Artificial Intelligence Technologies for COVID-19 De Novo Drug Design

Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884 · see citing works

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412 · see citing works

WHO Declares COVID-19 a Pandemic.

Cucinotta, D., & Vanelli, M. (2020) · Acta bio-medica dell'Ateneo parmense/Acta biomedica · doi:10.23750/abm.v91i1.9397 · Cited by 7,644 · see citing works

ChEMBL: a large-scale bioactivity database for drug discovery

Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr777 · Cited by 4,382 · see citing works

Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling

Svetnik, V., Liaw, A. I., Tong, C., Culberson, J. C., Sheridan, R. P., & Feuston, B. P. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034160g · Cited by 3,587 · see citing works

Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors

Zhang, L., Lin, D., Sun, X., Curth, U., Drosten, C., Sauerhering, L., Becker, S., Rox, K., & Hilgenfeld, R. (2020) · Science · doi:10.1126/science.abb3405 · Cited by 3,275 · see citing works

ZINC 15 – Ligand Discovery for Everyone

Sterling, T., & Irwin, J. J. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00559 · Cited by 3,224 · see citing works

Human trust in artificial intelligence: Review of empirical research

Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921 · see citing works

A forkhead-domain gene is mutated in a severe speech and language disorder

Lai, C. S. L., Fisher, S. E., Hurst, J. A., Vargha‐Khadem, F., & Monaco, A. P. (2001) · Nature · doi:10.1038/35097076 · Cited by 2,053 · see citing works

Prediction of continuous B‐cell epitopes in an antigen using recurrent neural network

Saha, S., & Raghava, G. P. S. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21078 · Cited by 1,758 · see citing works

BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes

Jespersen, M. C., Peters, B., Nielsen, M., & Marcatili, P. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx346 · Cited by 1,739 · see citing works

Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018

Wouters, O. J., McKee, M., & Luyten, J. (2020) · JAMA · doi:10.1001/jama.2020.1166 · Cited by 1,531 · see citing works

GalaxyRefine: protein structure refinement driven by side-chain repacking

Heo, L., Park, H., & Seok, C. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt458 · Cited by 1,375 · see citing works

Parallel tempering: Theory, applications, and new perspectives

Earl, D. J., & Deem, M. W. (2005) · Physical Chemistry Chemical Physics · doi:10.1039/b509983h · Cited by 1,317 · see citing works

Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide

Emini, E. A., Hughes, J. V., Perlow, D. S., & Boger, J. (1985) · Journal of Virology · doi:10.1128/jvi.55.3.836-839.1985 · Cited by 1,252 · see citing works

Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise

Koes, D. R., Baumgartner, M. P., & Camacho, C. J. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci300604z · Cited by 1,150 · see citing works

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): An overview of viral structure and host response

Astuti, I., & Ysrafil, Y. (2020) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2020.04.020 · Cited by 1,067 · see citing works

Parallel Tempering: Theory, Applications, and New PerspectivesPreprint

Earl, D. J., & Deem, M. W. (2008) · Cited by 1,015 · see citing works

Rethinking drug design in the artificial intelligence era

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., et al. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0050-3 · Cited by 894 · see citing works

Multidimensional replica-exchange method for free-energy calculations

Sugita, Y., Kitao, A., & Okamoto, Y. (2000) · The Journal of Chemical Physics · doi:10.1063/1.1308516 · Cited by 808 · see citing works

Enhanced sampling techniques in molecular dynamics simulations of biological systems

Bernardi, R. C., Melo, M. C. R., & Schulten, K. (2015) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2014.10.019 · Cited by 757 · see citing works

Predicting linear B‐cell epitopes using string kernels

EL‐Manzalawy, Y., Dobbs, D. L., & Honavar, V. (2008) · Journal of Molecular Recognition · doi:10.1002/jmr.893 · Cited by 755 · see citing works

NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence

Nielsen, M., Lundegaard, C., Blicher, T. H., Lamberth, K., Harndahl, M. N., Justesen, S. F. L., Røder, G., Peters, B., Sette, A., Lund, O., et al. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000796 · Cited by 719 · see citing works

