, 129(2), 616-630.
[] [PMID: 30457978]
Cai, Z.; Ouyang, Q.; Zeng, D.; Nguyen, K.N.; Modi, J.; Wang, L.; White, A.G.; Rogers, B.E.; Xie, X.Q.; Anderson, C.J. 64Cu-labeled somatostatin analogues conjugated with cross bridged phosphonate-based chelators via strain-promoted click chemistry for PET imaging: In silico through in vivo studies. J. Med. Chem., 2014, 57(14), 6019-6029.
[] [PMID: 24983404]
Chatterjee, S.; Lesniak, W.G.; Miller, M.S.; Lisok, A.; Sikorska, E.; Wharram, B.; Kumar, D.; Gabrielson, M.; Pomper, M.G.; Gabelli, S.B.; Nimmagadda, S. Rapid PD-L1 detection in tumors with PET using a highly specific peptide. Biochem. Biophys. Res. Commun., 2017, 483(1), 258-263.
[] [PMID: 28025143]
Cheng, C.; Pan, L.; Dimitrakopoulou, S.A.; Strauss, L.G. A new approach for the development of tracers: Data base screening and in silico modeling for the identification of new ligands for SSTR2. Hell. J. Nucl. Med., 2008, 11(2), 101-104.
[PMID: 18815664]
Lipiński, P.F.J.; Garnuszek, P.; Maurin, M.; Stoll, R.; Metzler, N.N.; Wodyński, A.; Dobrowolski, J.C.; Dudek, M.K.; Orzełowska, M.; Mikołajczak, R. Structural studies on radiopharmaceutical DOTA-minigastrin analogue (CP04) complexes and their interaction with CCK2 receptor. EJNMMI Res., 2018, 8(1), 33.
[] [PMID: 29663167]
El-Kawy, O.A.; Talaat, H.M. Preparation, characterization and evaluation of 186 Re-idarubicin: A novel agent for diagnosis and treatment of hepatocellular carcinoma. J. Labelled Comp. Radiopharm., 2016, 59(2), 72-77.
[] [PMID: 26725469]
Wolohan, P.; Reichert, D.E. CoMSIA and docking study of rhenium based estrogen receptor ligand analogs. Steroids, 2007, 72(3), 247-260.
[] [PMID: 17280694]
Pedersen, K.S.; Baun, C.; Nielsen, K.M.; Thisgaard, H.; Jensen, A.I.; Zhuravlev, F. Design, synthesis, computational, and preclinical evaluation of Ti-nat/Ti-45-labeled urea-based glutamate PSMA ligand. Molecules, 2020, 25(5), 1104.
Behnammanesh, H.; Jokar, S.; Erfani, M.; Geramifar, P.; Sabzevari, O.; Amini, M.; Mazidi, S.M.; Hajiramezanali, M.; Beiki, D. Design, preparation and biological evaluation of a 177Lu-labeled somatostatin receptor antagonist for targeted therapy of neuroendocrine tumors. Bioorg. Chem., 2020, 94, 103381.
[] [PMID: 31662215]
Bernard-Gauthier, V.; Aliaga, A.; Aliaga, A.; Boudjemeline, M.; Hopewell, R.; Kostikov, A.; Rosa, N.P.; Thiel, A.; Schirrmacher, R. Syntheses and evaluation of carbon-11- and fluorine-18-radiolabeled pan-tropomyosin receptor kinase (Trk) inhibitors: Exploration of the 4-aza-2-oxindole scaffold as Trk PET imaging agents. ACS Chem. Neurosci., 2015, 6(2), 260-276.
[] [PMID: 25350780]
Gelovani, J.G. Molecular imaging of epidermal growth factor receptor expression-activity at the kinase level in tumors with positron emission tomography. Cancer Metastasis Rev., 2008, 27(4), 645-653.
[] [PMID: 18626573]
Sun, D.; Bhanu Prasad, B.A.; Schuber, P.T., Jr; Peng, Z.; Maxwell, D.S.; Martin, D.V.; Guo, L.; Han, D.; Kurihara, H.; Yang, D.J.; Gelovani, J.G.; Powis, G.; Bornmann, W.G. Improved synthesis of 17β-hydroxy-16α-iodo-wortmannin, 17β-hydroxy-16α-iodoPX866, and the [131I] analogue as useful PET tracers for PI3-kinase. Bioorg. Med. Chem., 2013, 21(17), 5182-5187.
[] [PMID: 23859776]
Abdelaziz, G.; Shamsel, D.H.A.; Sarhan, M.O.; Gizawy, M.A. Tau protein targeting via radioiodinated azure A for brain theranostics: Radiolabeling, molecular docking, in vitro and in vivo biological evaluation. J. Labelled Comp. Radiopharm., 2020, 63(1), 33-42.
[] [PMID: 31785209]
Khater, S.I.; El-Sharawy, D.M.; El Refaye, M.S.; Farrag, N.S. Optimization and tissue distribution of [125I]iododomperidone as a radiotracer for D2-receptor imaging. J. Radioanal. Nucl. Chem., 2020, 325(2), 343-355.
Liu, Y.; Yu, H.; Zhao, L.; Zhang, H. Design and synthesis of new agents for neuronal nicotinic acetylcholine receptor (nAChRs) imaging. Nucl. Med. Biol., 2013, 40(1), 126-134.
[] [PMID: 23102538]
Yang, Y.; Cui, M.; Zhang, X.; Dai, J.; Zhang, Z.; Lin, C.; Guo, Y.; Liu, B. Radioiodinated benzyloxybenzene derivatives: A class of flexible ligands target to β-amyloid plaques in Alzheimer’s brains. J. Med. Chem., 2014, 57(14), 6030-6042.
[] [PMID: 24936678]
Mukherjee, J.; Liang, C.; Patel, K.K.; Lam, P.Q.; Mondal, R. Development and evaluation of [ 125 I]IPPI for Tau imaging in postmortem human Alzheimer’s disease brain. Synapse, 2021, 75(1), e22183.
[] [PMID: 32722889]
Chen, K.; Adelstein, S.J.; Kassis, A.I. Molecular modeling of the interaction of iodinated Hoechst analogs with DNA: Implications for new radiopharmaceutical design. J. Mol. Struct. Theochem., 2004, 711(1-3), 49-56.
Ibrahim, A.B.; Alaraby Salem, M.; Fasih, T.W.; Brown, A.; Sakr, T.M. Radioiodinated doxorubicin as a new tumor imaging model: Preparation, biological evaluation, docking and molecular dynamics. J. Radioanal. Nucl. Chem., 2018, 317(3), 1243-1252.
Carpenter, R.D.; Natarajan, A.; Lau, E.Y.; Andrei, M.; Solano, D.M.; Lightstone, F.C.; DeNardo, S.J.; Lam, K.S.; Kurth, M.J. Halogenated benzimidazole carboxamides target integrin alpha4beta1 on T-cell and B-cell lymphomas. Cancer Res., 2010, 70(13), 5448-5456.
[] [PMID: 20530664]
Sakr, T.M.; Ibrahim, I.T.; Abd-Alla, W.H. Molecular modeling and preclinical evaluation of radioiodinated tenoxicam for inflammatory disease diagnosis. J. Radioanal. Nucl. Chem., 2018, 316(1), 233-246.
Zhekova, H.R.; Sakuma, T.; Johnson, R.; Concilio, S.C.; Lech, P.J.; Zdravkovic, I.; Damergi, M.; Suksanpaisan, L.; Peng, K.W.; Russell, S.J.; Noskov, S. Mapping of ion and substrate binding sites in human sodium iodide symporter (hNIS). J. Chem. Inf. Model., 2020, 60(3), 1652-1665.
