Predicting Potential SARS-COV-2 Drugs—In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking
Open accessPublished version of a preprint
Abstract
Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. A concerted effort from research labs around the world resulted in the identification of potential pharmaceutical treatments for CoVID-19 using existing drugs, as well as the discovery of multiple vaccines. During an urgent crisis, rapidly identifying potential new treatments requires global and cross-discipline cooperation, together with an enhanced open-access research model to distribute new ideas and leads. Herein, we introduce an application of a deep neural network based drug screening method, validating it using a docking algorithm on approved drugs for drug repurposing efforts, and extending the screen to a large library of 750,000 compounds for de novo drug discovery effort. The results of large library screens are incorporated into an open-access web interface to allow researchers from diverse fields to target molecules of interest. Our combined approach allows for both the identification of existing drugs that may be able to be repurposed and de novo design of ACE2-regulatory compounds. Through these efforts we demonstrate the utility of a new machine learning algorithm for drug discovery, SSnet, that can function as a tool to triage large molecular libraries to identify classes of molecules with possible efficacy.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In some cases, AI-powered approaches have identified promising drug candidates for COVID-19 in a fraction of the time that traditional methods would take.
“In some cases, these AI-powered approaches have been able to identify promising drug candidates in a fraction of the time that it would take when using traditional methods [26,27,28,29,30,31].”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [29]
ML algorithms have been used to analyze large datasets of potential compounds and identify those with the most potential for treating COVID-19.
“ML algorithms have been used to analyze large datasets of potential compounds and identify those with the most potential for treating the virus.”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [29]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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
Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412 · see citing works
A pneumonia outbreak associated with a new coronavirus of probable bat origin
Zhou, P., Yang, X.-L., Wang, X.-G., Hu, B., Zhang, L., Zhang, W., Si, H.-R., Zhu, Y., Li, B., Huang, C.-L., et al. (2020) · Nature · doi:10.1038/s41586-020-2012-7 · Cited by 21,969 · see citing works
Hoffmann, M., Kleine-Weber, H., Schroeder, S., Krüger, N., Herrler, T., Erichsen, S., Schiergens, T. S., Herrler, G., Wu, N.-H., Nitsche, A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.02.052 · Cited by 20,286 · see citing works
Chan, J., Yuan, S.-F., Kok, K.-H., To, K., Chu, H., Yang, J., Xing, F., Liu, J.-L., Yip, C., Poon, R., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30154-9 · Cited by 8,450 · see citing works
Remdesivir for the Treatment of Covid-19 — Final Report
Beigel, J. H., Tomashek, K. M., Dodd, L. E., Mehta, A. K., Zingman, B. S., Kalil, A. C., Hohmann, E., Chu, H. Y., Luetkemeyer, A., Kline, S., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2007764 · Cited by 7,267 · see citing works
A Trial of Lopinavir–Ritonavir in Adults Hospitalized with Severe Covid-19
Cao, B., Wang, Y., Wen, D., Liu, W., Wang, J., Fan, G., Ruan, L., Song, B., Cai, Y., Wei, M., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001282 · Cited by 5,187 · see citing works
Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2
Yan, R., Zhang, Y., Li, Y., Xia, L., Guo, Y., & Zhou, Q. (2020) · Science · doi:10.1126/science.abb2762 · Cited by 4,950 · see citing works
Superspreading and the effect of individual variation on disease emergence
Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E., & Getz, W. M. (2005) · Nature · doi:10.1038/nature04153 · Cited by 2,514 · see citing works
The FDA-approved drug ivermectin inhibits the replication of SARS-CoV-2 in vitro.