Fundamentals and Methods for T- and B-Cell Epitope Prediction

Sanchez‐Trincado, J. L., Gomez‐Perosanz, M., & Reche, P. A. (2017) · Journal of Immunology Research · doi:10.1155/2017/2680160 · Cited by 675 · see citing works

Coronavirus Spike Proteins in Viral Entry and Pathogenesis

Gallagher, T. M., & Buchmeier, M. J. (2001) · Virology · doi:10.1006/viro.2000.0757 · Cited by 658 · see citing works

A family of diverse Cul4-Ddb1-interacting proteins includes Cdt2, which is required for S phase destruction of the replication factor Cdt1

Jin, J., Arias, E. E., Chen, J., Harper, J. W., & Walter, J. C. (2006) · Molecular Cell · doi:10.1016/j.molcel.2006.08.010 · Cited by 632 · see citing works

Computer-Aided Drug Design Methods

Yu, W., & MacKerell, A. D. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6634-9_5 · Cited by 599 · see citing works

The SARS coronavirus nucleocapsid protein – Forms and functions

Chang, C., Hou, M., Chang, C., Hsiao, C., & Huang, T.-H. (2014) · Antiviral Research · doi:10.1016/j.antiviral.2013.12.009 · Cited by 522 · see citing works

Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds

Ton, A., Gentile, F., Hsing, M., Ban, F., & Cherkasov, A. R. (2020) · Molecular Informatics · doi:10.1002/minf.202000028 · Cited by 518 · see citing works

AllerTOP - a server for in silico prediction of allergens

Dimitrov, I., Flower, D. R., & Doytchinova, I. A. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s6-s4 · Cited by 517 · see citing works

Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data

Reynisson, B., Barra, C., Kaabinejadian, S., Hildebrand, W. H., Peters, B., & Nielsen, M. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00874 · Cited by 448 · see citing works

Exploring dengue genome to construct a multi-epitope based subunit vaccine by utilizing immunoinformatics approach to battle against dengue infection

Ali, M., Pandey, R. K., Khatoon, N., Narula, A., Mishra, A., & Prajapati, V. K. (2017) · Scientific Reports · doi:10.1038/s41598-017-09199-w · Cited by 388 · see citing works

SVMTriP: A Method to Predict Antigenic Epitopes Using Support Vector Machine to Integrate Tri-Peptide Similarity and Propensity

Yao, B., Zhang, L., Liang, S., & Zhang, C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045152 · Cited by 386 · see citing works

De novo molecular design and generative models

Meyers, J., Fabian, B., & Brown, N. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.05.019 · Cited by 300 · see citing works

Quantitative Predictions of Peptide Binding to Any HLA-DR Molecule of Known Sequence: NetMHCIIpan

Nielsen, M., Lundegaard, C., Blicher, T. H., Peters, B., Sette, A., Justesen, S. F. L., Buus, S., & Lund, O. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000107 · Cited by 293 · see citing works

MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm

Bai, Q., Tan, S., Xu, T., Liu, H., Huang, J., & Yao, X. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa161 · Cited by 291 · see citing works

An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study

Yang, Z., Bogdan, P., & Nazarian, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-81749-9 · Cited by 271 · see citing works

Cloning of a human phosphoinositide 3-kinase with a C2 domain that displays reduced sensitivity to the inhibitor wortmannin

Jan Domin, Y., Pagès, F., Volinia, S., Rittenhouse-Simmons, S., Zvelebil, M., Stein, R. C., & Waterfield, M. D. (1997) · Biochemical Journal · doi:10.1042/bj3260139 · Cited by 251 · see citing works

Tox21Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways as Mediated by Exposure to Environmental Chemicals and Drugs

Huang, R., Xia, M., Nguyễn, Ð.-T., Zhao, T., Sakamuru, S., Zhao, J., Shahane, S. A., Rossoshek, A., & Simeonov, A. M. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00085 · Cited by 229 · see citing works

Review of COVID-19 vaccine subtypes, efficacy and geographical distributions

Francis, A. I., Ghany, S., Gilkes, T., & Umakanthan, S. (2021) · Postgraduate Medical Journal · doi:10.1136/postgradmedj-2021-140654 · Cited by 216 · see citing works