[] [PMID: 32134653]
Hanson, R.N.; Tongcharoensirikul, P.; Barnsley, K.; Ondrechen, M.J.; Hughes, A.; DeSombre, E.R. Synthesis and evaluation of 2-halogenated-1,1-bis(4-hydroxyphenyl)-2-(3-hydroxyphenyl)-ethylenes as potential estrogen receptor-targeted radiodiagnostic and radiotherapeutic agents. Steroids, 2015, 96, 50-62.
[] [PMID: 25637676]
Dubost, E.; Dumas, N.; Fossey, C.; Magnelli, R.; Butt, G.S.; Ballandonne, C.; Caignard, D.H.; Dulin, F.; Sopkova, O.S.J.; Millet, P.; Charnay, Y.; Rault, S.; Cailly, T.; Fabis, F. Synthesis and structure-affinity relationships of selective high-affinity 5-HT(4) receptor antagonists: Application to the design of new potential single photon emission computed tomography tracers. J. Med. Chem., 2012, 55(22), 9693-9707.
[] [PMID: 23102207]
Gao, H.; Wang, S.; Qi, Y.; He, G.; Qiang, B.; Wang, S.; Zhang, H. Synthesis and biological evaluation of 9-fluorenone derivatives for SPECT imaging of α7-nicotinic acetylcholine receptor. Bioorg. Med. Chem. Lett., 2019, 29(23), 126724.
[] [PMID: 31624040]
Amor-Coarasa, A.; Kelly, J.M.; Singh, P.K.; Ponnala, S.; Nikolopoulou, A.; Williams, C., Jr; Vedvyas, Y.; Jin, M.M.; Warren, J.D.; Babich, J.W. [18F]fluoroethyltriazolyl monocyclam derivatives as imaging probes for the chemokine receptor CXCR4. Molecules, 2019, 24(8), 1612.
[] [PMID: 31022852]
Balamurugan, K.; Murugan, N.A. Långström, B.; Nordberg, A.; Ågren, H. Effect of alzheimer familial chromosomal mutations on the amyloid fibril interaction with different PET tracers: Insight from molecular modeling studies. ACS Chem. Neurosci., 2017, 8(12), 2655-2666.
[] [PMID: 28898051]
Cary, B.P.; Brooks, A.F.; Fawaz, M.V.; Drake, L.R.; Desmond, T.J.; Sherman, P.; Quesada, C.A.; Scott, P.J.H. Synthesis and evaluation of [ 18 F]RAGER: A first generation small-molecule PET radioligand targeting the receptor for advanced glycation endproducts. ACS Chem. Neurosci., 2016, 7(3), 391-398.
[] [PMID: 26771209]
Goud, N.S.; Kanth Makani, V.K.; Pranay, J.; Alvala, R.; Qureshi, I.A.; Kumar, P.; Bharath, R.D.; Nagaraj, C.; Yerramsetty, S.; Pal, B.M.; Alvala, M. Synthesis, 18F-radiolabeling and apoptosis inducing studies of novel 4, 7-disubstituted coumarins. Bioorg. Chem., 2020, 97, 103663.
[] [PMID: 32106038]
Pretze, M.; Neuber, C.; Kinski, E.; Belter, B. Köckerling, M.; Caflisch, A.; Steinbach, J.; Pietzsch, J.; Mamat, C. Synthesis, radiolabelling and initial biological characterisation of 18 F-labelled xanthine derivatives for PET imaging of Eph receptors. Org. Biomol. Chem., 2020, 18(16), 3104-3116.
[] [PMID: 32253415]
Lindemann, M.; Hinz, S.; Deuther, C.W.; Namasivayam, V.; Dukic-Stefanovic, S.; Teodoro, R.; Toussaint, M.; Kranz, M.; Juhl, C.; Steinbach, J.; Brust, P.; Müller, C.E.; Wenzel, B. Radiosynthesis and in vivo evaluation of a fluorine-18 labeled pyrazine based radioligand for PET imaging of the adenosine A2B receptor. Bioorg. Med. Chem., 2018, 26(16), 4650-4663.
[] [PMID: 30104122]
Qi, Y.; Li, Y.; Fang, Y.; Gao, H.; Qiang, B.; Wang, S.; Zhang, H. Design, synthesis, biological evaluation, and molecular docking of 2,4-diaminopyrimidine derivatives targeting focal adhesion kinase as tumor radiotracers. Mol. Pharm., 2021, 18(4), 1634-1642.
[] [PMID: 33739836]
Najjar, A.M.; Nishii, R.; Maxwell, D.S.; Volgin, A.; Mukhopadhyay, U.; Bornmann, W.G.; Tong, W.; Alauddin, M.; Gelovani, J.G. Molecular-genetic PET imaging using an HSV1-tk mutant reporter gene with enhanced specificity to acycloguanosine nucleoside analogs. J. Nucl. Med., 2009, 50(3), 409-416.
[] [PMID: 19223410]
Fang, Y.; Wang, D.; Xu, X.; Liu, J.; Wu, A.; Li, X.; Xue, Q.; Wang, H.; Wang, H.; Zhang, H. Synthesis, biological evaluation, and Molecular Dynamics (MD) simulation studies of three novel F-18 labeled and Focal Adhesion Kinase (FAK) targeted 5-bromo pyrimidines as radiotracers for tumor. Eur. J. Med. Chem., 2017, 127, 493-508.
[] [PMID: 28109944]
Fang, Y.; Wang, D.; Xu, X.; Dava, G.; Liu, J.; Li, X.; Xue, Q.; Wang, H.; Zhang, J.; Zhang, H. Preparation, in vitro and in vivo evaluation, and Molecular Dynamics (MD) simulation studies of novel F-18 labeled tumor imaging agents targeting Focal Adhesion Kinase (FAK). RSC Advances, 2018, 8(19), 10333-10345.
[] [PMID: 35540451]
Kapp, O.H.; Siemion, J.; Kuo, J.; Johnson, B.A.; Shankaran, V.; Reba, R.C.; Mukherjee, J. Comparison of the interaction of dopamine and high affinity positron emission tomography radiotracer fallypride with the dopamine D-2 receptor: A molecular modeling study. J. Mol. Model., 2001, 7(1-3), 6-18.
Henriksen, G.; Platzer, S.; Marton, J.; Hauser, A.; Berthele, A.; Schwaiger, M.; Marinelli, L.; Lavecchia, A.; Novellino, E.; Wester, H.J. Syntheses, biological evaluation, and molecular modeling of 18F-labeled 4-anilidopiperidines as μ-opioid receptor imaging agents. J. Med. Chem., 2005, 48(24), 7720-7732.
[] [PMID: 16302812]
Mavel, S.; Vercouillie, J.; Garreau, L.; Raguza, T.; Ravna, A.W.; Chalon, S.; Guilloteau, D.; Emond, P. Docking study, synthesis, and in vitro evaluation of fluoro-MADAM derivatives as SERT ligands for PET imaging. Bioorg. Med. Chem., 2008, 16(19), 9050-9055.
[] [PMID: 18793858]
Fantoni, E.R.; Dal Ben, D.; Falzoni, S.; Di Virgilio, F.; Lovestone, S.; Gee, A. Design, synthesis and evaluation in an LPS rodent model of neuroinflammation of a novel 18 F-labelled PET tracer targeting P2X7. Eur. J. Nucl. Med. Mol. Imag Res., 2017, 7(1), 1-12.
Ferreira Schopf, P.; Zanella, I. Nanomarker for early detection of Alzheimer’s disease combining ab initio DFT simulations and molecular docking approach. Biophysica., 2021, 1(2), 76-86.
Hou, J.; Kovacs, M.S.; Dhanvantari, S.; Luyt, L.G. Development of candidates for Positron Emission Tomography (PET) imaging of ghrelin receptor in disease: Design, synthesis, and evaluation of fluorine-bearing quinazolinone derivatives. J. Med. Chem., 2018, 61(3), 1261-1275.
