Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Mathematics in Computer Science
Published in: Mathematics in Computer Science 4/2021

16-06-2021

Mathematical Multidimensional Modelling and Structural Artificial Intelligence Pipelines Provide Insights for the Designing of Highly Specific AntiSARS-CoV2 Agents

Authors: Dimitrios Vlachakis, Panayiotis Vlamos

Published in: Mathematics in Computer Science | Issue 4/2021

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

COVID19 is the most impactful pandemic of recent times worldwide. It is a highly infectious disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus), To date there is specific drug nor vaccination against COVID19. Therefor the need for novel and pioneering anti-COVID19 is of paramount importance. In this direction, computer-aided drug design constitutes a very promising antiviral approach for the discovery and analysis of drugs and molecules with biological activity against SARS-CoV2. In silico modelling takes advantage of the massive amounts of biological and chemical data available on the nature of the interactions between the targeted systems and molecules, as well as the rapid progress of computational tools and software. Herein, we describe the potential of the merging of mathematical modelling, artificial intelligence and learning techniques into seamless computational pipelines for the rapid and efficient discovery and design of potent anti- SARS-CoV-2 modulators.
previous article Computing the Topology of Voronoï Diagrams of Parallel Half-Lines
next article On Column-Convex and Convex Carlitz Polyominoes