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

Acharya, A., Agarwal, R., Baker, M. B., Baudry, J. Y., Bhowmik, D., Boehm, S., Byler, K., Chen, S., Coates, L., Cooper, C. J., et al. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01010 · Cited by 204 · see citing works

Clinical development times for innovative drugs

Brown, D. G., Wobst, H. J., Kapoor, A., Kenna, L. A., & Southall, N. T. (2021) · Nature Reviews Drug Discovery · doi:10.1038/d41573-021-00190-9 · Cited by 176 · see citing works

SARS-CoV-2 Mpro: A Potential Target for Peptidomimetics and Small-Molecule Inhibitors

Citarella, A., Scala, A., Piperno, A., & Micale, N. (2021) · Biomolecules · doi:10.3390/biom11040607 · Cited by 164 · see citing works

Challenges for Drug Repurposing in the COVID-19 Pandemic Era

Sultana, J., Crisafulli, S., Gabbay, F., Lynn, E., Shakir, S., & Trifirò, G. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.588654 · Cited by 147 · see citing works

SWEETLEAD: an In Silico Database of Approved Drugs, Regulated Chemicals, and Herbal Isolates for Computer-Aided Drug Discovery

Novick, P. A., Ortiz, O. F., Poelman, J., Abdulhay, A., & Pande, V. S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079568 · Cited by 129 · see citing works

Ranking Chemical Structures for Drug Discovery: A New Machine Learning Approach

Agarwal, S., Dugar, D., & Sengupta, S. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci9003865 · Cited by 128 · see citing works

How artificial intelligence may help the Covid‐19 pandemic: Pitfalls and lessons for the future

Malik, Y. S., Sircar, S., Bhat, S., Ansari, M. I., Pande, T., Kumar, P., Mathapati, B., Ganesh, B., Kaushik, R., Natesan, S., et al. (2020) · Reviews in Medical Virology · doi:10.1002/rmv.2205 · Cited by 124 · see citing works

<i>De Novo</i> Design of New Chemical Entities for SARS-CoV-2 Using Artificial Intelligence

Bung, N., Krishnan, S. R., Bulusu, G., & Roy, A. (2021) · Future Medicinal Chemistry · doi:10.4155/fmc-2020-0262 · Cited by 118 · see citing works

Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking

Mohapatra, S., Nath, P., Chatterjee, M., Das, N., Kalita, D., Roy, P., & Satapathi, S. (2020) · PLoS ONE · doi:10.1371/journal.pone.0241543 · Cited by 77 · see citing works

Predicting novel drugs for SARS-CoV-2 using machine learning from a >10 million chemical space

Kowalewski, J., & Ray, A. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e04639 · Cited by 76 · see citing works

The combination of artificial intelligence and systems biology for intelligent vaccine design

Russo, G., Reche, P. A., Pennisi, M. A., & Pappalardo, F. (2020) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2020.1791076 · Cited by 71 · see citing works

De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning

Santana, M. V., & Silva, F. P. (2021) · BMC Chemistry · doi:10.1186/s13065-021-00737-2 · Cited by 69 · see citing works

Radial Migration of Superficial Layer Cortical Neurons Controlled by Novel Ig Cell Adhesion Molecule MDGA1

Takeuchi, A., & O’Leary, D. D. M. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.4935-05.2006 · Cited by 61 · see citing works

Machine Learning Models Identify Inhibitors of SARS-CoV-2

Gawriljuk, V. O., Zin, P. P. K., Foil, D. H., Bernatchez, J., Beck, S., Beutler, N., Ricketts, J., Yang, L., Rogers, T., Puhl, A. C., et al. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00683 · Cited by 56 · see citing works

SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction

Verma, N., Qu, X., Trozzi, F., Elsaied, M., Karki, N., Tao, P., Zoltowski, B. D., Larson, E. C., & Kraka, E. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22031392 · Cited by 54 · see citing works

Cheminformatics-Based Identification of Potential Novel Anti-SARS-CoV-2 Natural Compounds of African Origin

Kwofie, S. K., Broni, E., Asiedu, S. O., Kwarko, G. B., Dankwa, B., Enninful, K. S., Tiburu, E. K., & Wilson, M. D. (2021) · Molecules · doi:10.3390/molecules26020406 · Cited by 49 · see citing works