[] [PMID: 29327929]
Hassan, A.H.E.; Park, K.T.; Kim, H.J.; Lee, H.J.; Kwon, Y.H.; Hwang, J.Y.; Jang, C.G.; Chung, J.H.; Park, K.D.; Lee, S.J.; Oh, S.J.; Lee, Y.S. Fluorinated CRA13 analogues: Synthesis, in vitro evaluation, radiosynthesis, in silico and in vivo PET study. Bioorg. Chem., 2020, 99, 103834.
[] [PMID: 32334193]
Tietz, O.; Sharma, S.K.; Kaur, J.; Way, J.; Marshall, A.; Wuest, M.; Wuest, F. Synthesis of three 18F-labelled cyclooxygenase-2 (COX-2) inhibitors based on a pyrimidine scaffold. Org. Biomol. Chem., 2013, 11(46), 8052-8064.
[] [PMID: 24145766]
Wodtke, R.; Hauser, C.; Ruiz, G.G. Jäckel, E.; Bauer, D.; Lohse, M.; Wong, A.; Pufe, J.; Ludwig, F.A.; Fischer, S.; Hauser, S.; Greif, D.; Pisabarro, M.T.; Pietzsch, J.; Pietsch, M.; Löser, R. Nε -acryloyllysine piperazides as irreversible inhibitors of transglutaminase 2: Synthesis, structure-activity relationships, and pharmacokinetic profiling. J. Med. Chem., 2018, 61(10), 4528-4560.
[] [PMID: 29664627]
Jacobson, K.A.; Fischer, B.; Rhee, A.M. Molecular probes for muscarinic receptors: Functionalized congeners of selective muscarinic antagonists. Life Sci., 1995, 56(11-12), 823-830.
[] [PMID: 10188781]
Thompson, A.J.; Verheij, M.H.P.; Verbeek, J.; Windhorst, A.D.; Esch, I.J.P.; Lummis, S.C.R. The binding characteristics and orientation of a novel radioligand with distinct properties at 5-HT3A and 5-HT3AB receptors. Neuropharmacology, 2014, 86, 378-388.
[] [PMID: 25174552]
Chen, X. QSAR and primary docking studies of trans-stilbene (TSB) series of imaging agents for β-amyloid plaques. J. Mol. Struct. Theochem., 2006, 763(1-3), 83-89.
Kumar, R.; Kumar, A. Långström, B.; Darreh-Shori, T. Discovery of novel choline acetyltransferase inhibitors using structure-based virtual screening. Sci. Rep., 2017, 7(1), 16287.
[] [PMID: 28127051]
Neo Shin, N.; Jeon, H.; Jung, Y.; Baek, S.; Lee, S.; Yoo, H.C.; Bae, G.H.; Park, K.; Yang, S.H.; Han, J.M.; Kim, I.; Kim, Y. Fluorescent 1,4-naphthoquinones to visualize diffuse and dense-core amyloid plaques in APP/PS1 transgenic mouse brains. ACS Chem. Neurosci., 2019, 10(6), 3031-3044.
[] [PMID: 31016960]
Kapp, O.H.; Siemion, J.; Eckelman, W.C.; Cohen, V.I.; Reba, R.C. Molecular modeling of the interaction of diagnostic radiopharmaceuticals with receptor proteins: m2 antagonist binding to the muscarinic M2 subtype receptor. Recept. Signal Transduct., 1997, 7(3), 177-201.
[PMID: 9440504]
Floresta, G.; Amata, E.; Barbaraci, C.; Gentile, D.; Turnaturi, R.; Marrazzo, A.; Rescifina, A. A structure and ligand based virtual screening of a database of “small” marine natural products for the identification of “blue” sigma-2 receptor ligands. Mar. Drugs, 2018, 16(10), 384.
[] [PMID: 30322188]
Muchtaridi, M.; Rosilawati, N.E.; Yusuf, M.; Kartamihardja, A.H.S.; Samsuddin, S. Molecular dynamics simulation of Fe-NO2 At-alpha mangostin as radiopharmaceutical model for detection of fatty acid synthase in cancer. J. Adv. Pharm. Technol. Res., 2021, 12(2), 113-119.
[] [PMID: 34159140]
Rivera, M.S. Fernández, M.L.; León, C.S.; Sablón, C.M.; Bencomo, M.A.; Perera, P.A.; Prats, C.A.; Zoppolo, F.; Kreimerman, I.; Pardo, T.; Reyes, L.; Balcerzyk, M.; Dubed, B.G.; Mercerón,M.D.; Espinosa, R.L.A.; Engler, H.; Savio, E.; Rodríguez,T.C. [18 F]am ylovis as a potential PET probe for β-amyloid plaque: Synthesis, in silico, in vitro and in vivo evaluations. Curr. Radiopharm., 2019, 12(1), 58-71.
[] [PMID: 30605068]
Murugan, N.A.; Chiotis, K.; Rodriguez-Vieitez, E.; Lemoine, L. Ågren, H.; Nordberg, A. Cross-interaction of tau PET tracers with monoamine oxidase B: Evidence from in silico modelling and in vivo imaging. Eur. J. Nucl. Med. Mol. Imaging, 2019, 46(6), 1369-1382.
[] [PMID: 30919054]
Balamurugan, K.; Murugan, N.A. Ågren, H. Multistep modeling strategy to improve the binding affinity prediction of PET tracers to Aβ42: Case study with styrylbenzoxazole derivatives. ACS Chem. Neurosci., 2016, 7(12), 1698-1705.
[] [PMID: 27626391]
Mishra, S.K.; Yamaguchi, Y.; Higuchi, M.; Sahara, N. Pick’s Tau fibril shows multiple distinct PET probe binding sites: Insights from computational modelling. Int. J. Mol. Sci., 2020, 22(1), 349.
[] [PMID: 33396273]
Hsieh, C.J.; Riad, A.; Lee, J.Y.; Sahlholm, K.; Xu, K.Y.; Luedtke, R.R. Interaction of ligands for PET with the dopamine D3 receptor: In silico and in vitro methods. Biomol., 2021, 11(4), 529.
Kuang, G.; Murugan, N.A.; Tu, Y.; Nordberg, A. Ågren, H. Investigation of the binding profiles of AZD2184 and thioflavin T with amyloid-β(1–42) fibril by molecular docking and molecular dynamics methods. J. Phys. Chem. B, 2015, 119(35), 11560-11567.
[] [PMID: 26266837]
Murugan, N.A.; Nordberg, A. Ågren, H. Different positron emission tomography tau tracers bind to multiple binding sites on the tau fibril: Insight from computational modeling. ACS Chem. Neurosci., 2018, 9(7), 1757-1767.
[] [PMID: 29630333]
Kerrigan, J.E. Molecular dynamics simulations in drug design. Methods Mol. Biol., 2013, 993, 95-113.
[] [PMID: 23568466]
Karplus, M.; McCammon, J.A. Molecular dynamics simulations of biomolecules. Nat. Struct. Biol., 2002, 9(9), 646-652.
[] [PMID: 12198485]
Leimkuhler, B.J.; Skeel, R.D. Symplectic numerical integrators in constrained Hamiltonian systems. J. Comput. Phys., 1994, 112(1), 117-125.
Hollingsworth, S.A.; Dror, R.O. Molecular dynamics simulation for all. Neuron, 2018, 99(6), 1129-1143.
[] [PMID: 30236283]
Do, P.C.; Lee, E.H.; Le, L. Steered molecular dynamics simulation in rational drug design. J. Chem. Inf. Model., 2018, 58(8), 1473-1482.
[] [PMID: 29975531]
Perez, A.; Morrone, J.A.; Simmerling, C.; Dill, K.A. Advances in free energy based simulations of protein folding and ligand binding. Curr. Opin. Struct. Biol., 2016, 36, 25-31.