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Acharya, C., Coop, A., Polli, J.E., Mackerell Jr., A.D.: Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Curr. Comput. Aided Drug Des. 7(1), 10–22 (2011)
2.
Adhikari, S.P., Meng, S., Wu, Y.J., Mao, Y.P., Ye, R.X., Wang, Q.Z., Sun, C., Sylvia, S., Rozelle, S., Raat, H., Zhou, H.: Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review. Infect Dis. Poverty 9(1), 29 (2020)
3.
Akamatsu, M.: Current state and perspectives of 3D-QSAR. Curr. Top. Med. Chem. 2(12), 1381–1394 (2002)
4.
Allen, R., Moore, H.: Perspectives on the role of mathematics in drug discovery and development. Bull. Math. Biol. 81(9), 3425–3435 (2019)
5.
Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25(17), 3389–3402 (1997)
6.
Belouzard, S., Chu, V.C., Whittaker, G.R.: Activation of the SARS coronavirus spike protein via sequential proteolytic cleavage at two distinct sites. Proc. Natl. Acad. Sci. U S A 106(14), 5871–5876 (2009)
7.
Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The protein data bank. Nucleic Acids Res. 28(1), 235–242 (2000)
8.
Bosch, B.J., van der Zee, R., de Haan, C.A., Rottier, P.J.: The coronavirus spike protein is a class I virus fusion protein: structural and functional characterization of the fusion core complex. J. Virol. 77(16), 8801–8811 (2003)
9.
Burbidge, R., Trotter, M., Buxton, B., Holden, S.: Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput. Chem. 26(1), 5–14 (2001)
10.
Cao, C., Liu, F., Tan, H., Song, D., Shu, W., Li, W., Zhou, Y., Bo, X., Xie, Z.: Deep learning and its applications in biomedicine. Genomics Proteomics Bioinf. 16(1), 17–32 (2018)
11.
Chan-Yeung, M., Xu, R.H.: SARS: epidemiology. Respirology 8(Suppl), S9–14 (2003)
12.
Chan, H.C.S., Shan, H., Dahoun, T., Vogel, H., Yuan, S.: Advancing drug discovery via artificial intelligence. Trends Pharmacol. Sci. 40(8), 592–604 (2019)
13.
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., Blaschke, T.: The rise of deep learning in drug discovery. Drug Discov. Today 23(6), 1241–1250 (2018)
14.
Chen, L., Morrow, J.K., Tran, H.T., Phatak, S.S., Du-Cuny, L., Zhang, S.: From laptop to benchtop to bedside: structure-based drug design on protein targets. Curr. Pharm. Des. 18(9), 1217–1239 (2012)
15.
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., Xia, J.a, Yu, T., Zhang, X., Zhang, L.: Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395(10223), 507–513 (2020)
16.
Cheng, F., Kovács, I.A., Barabási, A.-L.: Network-based prediction of drug combinations. Nat. Commun. 10(1), 1197 (2019)
17.
Cheng, Z.J., Shan, J.: 2019 Novel coronavirus: where we are and what we know. Infection 48(2), 155–163 (2020)MathSciNet
18.
Colijn, C., Jones, N., Johnston, I.G., Yaliraki, S., Barahona, M.: Toward precision healthcare: context and mathematical challenges. Front. Physiol. 8, 136 (2017)
19.
Davenport, T., Kalakota, R.: The potential for artificial intelligence in healthcare. Future Healthc. J. 6(2), 94–98 (2019)
20.
Dias, R., Torkamani, A.: Artificial intelligence in clinical and genomic diagnostics. Genome Med. 11(1), 70 (2019)
21.
Friesner, R.A., Murphy, R.B., Repasky, M.P., Frye, L.L., Greenwood, J.R., Halgren, T.A., Sanschagrin, P.C., Mainz, D.T.: Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem. 49(21), 6177–6196 (2006)
22.
Fischer, H.P.: Mathematical modeling of complex biological systems: from parts lists to understanding systems behavior. Alcohol Res. Health : J. Natl. Inst. Alcohol Abuse Alcohol. 31(1), 49–59 (2008)
23.
Ginsburg, G.S., Phillips, K.A.: Precision medicine: from science to value. Health Aff. (Project Hope) 37(5), 694–701 (2018)
24.
Gobburu, J.V.S., Lesko, L.J.: Quantitative disease, drug, and trial models. Annu. Rev. Pharmacol. Toxicol. 49(1), 291–301 (2009)
25.
Holford, N.: Pharmacodynamic principles and the time course of immediate drug effects. Transl. Clin. Pharmacol. 25(4), 157–61 (2017)
26.
Huang, P., Liu, T., Huang, L., Liu, H., Lei, M., Xu, W., Hu, X., Chen, J., Liu, B.: Use of chest CT in combination with negative RT-PCR assay for the 2019 novel coronavirus but high clinical suspicion. Radiology 295(1), 22–23 (2020)