Caly, L., Druce, J. D., Catton, M. G., Jans, D. A., & Wagstaff, K. M. (2020) · Antiviral Research · doi:10.1016/j.antiviral.2020.104787 · Cited by 2,179 · see citing works
Antibiotics and Bacterial Resistance in the 21st Century
Fair, R. J., & Tor, Y. (2014) · Perspectives in Medicinal Chemistry · doi:10.4137/pmc.s14459 · Cited by 1,979 · see citing works
Gilson, M. K., Liu, T., Baitaluk, M., Nicola, G., Hwang, L., & Chong, J. P. C. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1072 · Cited by 1,632 · 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
Heurich, A., Hofmann-Winkler, H., Gierer, S., Liepold, T., Jahn, O., & Pöhlmann, S. (2013) · Journal of Virology · doi:10.1128/jvi.02202-13 · Cited by 882 · see citing works
Protein–Ligand Scoring with Convolutional Neural Networks
Ragoza, M., Hochuli, J. E., Idrobo, E., Sunseri, J., & Koes, D. R. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00740 · Cited by 835 · see citing works
Discovery of SARS-CoV-2 antiviral drugs through large-scale compound repurposing
Riva, L., Yuan, S., Yin, X., Martin‐Sancho, L., Matsunaga, N., Pache, L., Burgstaller-Muehlbacher, S., Jesus, P. D. D., Teriete, P., Hull, M. V., et al. (2020) · Nature · doi:10.1038/s41586-020-2577-1 · Cited by 821 · see citing works
Tsubaki, M., Tomii, K., & Sese, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty535 · Cited by 688 · see citing works
Key Topics in Molecular Docking for Drug Design
Torres, P. H. M., Sodero, A. C. R., Jofily, P., & Silva, F. P. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184574 · Cited by 670 · 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
Sex Hormones Determine Immune Response
Taneja, V. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.01931 · Cited by 584 · see citing works
Tectonic conformational changes of a coronavirus spike glycoprotein promote membrane fusion
Walls, A. C., Tortorici, M. A., Snijder, J., Xiong, X., Bosch, B., Rey, F. A., & Veesler, D. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1708727114 · Cited by 539 · see citing works
ACE2: from vasopeptidase to SARS virus receptor
Turner, A. J., Hiscox, J. A., & Hooper, N. M. (2004) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2004.04.001 · Cited by 517 · see citing works
Simmons, G., Zmora, P., Gierer, S., Heurich, A., & Pöhlmann, S. (2013) · Antiviral Research · doi:10.1016/j.antiviral.2013.09.028 · Cited by 413 · see citing works
Breban, R., Riou, J., & Fontanet, A. L. (2013) · The Lancet · doi:10.1016/s0140-6736(13)61492-0 · Cited by 390 · see citing works
Open-source platform to benchmark fingerprints for ligand-based virtual screening
Riniker, S., & Landrum, G. A. (2013) · Journal of Cheminformatics · doi:10.1186/1758-2946-5-26 · Cited by 363 · see citing works
Estradiol, Progesterone, Immunomodulation, and COVID-19 Outcomes
Mauvais‐Jarvis, F., Klein, S. L., & Levin, E. R. (2020) · Endocrinology · doi:10.1210/endocr/bqaa127 · Cited by 271 · see citing works
Summa, V., Ludmerer, S. W., McCauley, J. A., Fandozzi, C. M., Burlein, C., Giuliano, C., Coleman, P. J., DiMuzio, J. M., Ferrara, M., Filippo, M. D., et al. (2012) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.00324-12 · Cited by 260 · see citing works
The role of superspreading in Middle East respiratory syndrome coronavirus (MERS-CoV) transmission
Kucharski, A. J., & Althaus, C. L. (2015) · Eurosurveillance · doi:10.2807/1560-7917.es2015.20.25.21167 · Cited by 249 · see citing works
Growth Factor Receptor Signaling Inhibition Prevents SARS-CoV-2 Replication
Klann, K., Bojková, D., Tascher, G., Ciesek, S., Münch, C., & Cinatl, J. J. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.08.006 · Cited by 237 · see citing works
Choudhary, S., Malik, Y. S., & Tomar, S. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01664 · Cited by 231 · see citing works