[] [PMID: 26773233]
Raval, A.; Piana, S.; Eastwood, M.P.; Dror, R.O.; Shaw, D.E. Refinement of protein structure homology models via long, all atom molecular dynamics simulations. Proteins, 2012, 80(8), 2071-2079.
[] [PMID: 22513870]
Zhou, J.; Yi, Q.; Tang, L. The roles of nuclear focal adhesion kinase (FAK) on cancer: A focused review. J. Exp. Clin. Cancer Res., 2019, 38(1), 250.
[] [PMID: 31186061]
Kuźnik, A.; Październiok, H.A.; Jewula, P.; Kuźnik, N. Bisphosphonates-much more than only drugs for bone diseases. Eur. J. Pharmacol., 2020, 866, 172773.
[] [PMID: 31705903]
Zhao, L.; Ciallella, H.L.; Aleksunes, L.M.; Zhu, H. Advancing Computer-Aided Drug Discovery (CADD) by big data and data-driven machine learning modeling. Drug Discov. Today, 2020, 25(9), 1624-1638.
Patel, L.; Shukla, T.; Huang, X.; Ussery, D.W.; Wang, S. Machine learning methods in drug discovery. Molecules, 2020, 25(22), 5277.
[] [PMID: 33198233]
Vamathevan, J.; Clark, D.; Czodrowski, P.; Dunham, I.; Ferran, E.; Lee, G.; Li, B.; Madabhushi, A.; Shah, P.; Spitzer, M.; Zhao, S. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov., 2019, 18(6), 463-477.
[] [PMID: 30976107]
Stephenson, N.; Shane, E.; Chase, J.; Rowland, J.; Ries, D.; Justice, N.; Zhang, J.; Chan, L.; Cao, R. Survey of machine learning techniques in drug discovery. Curr. Drug Metab., 2019, 20(3), 185-193.
[] [PMID: 30124147]
Ekins, S.; Puhl, A.C.; Zorn, K.M.; Lane, T.R.; Russo, D.P.; Klein, J.J.; Hickey, A.J.; Clark, A.M. Exploiting machine learning for end to end drug discovery and development. Nat. Mater., 2019, 18(5), 435-441.
[] [PMID: 31000803]
Lo, Y.C.; Rensi, S.E.; Torng, W.; Altman, R.B. Machine learning in chemoinformatics and drug discovery. Drug Discov. Today, 2018, 23(8), 1538-1546.
[] [PMID: 29750902]
Noble, W.S. What is a support vector machine? Nat. Biotechnol., 2006, 24(12), 1565-1567.
[] [PMID: 17160063]
Burden, F.R.; Winkler, D.A. Relevance vector machines: Sparse classification methods for QSAR. J. Chem. Inf. Model., 2015, 55(8), 1529-1534.
[] [PMID: 26158341]
Mitchell, J.B.O. Machine learning methods in chemoinformatics. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2014, 4(5), 468-481.
[] [PMID: 25285160]
Ghasemi, F.; Mehridehnavi, A.; Pérez, G.A.; Pérez, S.H. Neural network and deep-learning algorithms used in QSAR studies: Merits and drawbacks. Drug Discov. Today, 2018, 23(10), 1784-1790.
[] [PMID: 29936244]
LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature, 2015, 521(7553), 436-444.
[] [PMID: 26017442]
Janet, J.P.; Kulik, H.J. Resolving transition metal chemical space: Feature selection for machine learning and structure-property relationships. J. Phys. Chem. A, 2017, 121(46), 8939-8954.
[] [PMID: 29095620]
Winkler, D.A. Sparse QSAR modelling methods for therapeutic and regenerative medicine. J. Comput. Aided Mol. Des., 2018, 32(4), 497-509.
[] [PMID: 29445894]
Mirjalili, S. Genetic algorithm. In: Mirjalili, S., Ed.; Evolutionary Algorithms and Neural Networks; Springer: Cham, 2019; pp. 43- 55.
Bäck, T.; Schwefel, H.P. An overview of evolutionary algorithms for parameter optimization. Evol. Comput., 1993, 1(1), 1-23.
Slowik, A.; Kwasnicka, H. Evolutionary algorithms and their applications to engineering problems. Neur. Comp. App., 2020, 32, 12363-12379.
Korb, O. Efficient ant colony optimization algorithms for structure- and ligand-based drug design. Chem. Cent. J., 2009, 3(S1), O10.
Korb, O.; Stützle, T.; Exner, T.E. An ant colony optimization approach to flexible protein–ligand docking. Swarm Intell., 2007, 1(2), 115-134.
Topliss, J.G.; Edwards, R.P. Chance factors in studies of quantitative structure-activity relationships. J. Med. Chem., 1979, 22(10), 1238-1244.
[] [PMID: 513071]
Fernandez, M.; Caballero, J.; Fernandez, L.; Sarai, A. Genetic algorithm optimization in drug design QSAR: Bayesian-Regularized Genetic Neural Networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM). Mol. Divers., 2011, 15(1), 269-289.
[] [PMID: 20306130]
Urbanowicz, R.J.; Meeker, M.; La Cava, W.; Olson, R.S.; Moore, J.H. Relief-based feature selection: Introduction and review. J. Biomed. Inform., 2018, 85, 189-203.
[] [PMID: 30031057]
Ringnér, M. What is principal component analysis? Nat. Biotechnol., 2008, 26(3), 303-304.
[] [PMID: 18327243]
Le, T.; Epa, V.C.; Burden, F.R.; Winkler, D.A. Quantitative structure-property relationship modeling of diverse materials properties. Chem. Rev., 2012, 112(5), 2889-2919.
[] [PMID: 22251444]
Gupta, R.; Srivastava, D.; Sahu, M.; Tiwari, S.; Ambasta, R.K.; Kumar, P. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery. Mol. Divers., 2021, 25(3), 1315-1360.
[] [PMID: 33844136]
Duffy, I.R.; Boyle, A.J.; Vasdev, N. Improving PET imaging acquisition and analysis with machine learning: A narrative review with focus on Alzheimer’s disease and oncology. Mol. Imaging, 2019, 18, 15360121.
[] [PMID: 31429375]
Gong, K.; Berg, E.; Cherry, S.R.; Qi, J. Machine learning in PET: From photon detection to quantitative image reconstruction. Proc. IEEE, 2020, 108(1), 51-68.
Arabi, H.; Bortolin, K.; Ginovart, N.; Garibotto, V.; Zaidi, H. Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies. Hum. Brain Mapp., 2020, 41(13), 3667-3679.
[] [PMID: 32436261]
Wang, T.; Lei, Y.; Fu, Y.; Curran, W.J.; Liu, T.; Yang, X Machine learning in quantitative PET imaging arXiv:200106597, 2020.
Taylor, J.C.; Fenner, J.W. Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: The beginning of the end for semi-quantification? EJNMMI Phys., 2017, 4(1), 29.
[] [PMID: 29188397]
Vicente, A.M.G. Galán, M.J.T.; Pardo, F.J.P.; Amo-Salas, M.; Marín, B.M.; Muñoz, S.N. Increasing the confidence of 18F-Florbetaben PET interpretations: Machine learning quantitative approximation. Rev. Españ. Med. Nucl. Imag. Mol., 2021, 41(3), 153-163.
Huang, G.H.; Lin, C.H.; Cai, Y.R.; Chen, T.B.; Hsu, S.Y.; Lu, N.H.; Chen, H.Y.; Wu, Y.C. Multiclass machine learning classification of functional brain images for Parkinson’s disease stage prediction. Stat. Anal. Data Min., 2020, 13(5), 508-523.