27.
Illergard, K., Ardell, D.H., Elofsson, A.: Structure is three to ten times more conserved than sequence-a study of structural response in protein cores. Proteins 77(3), 499–508 (2009)
28.
Jin, Y.H., Cai, L., Cheng, Z.S., Cheng, H., Deng, T., Fan, Y.P., Fang, C., Huang, D., Huang, L.Q., Huang, Q., Han, Y., Hu, B., Hu, F., Li, B.H., Li, Y.R., Liang, K., Lin, L.K., Luo, L.S., Ma, J., Ma, L.L., Peng, Z.Y., Pan, Y.B., Pan, Z.Y., Ren, X.Q., Sun, H.M., Wang, Y., Wang, Y.Y., Weng, H., Wei, C.J., Wu, D.F., Xia, J., Xiong, Y., Xu, H.B., Yao, X.M., Yuan, Y.F., Ye, T.S., Zhang, X.C., Zhang, Y.W., Zhang, Y.G., Zhang, H.M., Zhao, Y., Zhao, M.J., Zi, H., Zeng, X.T., Wang, Y.Y., Wang, X.H., FTZHOWUNC Management, E-BMCOCIE Research Team: M. Promotive association for and C. health: a rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version). Mil. Med. Res. 7(1), 4 (2020)
29.
Kuntz, I.D., Blaney, J.M., Oatley, S.J., Langridge, R., Ferrin, T.E.: A geometric approach to macromolecule-ligand interactions. J. Mol. Biol. 161(2), 269–288 (1982)
30.
Kurogi, Y., Guner, O.F.: Pharmacophore modeling and three-dimensional database searching for drug design using catalyst. Curr. Med. Chem. 8(9), 1035–1055 (2001)
31.
Lalonde, R.L., Kowalski, K.G., Hutmacher, M.M., Ewy, W., Nichols, D.J., Milligan, P.A., et al.: Model-based drug development. Clin. Pharmaco. Ther. 82(1), 21–32 (2007)
32.
Lavezzi, S.M., Borella, E., Carrara, L., De Nicolao, G., Magni, P., Poggesi, I.: Mathematical modeling of efficacy and safety for anticancer drugs clinical development. Expert Opin. Drug Discov. 13(1), 5–21 (2018)
33.
Leelananda, S.P., Lindert, S.: Computational methods in drug discovery. Beilstein J. Org. Chem. 12, 2694–2718 (2016)
34.
Li, W., Tang, Y., Liu, H., Cheng, J., Zhu, W., Jiang, H.: Probing ligand binding modes of human cytochrome P450 2J2 by homology modeling, molecular dynamics simulation, and flexible molecular docking. Proteins 71(2), 938–949 (2008)
35.
Li, Y., Kong, Y., Zhang, M., Yan, A., Liu, Z.: Using support vector machine (SVM) for classification of selectivity of H1N1 neuraminidase inhibitors. Mol. Inform. 35(3–4), 116–124 (2016)
36.
Lu, H.: Drug treatment options for the 2019-new coronavirus (2019-nCoV). Biosci. Trends 14(1), 69–71 (2020)
37.
Malathi, K., Ramaiah, S.: Bioinformatics approaches for new drug discovery: a review. Biotechnol. Genet. Eng. Rev. 34(2), 243–260 (2018)
38.
Maltarollo, V.G., Kronenberger, T., Espinoza, G.Z., Oliveira, P.R., Honorio, K.M.: Advances with support vector machines for novel drug discovery. Expert Opin. Drug Discov. 14(1), 23–33 (2019)
39.
40.
Marti-Renom, M.A., Stuart, A.C., Fiser, A., Sanchez, R., Melo, F., Sali, A.: Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct. 29, 291–325 (2000)
41.
Moore, H., Allen, R.: What can mathematics do for drug development? Bull. Math. Biol. 81(9), 3421–3424 (2019)MATH
42.
Nichols, J.A., Herbert Chan, H.W., Baker, M.A.B.: Machine learning: applications of artificial intelligence to imaging and diagnosis. Biophys. Rev. 11(1), 111–118 (2019)
43.
44.
Ou-Yang, S.S., Lu, J.Y., Kong, X.Q., Liang, Z.J., Luo, C., Jiang, H.: Computational drug discovery. Acta Pharmacol. Sin 33(9), 1131–1140 (2012)
45.
Ou, X., Liu, Y., Lei, X., Li, P., Mi, D., Ren, L., Guo, L., Guo, R., Chen, T., Hu, J., Xiang, Z., Mu, Z., Chen, X., Chen, J., Hu, K., Jin, Q., Wang, J., Qian, Z.: Characterization of spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV. Nat. Commun. 11(1), 1620 (2020)
46.
Pagadala, N.S., Syed, K., Tuszynski, J.: Software for molecular docking: a review. Biophys. Rev. 9(2), 91–102 (2017)
47.
Patel, K., Simpson, J.A., Batty, K.T., Zaloumis, S., Kirkpatrick, C.M.: Modelling the time course of antimalarial parasite killing: a tour of animal and human models, translation and challenges. Br. J. Clin. Pharmacol. 79(1), 97–107 (2015)
48.
Paul, S.M., Mytelka, D.S., Dunwiddie, C.T., Persinger, C.C., Munos, B.H., Lindborg, S.R., Schacht, A.L.: How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov. 9(3), 203–214 (2010)
49.