Change in age distribution of COVID-19 deaths with the introduction of COVID-19 vaccination

Pastorino, R., Pezzullo, A. M., Villani, L., Causio, F. A., Axfors, C., Contopoulos‐Ioannidis, D. G., Boccia, S., & Ioannidis, J. P. A. (2021) · Environmental Research · doi:10.1016/j.envres.2021.112342 · Cited by 47 · see citing works

Pharmacophore Model for SARS-CoV-2 3CLpro Small-Molecule Inhibitors and in Vitro Experimental Validation of Computationally Screened Inhibitors

Glaab, E., Manoharan, G. B., & Abankwa, D. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00258 · Cited by 46 · see citing works

Predicting Potential SARS-COV-2 Drugs—In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking

Karki, N., Verma, N., Trozzi, F., Tao, P., Kraka, E., & Zoltowski, B. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22041573 · Cited by 43 · see citing works

Artificial Intelligence-Guided De Novo Molecular Design Targeting COVID-19

Srinivasan, S., Batra, R., Chan, H., Kamath, G., Cherukara, M. J., & Sankaranarayanan, S. K. R. S. (2021) · ACS Omega · doi:10.1021/acsomega.1c00477 · Cited by 42 · see citing works

3D-QSAR assisted identification of FABP4 inhibitors: An effective scaffold hopping analysis/QSAR evaluation

Floresta, G., Cilibrizzi, A., Abbate, V., Spampinato, A., Zagni, C., & Rescifina, A. (2018) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2018.11.045 · Cited by 41 · see citing works

Intranasal Vaccination against HIV-1 with Adenoviral Vector-Based Nanocomplex Using Synthetic TLR-4 Agonist Peptide as Adjuvant

Li, M., Jiang, Y., Gong, T., et al. (2016) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.5b00802 · Cited by 40 · see citing works

Discovery of SARS-CoV-2 main protease inhibitors using a synthesis-directed de novo design model

Morris, A., McCorkindale, W., The COVID Moonshot Consortium, Drayman, N., Chodera, J. D., Tay, S., London, N., & Lee, A. A. (2021) · Chemical Communications · doi:10.1039/d1cc00050k · Cited by 37 · see citing works

Discovery of High-Affinity Cannabinoid Receptors Ligands through a 3D-QSAR Ushered by Scaffold-Hopping Analysis

Floresta, G., Apirakkan, O., Rescifina, A., & Abbate, V. (2018) · Molecules · doi:10.3390/molecules23092183 · Cited by 36 · see citing works

A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition

Gaudêncio, S. P., & Pereira, F. (2020) · Marine Drugs · doi:10.3390/md18120633 · Cited by 35 · see citing works

A Structure- and Ligand-Based Virtual Screening of a Database of “Small” Marine Natural Products for the Identification of “Blue” Sigma-2 Receptor Ligands

Floresta, G., Amata, E., Barbaraci, C., et al. (2018) · Marine Drugs · doi:10.3390/md16100384 · Cited by 33 · see citing works

Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2

Born, J., Manica, M., Cadow, J., Markert, G., Mill, N. A., Filipavicius, M., Janakarajan, N., Cardinale, A., Laino, T., & Martínez, M. R. (2021) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abe808 · Cited by 33 · see citing works

Repurposing Therapeutics for COVID-19: Rapid Prediction of Commercially available drugs through Machine Learning and DockingPreprint

Mohapatra, S., Nath, P., Chatterjee, M., et al. (2020) · medRxiv · doi:10.1101/2020.04.05.20054254 · Cited by 31 · see citing works

Computational Tools in the Discovery of FABP4 Ligands: A Statistical and Molecular Modeling Approach

Floresta, G., Gentile, D., Perrini, G., et al. (2019) · Marine Drugs · doi:10.3390/md17110624 · Cited by 27 · see citing works

Targeting SARS‐CoV‐2 Spike Protein/ACE2 Protein‐Protein Interactions: a Computational Study

Pirolli, D., Righino, B., & Rosa, M. C. D. (2021) · Molecular Informatics · doi:10.1002/minf.202060080 · Cited by 27 · see citing works