Katako, A.; Shelton, P.; Goertzen, A.L.; Levin, D.; Bybel, B.; Aljuaid, M.; Yoon, H.J.; Kang, D.Y.; Kim, S.M.; Lee, C.S.; Ko, J.H. Machine learning identified an Alzheimer’s disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson’s disease dementia. Sci. Rep., 2018, 8(1), 13236.
[] [PMID: 29311619]
Liu, Y.; Zhao, T.; Ju, W.; Shi, S. Materials discovery and design using machine learning. J. Mater., 2017, 3(3), 159-177.
Salahinejad, M.; Le, T.C.; Winkler, D.A. Aqueous solubility prediction: Do crystal lattice interactions help? Mol. Pharm., 2013, 10(7), 2757-2766.
[] [PMID: 23718811]
Tropsha, A. Best practices for QSAR model development, validation, and exploitation. Mol. Inform., 2010, 29(6-7), 476-488.
[] [PMID: 27463326]
Daniel, C. González, L.; Neese, F. Quantum theory: The challenge of transition metal complexes. Phys. Chem. Chem. Phys., 2021, 23(4), 2533-2534.
[] [PMID: 33475117]
Sayers, E.W.; Agarwala, R.; Bolton, E.E.; Brister, J.R.; Canese, K.; Clark, K.; Connor, R.; Fiorini, N.; Funk, K.; Hefferon, T.; Holmes, J.B.; Kim, S.; Kimchi, A.; Kitts, P.A.; Lathrop, S.; Lu, Z.; Madden, T.L.; Marchler, B.A.; Phan, L.; Schneider, V.A.; Schoch, C.L.; Pruitt, K.D.; Ostell, J. Database resources of the national center for biotechnology information. Nucleic Acids Res., 2019, 47(D1), D23-D28.
[] [PMID: 30395293]
Irwin, J.J.; Tang, K.G.; Young, J.; Dandarchuluun, C.; Wong, B.R.; Khurelbaatar, M. ZINC20 - A free ultralarge scale chemical database for ligand discovery. J. Chem. Inf. Mod., 2020.
Gaulton, A.; Hersey, A.; Nowotka, M.; Bento, A.P.; Chambers, J.; Mendez, D.; Mutowo, P.; Atkinson, F.; Bellis, L.J. Cibrián, U.E.; Davies, M.; Dedman, N.; Karlsson, A.; Magariños, M.P.; Overington, J.P.; Papadatos, G.; Smit, I.; Leach, A.R. The ChEMBL database in 2017. Nucleic Acids Res., 2017, 45(D1), D945-D954.
[] [PMID: 27899562]
Pence, H.E.; Williams, A. ChemSpider: An online chemical information resource. J. Chem. Educ., 2010, 87(11), 1123-1124.
Chen, J.H.; Linstead, E.; Swamidass, S.J.; Wang, D.; Baldi, P. ChemDB update full-text search and virtual chemical space. Bioinformatics, 2007, 23(17), 2348-2351.
[] [PMID: 17599932]
Degtyarenko, K.; de Matos, P.; Ennis, M.; Hastings, J.; Zbinden, M.; McNaught, A. Alcántara, R.; Darsow, M.; Guedj, M.; Ashburner, M. ChEBI: A database and ontology for chemical entities of biological interest. Nucleic Acids Res., 2008, 36(S1), D344-D350.
[PMID: 17932057]
Ghasemi, J.B.; Salahinejad, M.; Rofouei, M.K. Review of the quantitative structure-activity relationship modelling methods on estimation of formation constants of macrocyclic compounds with different guest molecules. Supramol. Chem., 2011, 23(9), 614-629.
Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors; John Wiley & Sons: Weinheim, 2008.
Seko, A.; Togo, A.; Tanaka, I. Descriptors for machine learning of materials data. In: Tanaka, I.; Ed. Nanoinformatics; Springer: Singapore, 2018; pp. 3-23.
Lewis, R.A.; Wood, D. Modern 2D QSAR for drug discovery. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2014, 4(6), 505-522.
Ajmani, S.; Jadhav, K.; Kulkarni, S.A. Group based QSAR (G-QSAR): Mitigating interpretation challenges in QSAR. QSAR Comb. Sci., 2009, 28(1), 36-51.
Myint, K.Z.; Xie, X.Q. Recent advances in fragment based QSAR and multi-dimensional QSAR methods. Int. J. Mol. Sci., 2010, 11(10), 3846-3866.
[] [PMID: 21152304]
Verma, J.; Khedkar, V.; Coutinho, E. 3D-QSAR in drug design- a review. Curr. Top. Med. Chem., 2010, 10(1), 95-115.
[] [PMID: 19929826]
Akamatsu, M. Current state and perspectives of 3D-QSAR. Curr. Top. Med. Chem., 2002, 2(12), 1381-1394.
[] [PMID: 12470286]
Cramer, R.D.; Patterson, D.E.; Bunce, J.D. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc., 1988, 110(18), 5959-5967.
[] [PMID: 22148765]
Kubinyi, H. Comparative molecular field analysis (CoMFA). Encyc. Comp. Chem., 1998, 1, 448-460.
Salahinejad, M.; Ghasemi, J.B. 3D-QSAR studies on the toxicity of substituted benzenes to Tetrahymena pyriformis: CoMFA, CoMSIA and VolSurf approaches. Ecotoxicol. Environ. Saf., 2014, 105, 128-134.
[] [PMID: 24636479]
Mauri, A.; Consonni, V.; Pavan, M.; Todeschini, R. Dragon software: An easy approach to molecular descriptor calculations. MATCH Commun. Math. Comput. Chem., 2006, 56(2), 237-248.
Yap, C.W. PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem., 2011, 32(7), 1466-1474.
[] [PMID: 21425294]
Vilar, S.; Cozza, G.; Moro, S. Medicinal chemistry and the molecular operating environment (MOE): Application of QSAR and molecular docking to drug discovery. Curr. Top. Med. Chem., 2008, 8(18), 1555-1572.
[] [PMID: 19075767]
Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoinformatics; Wiley‐VCH; VCH Verlag GmbH & Co: Weinheim, 2009.
Schütt, K.T.; Glawe, H.; Brockherde, F.; Sanna, A.; Müller, K.R.; Gross, E.K.U. How to represent crystal structures for machine learning: Towards fast prediction of electronic properties. Phys. Rev. B Condens. Matter Mater. Phys., 2014, 89(20), 205118.
Minenkov, Y.; Sharapa, D.I.; Cavallo, L. Application of semiempirical methods to transition metal complexes: Fast results but hard to predict accuracy. J. Chem. Theory Comput., 2018, 14(7), 3428-3439.
[] [PMID: 29787256]
Rogers, D.; Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model., 2010, 50(5), 742-754.
[] [PMID: 20426451]
Xie, L.; Xu, L.; Kong, R.; Chang, S.; Xu, X. Improvement of prediction performance with conjoint molecular fingerprint in deep learning. Front. Pharmacol., 2020, 11, 606668.
[] [PMID: 33488387]
David, L.; Thakkar, A.; Mercado, R.; Engkvist, O. Molecular representations in AI-driven drug discovery: A review and practical guide. J. Cheminform., 2020, 12(1), 56.
[] [PMID: 33431035]
Townsend, J.; Micucci, C.P.; Hymel, J.H.; Maroulas, V.; Vogiatzis, K.D. Representation of molecular structures with persistent homology for machine learning applications in chemistry. Nat. Commun., 2020, 11(1), 3230.
[] [PMID: 31911652]
Schneider, G. Virtual screening: An endless staircase? Nat. Rev. Drug Discov., 2010, 9(4), 273-276.
[] [PMID: 20357802]
Salahinejad, M. Nano-QSPR modelling of carbon-based nanomaterials properties. Curr. Top. Med. Chem., 2015, 15(18), 1868-1886.