Pickett, B.E., Sadat, E.L., Zhang, Y., Noronha, J.M., Squires, R.B., Hunt, V., Liu, M., Kumar, S., Zaremba, S., Gu, Z., Zhou, L., Larson, C.N., Dietrich, J., Klem, E.B., Scheuermann, R.H.: ViPR: an open bioinformatics database and analysis resource for virology research. Nucleic Acids Res. 40, D593–598 (2012)
50.
Popova, M., Isayev, O., Tropsha, A.: Deep reinforcement learning for de novo drug design. Sci. Adv. 4(7), eaap7885 (2018)
51.
Pushpakom, S., Iorio, F., Eyers, P.A., Escott, K.J., Hopper, S., Wells, A., Doig, A., Guilliams, T., Latimer, J., McNamee, C., Norris, A., Sanseau, P., Cavalla, D., Pirmohamed, M.: Drug repurposing: progress, challenges and recommendations. Nat. Rev. Drug Discov. 18(1), 41–58 (2019)
52.
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 80(8), 2071–2079 (2012)
53.
Réda, C., Kaufmann, E., Delahaye-Duriez, A.: Machine learning applications in drug development. Comput. Struct. Biotechnol. J. 18, 241–252 (2020)
54.
Ristevski, B., Chen, M.: Big data analytics in medicine and healthcare. J. Integr. Bioinform. 15(3), 20170030 (2018)
55.
Rothan, H.A., Byrareddy, S.N.: The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J. Autoimmun. 109, 102433 (2020)
56.
Rothe, C., Schunk, M., Sothmann, P., Bretzel, G., Froeschl, G., Wallrauch, C., Zimmer, T., Thiel, V., Janke, C., Guggemos, W., Seilmaier, M., Drosten, C., Vollmar, P., Zwirglmaier, K., Zange, S., Wolfel, R., Hoelscher, M.: Transmission of 2019-nCoV infection from an asymptomatic contact in Germany. N. Engl. J. Med. 382(10), 970–971 (2020)
57.
Salahudeen, M.S., Nishtala, P.S.: An overview of pharmacodynamic modelling, ligand-binding approach and its application in clinical practice. Saudi Pharm. J. 25(2), 165–75 (2017)
58.
Schames, J.R., Henchman, R.H., Siegel, J.S., Sotriffer, C.A., Ni, H., McCammon, J.A.: Discovery of a novel binding trench in HIV integrase. J. Med. Chem. 47(8), 1879–1881 (2004)
59.
Schneider, G.: Automating drug discovery. Nat. Rev. Drug Discov. 17(2), 97–113 (2018)
60.
Schneider, P., Walters, W.P., Plowright, A.T., Sieroka, N., Listgarten, J., Goodnow, R.A., Fisher, J., Jansen, J.M., Duca, J.S., Rush, T.S., Zentgraf, M., Hill, J.E., Krutoholow, E., Kohler, M., Blaney, J., Funatsu, K., Luebkemann, C., Schneider, G.: Rethinking drug design in the artificial intelligence era. Nat. Rev. Drug Discov. 19(5), 353–364 (2020)
61.
Sethi, A., Joshi, K., Sasikala, K., Alvala, M.: Molecular docking in modern drug discovery: principles and recent applications. In: Gaitonde, V., Karmakar, P., Trivedi, A. (eds.) Drug Discovery and Development - New Advances. IntechOpen, London (2020)
62.
Shang, J., Ye, G., Shi, K., Wan, Y., Luo, C., Aihara, H., Geng, Q., Auerbach, A., Li, F.: Structural basis of receptor recognition by SARS-CoV-2. Nature 581(7807), 221–224 (2020)
63.
Singh, B., Mal, G., Gautam, S.K., Mukesh M.: Computer-aided drug discovery. Advances in Animal Biotechnology (2019)
64.
Singhal, T.: A review of coronavirus disease-2019 (COVID-19). Indian J. Pediatr. 87(4), 281–286 (2020)
65.
Slater, H.C., Okell, L.C., Ghani, A.C.: Mathematical modelling to guide drug development for malaria elimination. Trends Parasitol. 33(3), 175–184 (2017)
66.
Sliwoski, G., Kothiwale, S., Meiler, J., Lowe Jr., E.W.: Computational methods in drug discovery. Pharmacol. Rev. 66(1), 334–395 (2013)
67.
Smellie, A., Teig, S.L., Towbin, P.: Poling: promoting conformational variation. J. Comput. Chem. 16(2), 171–187 (1995)
68.
Sohrabi, C., Alsafi, Z., O’Neill, N., Khan, M., Kerwan, A., Al-Jabir, A., Iosifidis, C., Agha, R.: World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int. J. Surg. 76, 71–76 (2020)
69.
Sonawane, A.R., Weiss, S.T., Glass, K., Sharma, A.: Network medicine in the age of biomedical big data. Front. Genet. 10, 294 (2019)
70.
Tang, Y., Zhu, W., Chen, K., Jiang, H.: New technologies in computer-aided drug design: toward target identification and new chemical entity discovery. Drug Discov. Today Technol. 3(3), 307–313 (2006)
71.
Toots, M., Yoon, J.J., Cox, R.M., Hart, M., Sticher, Z.M., Makhsous, N., Plesker, R., Barrena, A.H., Reddy, P.G., Mitchell, D.G., Shean, R.C., Bluemling, G.R., Kolykhalov, A.A., Greninger, A.L., Natchus, M.G., Painter, G.R., Plemper, R.K.: Characterization of orally efficacious influenza drug with high resistance barrier in ferrets and human airway epithelia. Sci. Transl. Med. 11, 515 (2019)