Wambier, C. G., & Goren, A. (2020) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2020.04.032 · Cited by 226 · see citing works
Pilon, A. C., Valli, M., Dametto, A. C., Pinto, M. E. F., Freire, R. T., Castro‐Gamboa, I., Andricopulo, A. D., & Bolzani, V. D. S. (2017) · Scientific Reports · doi:10.1038/s41598-017-07451-x · Cited by 222 · see citing works
Smith, M. D., & Smith, J. C. (2020) · ChemRxiv · doi:10.26434/chemrxiv.11871402.v4 · Cited by 208 · see citing works
Antivirals for COVID-19: A critical review
Frediansyah, A., Tiwari, R., Sharun, K., Dhama, K., & Harapan, H. (2020) · Clinical Epidemiology and Global Health · doi:10.1016/j.cegh.2020.07.006 · Cited by 185 · see citing works
Report 2: Estimating the potential total number of novel Coronavirus cases in Wuhan City, China
Imai, N., Dorigatti, I., Cori, A., Donnelly, C. A., Riley, S., & Ferguson, N. M. (2020) · Spiral (Imperial College London) · doi:10.25561/77150 · Cited by 184 · see citing works
Oldstone, M. B. A. (2020) · doi:10.1093/oso/9780190056780.001.0001 · Cited by 172 · see citing works
Commentary: Testosterone, a key hormone in the context of COVID-19 pandemic
Pozzilli, P., & Lenzi, A. (2020) · Metabolism · doi:10.1016/j.metabol.2020.154252 · Cited by 162 · see citing works
Qu, X., Hao, P., Song, X., Jiang, S., Liu, Y., Wang, P., Rao, X., Song, H., Wang, S., Zuo, Y., et al. (2005) · Journal of Biological Chemistry · doi:10.1074/jbc.m500662200 · Cited by 159 · see citing works
Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening
Vieira, T. F., & Sousa, S. F. (2019) · Applied Sciences · doi:10.3390/app9214538 · Cited by 145 · see citing works
Smith, M. D., & Smith, J. C. (2020) · ChemRxiv · doi:10.26434/chemrxiv.11871402 · Cited by 126 · see citing works
SANCDB: a South African natural compound database
Hatherley, R., Brown, D. K., Musyoka, T. M., Penkler, D. L., Faya, N., Lobb, K. A., & Bishop, Ö. T. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0080-8 · Cited by 125 · see citing works
ACE2 Cell Biology, Regulation, and Physiological Functions
Turner, A. J. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-801364-9.00025-0 · Cited by 125 · see citing works
Ding, T., Zhang, J., Wang, T., Cui, P., Chen, Z., Jiang, J., Zhou, S., Dai, J., Wang, B., Yuan, S., et al. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa1022 · Cited by 124 · see citing works
The flexibility of ACE2 in the context of SARS-CoV-2 infection
de Barros, E. P., Casalino, L., Gaieb, Z., Dommer, A. C., Wang, Y., Fallon, L., Raguette, L. E., Belfon, K. A. A., Simmerling, C. L., & Amaro, R. E. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2020.10.036 · Cited by 118 · see citing works
An Insight into the Sex Differences in COVID-19 Patients: What are the Possible Causes?
Dana, P. M., Sadoughi, F., Hallajzadeh, J., Asemi, Z., Mansournia, M. A., Yousefi, B., & Momen–Heravi, M. (2020) · Prehospital and Disaster Medicine · doi:10.1017/s1049023x20000837 · Cited by 115 · see citing works
Antimalarial drugs inhibit the replication of SARS-CoV-2: An in vitro evaluation
Gendrot, M., Andréani, J., Boxberger, M., Jardot, P., Fonta, I., Bideau, M. L., Duflot, I., Mosnier, J., Rolland, C., Bogreau, H., et al. (2020) · Travel Medicine and Infectious Disease · doi:10.1016/j.tmaid.2020.101873 · Cited by 105 · see citing works
Molecular mechanisms of sex bias differences in COVID-19 mortality
Li, Y., Jerkić, M., Slutsky, A. S., & Zhang, H. (2020) · Critical Care · doi:10.1186/s13054-020-03118-8 · Cited by 104 · see citing works
Sex‐related differences in COVID‐19 lethality
Penna, C. R. R., Mercurio, V., Tocchetti, C. G., & Pagliaro, P. (2020) · British Journal of Pharmacology · doi:10.1111/bph.15207 · Cited by 100 · see citing works