[] [PMID: 25961518]
Schleder, G.R.; Padilha, A.C.M.; Acosta, C.M.; Costa, M.; Fazzio, A. From DFT to machine learning: Recent approaches to materials science–a review. J. Phys. Mater, 2019, 2(3), 032001.
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.; Rathman, J.; Terfloth, L.; Gasteiger, J.; Richard, A.; Tropsha, A. QSAR modeling: Where have you been? Where are you going to? J. Med. Chem., 2014, 57(12), 4977-5010.
[] [PMID: 24351051]
Roy, K.; Das, R.N.; Ambure, P.; Aher, R.B. Be aware of error measures. Further studies on validation of predictive QSAR models. Chemom. Intell. Lab. Syst., 2016, 152, 18-33.
Alexander, D.L.J.; Tropsha, A.; Winkler, D.A. Beware of R2: Simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. J. Chem. Inf. Model., 2015, 55(7), 1316-1322.
[] [PMID: 26099013]
Gramatica, P. External evaluation of QSAR models, in addition to cross-validation: Verification of predictive capability on totally new chemicals. Mol. Inform., 2014, 33(4), 311-314.
[] [PMID: 27485777]
Tropsha, A.; Gramatica, P.; Gombar, V.K. The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb. Sci., 2003, 22(1), 69-77.
Golbraikh, A.; Tropsha, A. Beware of q2! J. Mol. Graph. Model., 2002, 20(4), 269-276.
[] [PMID: 11858635]
Veerasamy, R.; Rajak, H.; Jain, A.; Sivadasan, S.; Varghese, C.P.; Agrawal, R.K. Validation of QSAR models strategies and importance. Int. J. Drug Des. Discov., 2011, 3, 511-519.
Nunn, A.D. Structure distribution relationships of radiopharmaceuticals. J. Chromatogr. A, 1983, 255, 91-100.
[] [PMID: 6863425]
Nunn, A.D.; Loberg, M.D.; Conley, R.A. A structure distribution relationship approach leading to the development of Tc-99m mebrofenin: An improved cholescintigraphic agent. J. Nucl. Med., 1983, 24(5), 423-430.
[PMID: 6842291]
Maddalena, D.J.; Snowdon, G.M.; Wilson, J.G. Structure distribution studies on some 99mTc-o-hydroxybenzyliminodiacetic acid complexes. Int. J. Rad. Appl. Instrum. B, 1988, 15(3), 319-325.
[] [PMID: 3384680]
Salako, Q.; Theobald, A.E. Structure distribution relationship studies of 99mTc-2,3-diamine complexes. Int. J. Rad. Appl. Instrum. B, 1990, 17(4), 437-441.
[] [PMID: 2387751]
Hui, M.B.V.; Chen, D.C.P.; Lien, E.J. Analysis of the quantitative structure activity relationship of technetium-99m-labeled diaminedithiol (DADT) and propyleneamineoxime (PAO) brain blood flow analogues. Int. J. Rad. Appl. Instrum. [A], 1991, 42(6), 503-508.
[] [PMID: 1652579]
Zhang, H.; Li, B.; Dai, M. Quantitative Structure–Activity Relationship (QSAR) analysis of cationic complexes of heart perfusion imaging agents and subsequent proposition of two different uptake mechanisms. J. Pharm. Pharmacol., 2010, 55(4), 505-511.
[] [PMID: 12803772]
Zhang, H.; Dai, M.; Qi, C.; Li, B.; Guo, X. Synthesis, biodistribution and quantitative structure activity relationship studies of new 99mTc labeled pseudo-peptide complexes. Appl. Radiat. Isot., 2004, 60(5), 643-651.
[] [PMID: 15082041]
Zhang, H.; Ye, H.; Zhang, Y.; Zheng, X.; Han, J.; Li, H.; Liu, C. Synthesis, biodistribution and QSAR studies of five Tc-99m labeled novel N3S pseudo peptide complexes. Med. Chem. Res., 2005, 14(1), 40-56.
Wolohan, P.; Reichert, D.E. Molecular modeling of hexakis(areneisonitrile)technetium(I), tricarbonyl η5 cyclopentadienyl technetium and technetium(V)-oxo complexes: MM3 parameter development and prediction of biological properties. J. Mol. Graph. Model., 2007, 25(5), 616-632.
[] [PMID: 16769234]
Singh, S.; Ojha, H.; Tiwari, A.K.; Kumar, N.; Singh, B.; Mishra, A.K. Design, synthesis, and in vitro antiproliferative activity of benzimidazole analogues for radiopharmaceutical efficacy. Cancer Biother. Radiopharm., 2010, 25(2), 245-250.
[] [PMID: 20423239]
Salahinejad, M.; Mirshojaei, S.F. Quantitative structure–activity relationship analysis to elucidate the clearance mechanisms of Tc-99m labeled quinolone antibiotics. J. Radioanal. Nucl. Chem., 2016, 307(1), 437-445.
Salahinejad, M. Quantitative structure property relationships on formation constants of radiometals for radiopharmaceuticals applications. J. Radioanal. Nucl. Chem., 2015, 303(1), 671-680.
Salahinejad, M.; Zolfonoun, E. Modeling of radiometal complexation formation with bifunctional coupling agents using ligand metal interaction profile. Int. J. Quant. Struct. Prop. Relat., 2017, 2(1), 95-105.
Wolohan, P.; Yoo, J.; Welch, M.J.; Reichert, D.E. QSAR studies of copper azamacrocycles and thiosemicarbazones: MM3 parameter development and prediction of biological properties. J. Med. Chem., 2005, 48(17), 5561-5569.
[] [PMID: 16107156]
Comba, P.; Martin, B.; Sanyal, A.; Stephan, H. The computation of lipophilicities of 64Cu PET systems based on a novel approach for fluctuating charges. Dalton Trans., 2013, 42(31), 11066-11073.
[] [PMID: 23799488]
Lambie, H.; Cook, A.M.; Scarsbrook, A.F.; Lodge, J.P.A.; Robinson, P.J.; Chowdhury, F.U. Tc99m- Hepatobiliary Iminodiacetic Acid (HIDA) scintigraphy in clinical practice. Clin. Radiol., 2011, 66(11), 1094-1105.
[] [PMID: 21861996]
Schmidt, D.E.; Kessler, R.M.; De Paulis, T.; Votaw, J.R. Aromatic and amine substituent effects on the apparent lipophilicities of N-[(2-pyrrolidinyl)methyl]-substituted benzamides. J. Pharm. Sci., 1994, 83(3), 305-315.
[] [PMID: 8207673]
Huang, Y.; Hammond, P.S.; Whirrett, B.R.; Kuhner, R.J.; Wu, L.; Childers, S.R.; Mach, R.H. Synthesis and quantitative structure activity relationships of N-(1-benzylpiperidin-4-yl)phenylacetamides and related analogues as potent and selective σ1 receptor ligands. J. Med. Chem., 1998, 41(13), 2361-2370.
[] [PMID: 9632369]
Wang, W.; Zhang, J.; Liu, B. QSAR study of 125I-labeled 2-(4-aminophenyl)benzothiazole derivatives as imaging agents for β-amyloid in the brain with Alzheimer’s disease. J. Radioanal. Nucl. Chem., 2005, 266(1), 107-111.
Cisek, K.; Kuret, J. QSAR studies for prediction of cross-β sheet aggregate binding affinity and selectivity. Bioorg. Med. Chem., 2012, 20(4), 1434-1441.
[] [PMID: 22285571]
Mavel, S.; Mincheva, Z.; Méheux, N.; Carcenac, Y.; Guilloteau, D.; Abarbri, M.; Emond, P. QSAR study and synthesis of new phenyltropanes as ligands of the dopamine transporter (DAT). Bioorg. Med. Chem., 2012, 20(4), 1388-1395.