Support Vector Machine as a Supervised Learning for the Prioritization of Novel Potential SARS-CoV-2 Main Protease Inhibitors

Mekni, N., Coronnello, C., Langer, T., et al. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22147714 · Cited by 27 · see citing works

Drug repurposing for COVID-19 based on an integrative meta-analysis of SARS-CoV-2 induced gene signature in human airway epithelium

Gupta, R. K., Nwachuku, E. L., Zusman, B. E., Jha, R. M., & Puccio, A. M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0257784 · Cited by 27 · see citing works

Prioritisation of Compounds for 3CLpro Inhibitor Development on SARS-CoV-2 Variants

Jukič, M., Škrlj, B., Tomšič, G., Pleško, S., Podlipnik, Č., & Bren, U. (2021) · Molecules · doi:10.3390/molecules26103003 · Cited by 26 · see citing works

A Systematic Review on the Contribution of Artificial Intelligence in the Development of Medicines for COVID-2019

Pires, C. M. B. F. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11090926 · Cited by 23 · see citing works

NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known

Nielsen, M., Lundegaard, C., Blicher, T. H., et al. (2007) · SciVee · doi:10.4016/4651.01 · Cited by 23 · see citing works

Machine learning vs. field 3D-QSAR models for serotonin 2A receptor psychoactive substances identification

Floresta, G., & Abbate, V. (2021) · RSC Advances · doi:10.1039/d1ra01335a · Cited by 16 · see citing works

Identification of Unique Peptides for SARS-CoV-2 Diagnostics and Vaccine Development by an In Silico Proteomics Approach

Kesarwani, V., Gupta, R., Vetukuri, R. R., Kushwaha, S. K., & Gandhi, S. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.725240 · Cited by 16 · see citing works

Black carp TAB1 up-regulates TAK1/IRF7/IFN signaling during the antiviral innate immune activation

Zou, Z., Xie, X., Li, W., et al. (2019) · Fish & Shellfish Immunology · doi:10.1016/j.fsi.2019.04.040 · Cited by 13 · see citing works

Novel Development of Predictive Feature Fingerprints to Identify Chemistry-Based Features for the Effective Drug Design of SARS-CoV-2 Target Antagonists and Inhibitors Using Machine Learning

Cooper, K., Baddeley, C., French, B., Gibson, K., Golden, J., Lee, T., Pierre, S., Weiss, B., & Yang, J. (2021) · ACS Omega · doi:10.1021/acsomega.0c05303 · Cited by 10 · see citing works

Active Learning and the Potential of Neural Networks Accelerate Molecular Screening for the Design of a New Molecule Effective against SARS‐CoV‐2

Rabhi, Y., Mrabet, M., & Fnaiech, F. (2021) · BioMed Research International · doi:10.1155/2021/6696012 · Cited by 9 · see citing works

The role of AI in drug discovery: challenges, opportunities, and strategies

Blanco-González, A., Cabezón, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., & Garcia-Fandino, R. (2023) · Pharmaceuticals · doi:10.3390/ph16060891 · Cited by 690 · see citing works

Big Data 4.0: The Era of Big Intelligence

Sun, Z. (2024) · Journal of Computer Science Research · doi:10.30564/jcsr.v6i1.6054 · Cited by 4 · see citing works

Big Data 4.0 = Meta4 (Big Data) = The Era of Big Intelligence

Sun, Z. (2024) · International Conference on Software Engineering and Information Management (ICSIM) · doi:10.1145/3647722.3647725 · Cited by 6 · see citing works

Big Data Analysis using Apache Hadoop and Spark

Sharmila, K., Somasundaram, K., Devi, R., et al. (2019) · International Journal of Recent Technology and Engineering (IJRTE) · doi:10.35940/ijrte.a2128.078219 · Cited by 5 · see citing works

Introduction to Bigdata and Relation with IoT

Deore, A., & . (2018) · International Journal of Engineering & Technology · doi:10.14419/ijet.v7i3.8.16851

A Service-Oriented Foundation for Big Data

Sun, Z. (2020) · International Journal of Systems and Service-Oriented Engineering · doi:10.4018/ijssoe.2020010101