Ng, T. I., Tripathi, R. L., Reisch, T., Lu, L., Middleton, T. L., Hopkins, T. A., Pithawalla, R. B., Irvin, M., Dekhtyar, T., Krishnan, P., et al. (2017) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.01620-17 · Cited by 91 · see citing works
Influenza Transmission in Households During the 1918 Pandemic
Fraser, C., Cummings, D. A. T., Klinkenberg, D., Burke, D. S., & Ferguson, N. M. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwr122 · Cited by 87 · see citing works
The interaction of animal cytoplasmic RNA viruses with the nucleus to facilitate replication
Hiscox, J. A. (2003) · Virus Research · doi:10.1016/s0168-1702(03)00160-6 · Cited by 80 · see citing works
Sex Hormones and Hormone Therapy during COVID-19 Pandemic: Implications for Patients with Cancer
Cattrini, C., Bersanelli, M., Latocca, M. M., Conte, B., Vallome, G., & Boccardo, F. (2020) · Cancers · doi:10.3390/cancers12082325 · Cited by 75 · see citing works
Comprehensive assessment of flexible-ligand docking algorithms: current effectiveness and challenges
Huang, S. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx030 · Cited by 74 · see citing works
Middle East Respiratory Syndrome Coronavirus Superspreading Event Involving 81 Persons, Korea 2015
Oh, M., Choe, P. G., Oh, H. S., Park, W. B., Lee, S., Park, J., Lee, S. K., Song, J.-S., & Kim, N. J. (2015) · Journal of Korean Medical Science · doi:10.3346/jkms.2015.30.11.1701 · Cited by 72 · see citing works
Francés‐Monerris, A., Hognon, C., Miclot, T., García‐Iriepa, C., Iriepa, I., Terenzi, A., Grandemange, S., Barone, G., Marazzi, M., & Monari, A. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00779 · Cited by 70 · see citing works
Prediction of venetoclax activity in precursor B-ALL by functional assessment of apoptosis signaling
Seyfried, F., Demir, S., Hörl, R. L., Stirnweiß, F. U., Ryan, J. A., Scheffold, A., Villalobos-Ortiz, M., Boldrin, E., Zinngrebe, J., Enzenmüller, S., et al. (2019) · Cell Death and Disease · doi:10.1038/s41419-019-1801-0 · Cited by 64 · see citing works
Teralı, K., Baddal, B., & Gülcan, H. O. (2020) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2020.107697 · Cited by 63 · see citing works
Kalidhindi, R. S. R., Borkar, N. A., Ambhore, N. S., Pabelick, C. M., Prakash, Y. S., & Sathish, V. (2020) · American Journal of Physiology-Lung Cellular and Molecular Physiology · doi:10.1152/ajplung.00391.2020 · Cited by 63 · see citing works
Are sex discordant outcomes in COVID-19 related to sex hormones?
Strope, J. D., Chau, C. H., & Figg, W. D. (2020) · Seminars in Oncology · doi:10.1053/j.seminoncol.2020.06.002 · Cited by 55 · 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
Self-citationACE Inhibition and Bradykinin-Mediated Renal Vascular Responses
Imig, J. D. (2004) · Hypertension · doi:10.1161/01.hyp.0000118054.86193.ce · Cited by 49 · see citing works
Galimberti, S., Petrini, M., Baratè, C., Ricci, F., Balducci, S., Grassi, S., Guerrini, F., Ciabatti, E., Mechelli, S., Paolo, A. D., et al. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01428 · Cited by 48 · see citing works
COVID-19 among fit patients with CLL treated with venetoclax-based combinations
Fürstenau, M., Langerbeins, P., Silva, N. D., Fink, A. M., Robrecht, S., von Tresckow, J., Simon, F., Hohloch, K., Droogendijk, J., van der Klift, M., et al. (2020) · Leukemia · doi:10.1038/s41375-020-0941-7 · Cited by 44 · see citing works
Cowling, B. J., Ho, L., & Leung, G. M. (2007) · Epidemiology and Infection · doi:10.1017/s0950268807008722 · Cited by 43 · see citing works
The flexibility of ACE2 in the context of SARS-CoV-2 infectionPreprint
Barros, E. P., Casalino, L., Gaieb, Z., et al. (2020) · bioRxiv · doi:10.1101/2020.09.16.300459 · Cited by 35 · see citing works
Aberth, J. (2011) · Rowman & Littlefield Publishers eBooks · doi:10.5771/9781442207967 · Cited by 30 · see citing works