[] [PMID: 22300887]
Ambure, P.; Roy, K. Exploring structural requirements of imaging agents against Aβ plaques in Alzheimer’s disease: A QSAR approach. Comb. Chem. High Throughput Screen., 2015, 18(4), 411-419.
[] [PMID: 25747447]
Kumar, R. Långström, B.; Darreh, D.T. Novel ligands of choline acetyltransferase designed by in silico molecular docking, hologram QSAR and lead optimization. Sci. Rep., 2016, 6(1), 31247.
[] [PMID: 27507101]
Tamiji, Z.; Salahinejad, M.; Niazi, A. Molecular modeling of potential PET imaging agents for adenosine receptor in Parkinson’s disease. Struct. Chem., 2018, 29(2), 467-479.
De, P.; Bhattacharyya, D.; Roy, K. Application of multilayered strategy for variable selection in QSAR modeling of PET and SPECT imaging agents as diagnostic agents for Alzheimer’s disease. Struct. Chem., 2019, 30(6), 2429-2445.
Kumar, N.; Tiwari, A.K.; Kakkar, D.; Saini, N.; Chand, M.; Mishra, A.K. Design, synthesis, and fluorescence lifetime study of benzothiazole derivatives for imaging of amyloids. Cancer Biother. Radiopharm., 2010, 25(5), 571-575.
[] [PMID: 20874487]
De, P.; Roy, J.; Bhattacharyya, D.; Roy, K. Chemometric modeling of PET imaging agents for diagnosis of Parkinson’s disease: A QSAR approach. Struct. Chem., 2020, 31(5), 1969-1981.
De, P.; Roy, K. QSAR modeling of PET imaging agents for the diagnosis of Parkinson’s disease targeting dopamine receptor. Theor. Chem. Acc., 2020, 139(12), 176.
Wellsow, J.; Kovar, K.A.; Machulla, H.J. Molecular modeling of potential new and selective PET radiotracers for the serotonin transporter. Positron Emission Tomography. J. Pharm. Pharm. Sci., 2002, 5(3), 245-257.
[PMID: 12553893]
Wellsow, J.; Machulla, H.J.; Kovar, K.A. 3D QSAR of serotonin transporter ligands: CoMFA and CoMSIA studies. Quant. Struct.-Act. Relationsh., 2002, 21(6), 577-589.
Kim, M.K.; Choo, I.H.; Lee, H.S.; Woo, J.I.; Chong, Y. 3D-QSAR of PET Agents for Imaging β-Amyloid in Alzheimer’s Disease. Bull. Korean Chem. Soc., 2007, 28(7), 1231-1234.
Hocke, C.; Prante, O.; Salama, I.; Hübner, H. Löber, S.; Kuwert, T.; Gmeiner, P. 18F-Labeled FAUC 346 and BP 897 derivatives as subtype-selective potential PET radioligands for the dopamine D3 receptor. ChemMedChem, 2008, 3(5), 788-793.
[] [PMID: 18306190]
Oberdorf, C.; Schmidt, T.J.; Wünsch, B. 5D-QSAR for spirocyclic σ1 receptor ligands by Quasar receptor surface modeling. Eur. J. Med. Chem., 2010, 45(7), 3116-3124.
[] [PMID: 20427100]
Yang, Y.; Zhang, X.; Cui, M.; Zhang, J.; Guo, Z.; Li, Y.; Zhang, X.; Dai, J.; Liu, B. Preliminary characterization and in vivo studies of structurally identical 18 f-and 125 i-labeled benzyloxybenzenes for PET/SPECT imaging of β-amyloid plaques. Sci. Rep., 2015, 5(1), 12084.
[] [PMID: 26170205]
Kovac, M.; Mavel, S.; Deuther-Conrad, W.; Méheux, N. Glöckner, J.; Wenzel, B.; Anderluh, M.; Brust, P.; Guilloteau, D.; Emond, P. 3D QSAR study, synthesis, and in vitro evaluation of (+)-5-FBVM as potential PET radioligand for the vesicular acetylcholine transporter (VAChT). Bioorg. Med. Chem., 2010, 18(21), 7659-7667.
[] [PMID: 20889347]
Szymoszek, A.; Wenzel, B.; Scheunemann, M.; Steinbach, J.; Schüürmann, G. First CoMFA characterization of vesamicol analogs as ligands for the vesicular acetylcholine transporter. J. Med. Chem., 2008, 51(7), 2128-2136.
[] [PMID: 18324757]
Salama, I.; Hocke, C.; Utz, W.; Prante, O.; Boeckler, F.; Hübner, H.; Kuwert, T.; Gmeiner, P. Structure-selectivity investigations of D2-like receptor ligands by CoMFA and CoMSIA guiding the discovery of D3 selective PET radioligands. J. Med. Chem., 2007, 50(3), 489-500.
[] [PMID: 17266201]
Muñoz, C.; Adasme, F.; Alzate-Morales, J.H.; Vergara-Jaque, A.; Kniess, T.; Caballero, J. Study of differences in the VEGFR2 inhibitory activities between semaxanib and SU5205 using 3D-QSAR, docking, and molecular dynamics simulations. J. Mol. Graph. Model., 2012, 32, 39-48.
[] [PMID: 22070999]
Laurini, E.; Zampieri, D.; Mamolo, M.G.; Vio, L.; Zanette, C.; Florio, C.; Posocco, P.; Fermeglia, M.; Pricl, S. A 3D-pharmacophore model for σ2 receptors based on a series of substituted benzo[d]oxazol-2(3H)-one derivatives. Bioorg. Med. Chem. Lett., 2010, 20(9), 2954-2957.
[] [PMID: 20347592]
Wunsch, B. Pharmacophore models and development of spirocyclic ligands for σ1 receptors. Curr. Pharm. Des., 2012, 18(7), 930-937.
[] [PMID: 22288413]
Marondedze, E.F.; Govender, K.K.; Govender, P.P. Ligand-based pharmacophore modelling and virtual screening for the identification of amyloid-beta diagnostic molecules. J. Mol. Graph. Model., 2020, 101, 107711.
[] [PMID: 32898834]
Vlassenko, A.G.; Benzinger, T.L.S.; Morris, J.C. PET amyloid-beta imaging in preclinical Alzheimer’s disease. Biochim. Biophys. Acta Mol. Basis Dis., 2012, 1822(3), 370-379.
[] [PMID: 22108203]
Günther, R.; Deuther, C.W.; Moldovan, R.; Fischer, S.; Brust, P. A 3D-QSAR model for cannabinoid receptor (CB2) ligands derived from aligned pharmacophors. J. Cheminform., 2013, 5(S1), 40.
[] [PMID: 23289532]
Wermuth, C.G.; Ganellin, C.R.; Lindberg, P.; Mitscher, L.A. Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998). Pure Appl. Chem., 1998, 70(5), 1129-1143.
Güner, O.F.; Bowen, J.P. Setting the record straight: The origin of the pharmacophore concept. J. Chem. Inf. Model., 2014, 54(5), 1269-1283.
[] [PMID: 24745881]
Seidel, T.; Bryant, S.D.; Ibis, G.; Poli, G.; Langer, T. 3D pharmacophore modeling techniques in computer aided molecular design using LigandScout. Tut. Cheminf., 2017, 281, 279-309.
Schaller, D. Šribar, D.; Noonan, T.; Deng, L.; Nguyen, T.N.; Pach, S.; Machalz, D.; Bermudez, M.; Wolber, G. Next generation 3D pharmacophore modeling. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2020, 10(4), e1468.
Pirhadi, S.; Shiri, F.; Ghasemi, J.B. Methods and applications of structure based pharmacophores in drug discovery. Curr. Top. Med. Chem., 2013, 13(9), 1036-1047.