72.
Usha, T., Shanmugarajan, D., Goyal, A.K., Kumar, C.S., Middha, S.K.: Recent updates on computer-aided drug discovery: time for a paradigm shift. Curr. Top Med. Chem. 17(30), 3296–3307 (2017)
73.
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. 18(6), 463–477 (2019)
74.
Verdonk, M.L., Chessari, G., Cole, J.C., Hartshorn, M.J., Murray, C.W., Nissink, J.W., Taylor, R.D., Taylor, R.: Modeling water molecules in protein-ligand docking using GOLD. J. Med. Chem. 48(20), 6504–6515 (2005)
75.
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. 8(18), 1555–1572 (2008)
76.
Wang, F., Liu, D., Wang, H., Luo, C., Zheng, M., Liu, H., Zhu, W., Luo, X., Zhang, J., Jiang, H.: Computational screening for active compounds targeting protein sequences: methodology and experimental validation. J. Chem. Inf. Model 51(11), 2821–2828 (2011)
77.
Wang, L., Gu, Q., Zheng, X., Ye, J., Liu, Z., Li, J., Hu, X., Hagler, A., Xu, J.: Discovery of new selective human aldose reductase inhibitors through virtual screening multiple binding pocket conformations. J. Chem. Inf. Model 53(9), 2409–2422 (2013)
78.
Wang, M., Cao, R., Zhang, L., Yang, X., Liu, J., Xu, M., Shi, Z., Hu, Z., Zhong, W., Xiao, G.: Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Res. 30(3), 269–271 (2020)
79.
Wang, Z., Deisboeck, T.S.: Mathematical modeling in cancer drug discovery. Drug Discov. Today 19(2), 145–150 (2014)
80.
81.
82.
Wolber, G., Langer, T.: LigandScout: 3-D Pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J. Chem. Inf. Model. 45(1), 160–169 (2005)
83.
Xiang, Z.: Advances in homology protein structure modeling. Curr. Protein Pept. Sci. 7(3), 217–227 (2006)
84.
Xu, H., Aldrich, M.C., Chen, Q., Liu, H., Peterson, N.B., Dai, Q., Levy, M., Shah, A., Han, X., Ruan, X., Jiang, M., Li, Y., Julien, J.S., Warner, J., Friedman, C., Roden, D.M., Denny, J.C.: Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality. J. Am. Med. Inf. Assoc. : JAMIA 22(1), 179–191 (2015)
85.
Xue, H., Li, J., Xie, H., Wang, Y.: Review of drug repositioning approaches and resources. Int. J. Biol. Sci. 14(10), 1232–1244 (2018)
86.
Yu, R.H., Cao, Y.X.: A method to determine pharmacokinetic parameters based on andante constant-rate intravenous infusion. Sci. Rep. 7(1), 13279 (2017)
87.
Yuki, K., Fujiogi, M., Koutsogiannaki, S.: COVID-19 pathophysiology: a review. Clin. Immunol. (Orlando, Fla.) 215, 108427 (2020)
88.
Zhang, Y., Geng, X., Tan, Y., Li, Q., Xu, C., Xu, J., Hao, L., Zeng, Z., Luo, X., Liu, F., Wang, H.: New understanding of the damage of SARS-CoV-2 infection outside the respiratory system. Biomed. Pharmacother. = Biomed. Pharmacother. 127, 110195 (2020)
89.
Zhao, H., Caflisch, A.: Molecular dynamics in drug design. Eur. J. Med. Chem. 91, 4–14 (2015)
90.
Zhu, H.: Big data and artificial intelligence modeling for drug discovery. Annu. Rev. Pharmacol. Toxicol. 60(1), 573–589 (2020)
Metadata
Title
Mathematical Multidimensional Modelling and Structural Artificial Intelligence Pipelines Provide Insights for the Designing of Highly Specific AntiSARS-CoV2 Agents
Authors
Dimitrios Vlachakis
Panayiotis Vlamos
Publication date
16-06-2021
Publisher
Springer International Publishing
Published in
Mathematics in Computer Science / Issue 4/2021
Print ISSN: 1661-8270
Electronic ISSN: 1661-8289
DOI
https://doi.org/10.1007/s11786-021-00517-0

Other articles of this Issue 4/2021

Mathematics in Computer Science 4/2021 Go to the issue

OriginalPaper

Phylogenetics of Indo-European Language Families via an Algebro-Geometric Analysis of Their Syntactic Structures

OriginalPaper

Two almost-circles, and two real ones

OriginalPaper

Compositional Properties of Alignments

OriginalPaper

Factorials Experiments, Covering Arrays, and Combinatorial Testing

OriginalPaper

Counting Lattice Points on Bargraphs of Catalan Words

OriginalPaper

High-Order Methods for Systems of Fractional Ordinary Differential Equations and Their Application to Time-Fractional Diffusion Equations

Premium Partner

    Image Credits