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Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Editorial

Nanoinformatics: Artificial Intelligence and Nanotechnology in the New Decade

Author(s): Antreas Afantitis

Volume 23, Issue 1, 2020

Page: [4 - 5] Pages: 2

DOI: 10.2174/138620732301200316112000

[1]
Mullard, A. How machine learning and big data are helping chemists search the vast chemical universe for better medicines. The drug-maker’s guide to the galaxy. Nature, 26 September . 2017.
[http://dx.doi.org/10.1038/549445a]
[2]
Chan, D.S-H.; Lee, H-M.; Yang, F.; Che, C-M.; Wong, C.C.L.; Abagyan, R.; Leung, C-H.; Ma, D-L. Structure-based discovery of natural-product-like TNF-α inhibitors. Angewandte Chemie (International Ed. in English)., 2010, 49(16), 2860-2864.
[http://dx.doi.org/10.1002/anie.200907360]
[3]
Leung, C-H.; Chan, D.S-H.; Kwan, M.H-T.; Cheng, Z.; Wong, C-Y.; Zhu, G-Y.; Fong, W-F.; Ma, D-L. Structure-based repurposing of FDA-approved drugs as TNF-α inhibitors. ChemMedChem, 2011, 6(5), 765-768.
[http://dx.doi.org/10.1002/cmdc.201100016]
[4]
Melagraki, G.; Ntougkos, E.; Rinotas, V.; Papaneophytou, C.; Leonis, G.; Mavromoustakos, T.; Kontopidis, G.; Douni, E.; Afantitis, A.; Kollias, G. Cheminformatics-aided discovery of small-molecule Protein-Protein Interaction (PPI) dual inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL). PLOS Comput. Biol., 2017, 13(4)e1005372
[http://dx.doi.org/10.1371/journal.pcbi.1005372]
[5]
Melagraki, G.; Ntougkos, E.; Papadopoulou, D.; Rinotas, V.; Leonis, G.; Douni, E. … Kollias, G. In silico discovery of plant-origin natural product inhibitors of tumor necrosis factor (TNF) and receptor activator of NF-κB ligand (RANKL). Front. Pharmacol., 2018, 9, 800.
[http://dx.doi.org/10.3389/fphar.2018.00800]
[6]
Fleming, N. Computer-calculated compounds. 557. Nature, S55(May 31, 2018). Available at ,. https://www.nature.com/magazine-assets/d41586-018-05267-x/d41586-018-05267-x.pdf
[7]
Freedman, D.H. Hunting for new drugs with AI. Nature, 2019, 576, S49-S53.
[http://dx.doi.org/10.1038/d41586-019-03846-0]
[8]
Afantitis, A.; Melagraki, G.; Tsoumanis, A.; Valsami-Jones, E.; Lynch, I. A nanoinformatics decision support tool for the virtual screening of gold nanoparticle cellular association using protein corona fingerprints. Nanotoxicology, 2018, 12(10), 1148-1165.
[http://dx.doi.org/10.1080/17435390.2018.1504998]
[9]
Gajewicz, A.; Puzyn, T.; Odziomek, K.; Urbaszek, P.; Haase, A.; Riebeling, C.; Luch, A.; Irfan, M.A.; Landsiedel, R.; van der Zande, M.; Bouwmeester, H. Decision tree models to classify nanomaterials according to the DF4nanoGrouping scheme. Nanotoxicology, 2018, 12(1), 1-17.
[http://dx.doi.org/10.1080/17435390.2017.1415388]
[10]
Marchese Robinson, R.L.; Lynch, I.; Peijnenburg, W.; Rumble, J.; Klaessig, F.; Marquardt, C.; Rauscher, H.; Puzyn, T.; Purian, R.; Åberg, C.; Karcher, S.; Vriens, H.; Hoet, P.; Hoover, M.D.; Hendren, C.O.; Harper, S.L. How should the completeness and quality of curated nanomaterial data be evaluated? Nanoscale, 2016, 8(19), 9919-9943.
[http://dx.doi.org/10.1039/C5NR08944A]
[11]
Oh, E.; Liu, R.; Nel, A.; Gemill, K.B.; Bilal, M.; Cohen, Y.; Medintz, I.L. Meta-analysis of cellular toxicity for cadmium-containing quantum dots. Nat. Nanotechnol., 2016, 11(5), 479-486.
[http://dx.doi.org/10.1038/nnano.2015.338]
[12]
Tämm, K.; Sikk, L.; Burk, J.; Rallo, R.; Pokhrel, S.; Mädler, L.; Scott-Fordsmand, J.J.; Burka, P.; Tamm, T. Parametrization of nanoparticles: development of full-particle nanodescriptors. Nanoscale, 2016, 8(36), 16243-16250.
[http://dx.doi.org/10.1039/C6NR04376C]
[13]
Varsou, D-D.; Afantitis, A.; Tsoumanis, A.; Melagraki, G.; Sarimveis, H.; Valsami-Jones, E.; Lynch, I. A safe-by-design tool for functionalised nanomaterials through the enalos nanoinformatics cloud platform. Nanoscale Advances, 2019, 1(2), 706-718.
[http://dx.doi.org/10.1039/C8NA00142A]
[14]
Kluender, E.J.; Hedrick, J.L.; Brown, K.A.; Rao, R.; Meckes, B.; Du, J.S.; Moreau, L.M.; Maruyama, B.; Mirkin, C.A. Catalyst discovery through megalibraries of nanomaterials. Proc. Natl. Acad. Sci. USA, 2019, 116(1), 40-45.
[http://dx.doi.org/10.1073/pnas.1815358116]
[15]
NanoCommons H2020 Project. (n.d.). Retrieved from ; www.nanocommons.eu NanoSolveIT H2020 Nanoinformatics Project. (n.d.). Retrieved January 29, ; 2020. Available from. (www.nanosolveit.eu)
[16]
Lopez, H.; Brandt, E.G.; Mirzoev, A.; Zhurkin, D.; Lyubartsev, A.; Lobaskin, V. Multiscale Modelling of Bionano Interface. In: Advances in Experimental Medicine and Biology., Tran, L.; Bañares, M.A.; Rallo, R.; Eds., . 2017, Vol. 947, pp. 173-206.
[http://dx.doi.org/10.1007/978-3-319-47754-1_7]

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