[] [PMID: 23651482]
Muchtaridi, M.; Syahidah, H.; Subarnas, A.; Yusuf, M.; Bryant, S.; Langer, T. Molecular docking and 3D-pharmacophore modeling to study the interactions of chalcone derivatives with estrogen receptor alpha. Pharmaceuticals (Basel), 2017, 10(4), 81.
[] [PMID: 29035298]
Glennon, R. Pharmacophore identification for sigma-1 (sigma1) receptor binding: Application of the “deconstruction reconstruction elaboration” approach. Mini Rev. Med. Chem., 2005, 5(10), 927-940.
[] [PMID: 16250835]
Neves, B.J.; Braga, R.C.; Melo, F.C.C.; Moreira, F.J.T.; Muratov, E.N.; Andrade, C.H. QSAR-based virtual screening: Advances and applications in drug discovery. Front. Pharmacol., 2018, 9(1275), 1275.
[] [PMID: 30524275]
Liu, C.; Yin, J.; Yao, J.; Xu, Z.; Tao, Y.; Zhang, H. Pharmacophore-based virtual screening toward the discovery of novel anti echinococcal compounds. Front. Cell. Infect. Microbiol., 2020, 10(118), 118.
[] [PMID: 32266168]
Li, Q. Application of fragment based drug discovery to versatile targets. Front. Mol. Biosci., 2020, 7(180), 180.
[] [PMID: 32850968]
Maia, E.H.B.; Assis, L.C.; de Oliveira, T.A.; da Silva, A.M.; Taranto, A.G. Structure based virtual screening: From classical to artificial intelligence. Front Chem., 2020, 8(343), 343.
[] [PMID: 32411671]
Lionta, E.; Spyrou, G.; Vassilatis, D.; Cournia, Z. Structure based virtual screening for drug discovery: Principles, applications and recent advances. Curr. Top. Med. Chem., 2014, 14(16), 1923-1938.
[] [PMID: 25262799]
Li, Q.; Shah, S. Protein Bioinformatics: From Protein Modifications and Networks to Proteomics. In: Wu, C.H.; Arighi, C.N.; Ross, K.E., Eds.; Humana Press: Protein Bioinformatics; New York, 2017; pp. 111-124.
Li, H.; Sze, K.H.; Lu, G.; Ballester, P.J. Machine-learning scoring functions for structure based virtual screening. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2021, 11(1), e1478.
Shen, C.; Ding, J.; Wang, Z.; Cao, D.; Ding, X.; Hou, T. From machine learning to deep learning: Advances in scoring functions for protein-ligand docking. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2020, 10(1), e1429.
Vázquez, J.; López, M.; Gibert, E.; Herrero, E.; Luque, F.J. Merging ligand based and structure based methods in drug discovery: An overview of combined virtual screening approaches. Molecules, 2020, 25(20), 4723.
[] [PMID: 33076254]
Drwal, M.N.; Griffith, R. Combination of ligand- and structure-based methods in virtual screening. Drug Discov. Today. Technol., 2013, 10(3), e395-e401.
[] [PMID: 24050136]
Vermeulen, K.; Vandamme, M.; Bormans, G.; Cleeren, F. Design and challenges of radiopharmaceuticals. Semin. Nucl. Med., 2019, 49(5), 339-356.
[] [PMID: 31470930]
Wiebe, L.I. Comparative evaluation of therapeutic radiopharmaceuticals. J. Nucl. Med., 2008, 49(11), 1900.
Tan, S.J.; Yan, Y.K.; Lee, P.P.F.; Lim, K.H. Copper, gold and silver compounds as potential new anti tumor metallodrugs. Future Med. Chem., 2010, 2(10), 1591-1608.
[] [PMID: 21426151]
Walker, J.D.; Newman, M.C.; Enache, M. Fundamental QSARs for Metal Ions; CRC Press: Boca Raton, 2019.
Riccardi, L.; Genna, V.; De Vivo, M. Metal-ligand interactions in drug design. Nat. Rev. Chem., 2018, 2(7), 100-112.
Palermo, G.; Magistrato, A.; Riedel, T.; von Erlach, T.; Davey, C.A.; Dyson, P.J.; Rothlisberger, U. Fighting cancer with transition metal complexes: From naked DNA to protein and chromatin targeting strategies. ChemMedChem, 2016, 11(12), 1199-1210.
[] [PMID: 26634638]
Palermo, G.; Spinello, A.; Saha, A.; Magistrato, A. Frontiers of metal-coordinating drug design. Expert Opin. Drug Discov., 2021, 16(5), 497-511.
Cho, A.E.; Rinaldo, D. Extension of QM/MM docking and its applications to metalloproteins. J. Comput. Chem., 2009, 30(16), 2609-2616.
[] [PMID: 19373896]
Unke, O.T.; Chmiela, S.; Sauceda, H.E.; Gastegger, M.; Poltavsky, I.; Schütt, K.T.; Tkatchenko, A.; Müller, K.R. Machine learning force fields. Chem. Rev., 2021, 121(16), 10142-10186.
[] [PMID: 33705118]
Botu, V.; Batra, R.; Chapman, J.; Ramprasad, R. Machine learning force fields: Construction, validation, and outlook. J. Phys. Chem. C, 2017, 121(1), 511-522.
Xu, P.; Guidez, E.B.; Bertoni, C.; Gordon, M.S. Perspective: Ab initio force field methods derived from quantum mechanics. J. Chem. Phys., 2018, 148(9), 090901.
Fracchia, F.; Del Frate, G.; Mancini, G.; Rocchia, W.; Barone, V. Force field parametrization of metal ions from statistical learning techniques. J. Chem. Theory Comput., 2018, 14(1), 255-273.
[] [PMID: 29112432]
Vassilev, G.V.; Fonseca, G.; Poltavsky, I.; Tkatchenko, A. Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules. J. Chem. Phys., 2021, 154(9), 094119.
[] [PMID: 33685131]
Coogan, M.P.; Dyson, P.J.; Bochmann, M. Introduction to the organometallics in biology and medicine issue. Organometallics, 2012, 31(16), 5671-5672.
Kulik, H.J. Making machine learning a useful tool in the accelerated discovery of transition metal complexes. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2020, 10(1), e1439.
Dral, P.O. Quantum chemistry in the age of machine learning. J. Phys. Chem. Lett., 2020, 11(6), 2336-2347.
[] [PMID: 32125858]
Smith, J.S.; Nebgen, B.; Mathew, N.; Chen, J.; Lubbers, N.; Burakovsky, L.; Tretiak, S.; Nam, H.A.; Germann, T.; Fensin, S.; Barros, K. Automated discovery of a robust interatomic potential for aluminum. Nat. Commun., 2021, 12(1), 1257.
[] [PMID: 33623036]
Czernin, J.; Sonni, I.; Razmaria, A.; Calais, J. The future of nuclear medicine as an independent specialty. J. Nucl. Med., 2019, 60(S2), 3S-12S.
[] [PMID: 31481589]
Langbein, T.; Weber, W.A.; Eiber, M. Future of theranostics: An outlook on precision oncology in nuclear medicine. J. Nucl. Med., 2019, 60(S2), 13S-19S.
[] [PMID: 31481583]
Gagnon, M.K.J.; Hausner, S.H.; Marik, J.; Abbey, C.K.; Marshall, J.F.; Sutcliffe, J.L. High-throughput in vivo screening of targeted molecular imaging agents. Proc. Natl. Acad. Sci. USA, 2009, 106(42), 17904-17909.
[] [PMID: 19815497]
Hu, L.Y.; Kelly, K.A.; Sutcliffe, J.L. High-throughput approaches to the development of molecular imaging agents. Mol. Imaging Biol., 2017, 19(2), 163-182.
[] [PMID: 27812924]

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