Lovetrue, B. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2020.110180 · Cited by 29 · see citing works
Emerging role of artificial intelligence in therapeutics for COVID-19: a systematic review
Kaushal, K., Sarma, P., Rana, S., Medhi, B., & Naithani, M. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1855250 · Cited by 29 · see citing works
Nelfinavir Is Active Against SARS-CoV-2 in Vero E6 CellsPreprint
Xu, Z., Yao, H., Shen, J., Wu, N., Xu, Y., Lu, X., Zhu, W., & Li, L. (2020) · ChemRxiv · doi:10.26434/chemrxiv.12039888.v1 · Cited by 29 · see citing works
Mancilla-Galindo, J., García-Méndez, J., Márquez-Sánchez, J., Reyes-Casarrubias, R. E., Aguirre-Aguilar, E., Rocha-González, H. I., & Kammar‐García, A. (2020) · medRxiv · doi:10.1101/2020.10.13.20211797 · Cited by 27 · see citing works
Mancilla-Galindo, J., García-Méndez, J., Márquez-Sánchez, J., Reyes-Casarrubias, R. E., Aguirre-Aguilar, E., Rocha-González, H. I., & Kammar‐García, A. (2021) · PubMed Central · doi:10.17179/excli2021-3413 · Cited by 21 · see citing works
Nelfinavir Is Active Against SARS-CoV-2 in Vero E6 CellsPreprint
Xu, Z., Yao, H., Shen, J., Wu, N., Xu, Y., Lu, X., Zhu, W., & Li, L. (2020) · ChemRxiv · doi:10.26434/chemrxiv.12039888 · Cited by 18 · see citing works
Guo, Y. H., Zeng, J., Li, Q., Li, P., Luo, F., Zhang, W., Lu, Y., Wáng, Q., Zhang, W. J., Zeng, Z.-P., et al. (2020) · PubMed · doi:10.3760/cma.j.cn112138-20200328-00310 · Cited by 6 · see citing works
Drug Repurposing Study Pinpoints Potential COVID-19 Antivirals
Abbasi, J. (2020) · JAMA · doi:10.1001/jama.2020.15948 · Cited by 5 · see citing works
Flagged first, then by citations
1 of the 75 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- [29]Verma N. Qu X. Trozzi F. Elsaied M. Tao Y. Larson E.C. Kraka E. SSnet-Secondary Structure based End-to-End Learning model for Protein-Ligand Interaction Prediction bioRxiv 2020 10.1101/2019.12.20.884841 · DOI
- [35]Wallach I. Dzamba M. Heifets A. AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery arXiv 2015 1510.02855
Cited by
Sorted by citations
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
Di Pierro, F., Derosa, G., Maffioli, P., Bertuccioli, A., Togni, S., Riva, A., Allegrini, P., Khan, A., Khan, S., Khan, B. A., et al. (2021) · International Journal of General Medicine · doi:10.2147/ijgm.s318720 · Cited by 182 · see citing works
Tribulations and future opportunities for artificial intelligence in precision medicine
Carini, C., & Seyhan, A. A. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05067-0 · Cited by 175 · see citing works
Lv, H., Shi, L., Berkenpas, J. W., Dao, F.-Y., Zulfiqar, H., Ding, H., Zhang, Y., Yang, L., & Cao, R. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab320 · Cited by 107 · see citing works
Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2
Gao, K., Wang, R., Chen, J., Cheng, L., Frishcosy, J., Huzumi, Y., Qiu, Y., Schluckbier, T., Wei, X., & Wei, G. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00965 · Cited by 91 · see citing works
The Potential Applications and Challenges of ChatGPT in the Medical Field
Mu, Y.-L., & He, D. (2024) · International Journal of General Medicine · doi:10.2147/ijgm.s456659 · Cited by 67 · see citing works
Francés‐Monerris, A., Hognon, C., Miclot, T., García‐Iriepa, C., Iriepa, I., Terenzi, A., Grandemange, S., Barone, G., Marazzi, M., & Monari, A. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00779 · Cited by 70 · see citing works
Artificial Intelligence Technologies for COVID-19 De Novo Drug Design
Floresta, G., Zagni, C., Gentile, D., Patamia, V., & Rescifina, A. (2022) · doi:10.20944/preprints202203.0032.v1 · Cited by 67 · see citing works
Pirolli, D., Righino, B., Camponeschi, C., Ria, F., Sante, G. D., & Rosa, M. C. D. (2023) · Scientific Reports · doi:10.1038/s41598-023-28716-8 · Cited by 70 · see citing works
SANCDB: an update on South African natural compounds and their readily available analogs
Diallo, B. N., Glenister, M., Musyoka, T. M., Lobb, K. A., & Bishop, Ö. T. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00514-2 · Cited by 59 · see citing works
Innovative applications of artificial intelligence in zoonotic disease management
Guo, W., Lv, C., Guo, M., Zhao, Q. C., Yin, X., & Zhang, L. (2023) · Science in One Health · doi:10.1016/j.soh.2023.100045 · Cited by 64 · see citing works
Ma, L.-L., Liu, H.-M., Liu, X., Yuan, X., Xu, C., Wang, F., Lin, J., Xu, R., & Zhang, D. (2021) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2021.113857 · Cited by 42 · see citing works
Machine learning and protein allostery
Xiao, S., Verkhivker, G. M., & Tao, P. (2022) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.12.001 · Cited by 34 · see citing works
Self-citationArtificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond
McNair, D. (2022) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-051921-023255 · Cited by 32 · see citing works
Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2
Sun, Y., Jiao, Y., Shi, C., & Zhang, Y. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.09.002 · Cited by 31 · see citing works
Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery
Kwofie, S. K., Adams, J., Broni, E., Enninful, K. S., Agoni, C., Soliman, M. E. S., & Wilson, M. D. (2023) · Pharmaceuticals · doi:10.3390/ph16030332 · Cited by 29 · see citing works
Emerging role of artificial intelligence in therapeutics for COVID-19: a systematic review
Kaushal, K., Sarma, P., Rana, S., Medhi, B., & Naithani, M. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1855250 · Cited by 29 · see citing works
Machine learning prediction of 3CL SARS-CoV-2 docking scores
Bučinský, L., Bortňák, D., Gall, M., Matúška, J., Milata, V., Pitoňák, M., Štekláč, M., Végh, D., & Zajaček, D. (2022) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2022.107656 · Cited by 19 · see citing works
Strategies to identify candidate repurposable drugs: COVID-19 treatment as a case example
Imami, A. S., McCullumsmith, R. E., & O'Donovan, S. M. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01724-w · Cited by 15 · see citing works
AI's role in pharmaceuticals: Assisting drug design from protein interactions to drug development
Bechelli, S., & Delhommelle, J. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100038 · Cited by 14 · see citing works
Nedyalkova, M., Vasighi, M., Sappati, S., Kumar, A., Madurga, S., & Simeonov, V. (2021) · Pharmaceuticals · doi:10.3390/ph14121328 · Cited by 12 · see citing works
A review of SARS-CoV-2 drug repurposing: databases and machine learning models
Elkashlan, M., Ahmad, R. M., Hajar, M., Jasmi, F. A., Corchado, J. M., Nasarudin, N. A., & Mohamad, M. S. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1182465 · Cited by 14 · see citing works
Lessons learnt from broad-spectrum coronavirus antiviral drug discovery
Bolinger, A. A., Li, J., Xie, X., Li, H., & Zhou, J. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2385598 · Cited by 10 · see citing works
Srinivas, R., Verma, N., Kraka, E., & Larson, E. C. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01355 · Cited by 9 · see citing works
Self-citationA multilevel approach for screening natural compounds as an antiviral agent for COVID-19
Vasighi, M., Romanova, J., & Nedyalkova, M. (2022) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2022.107694 · Cited by 9 · see citing works
Khang, L. T. P., Dinh‐Hung, N., Islam, S. I., Dwinanti, S. H., Mwamburi, S. M., Permpoonpattana, P., & Linh, N. V. (2025) · Journal of Fish Diseases · doi:10.1111/jfd.14120 · Cited by 8 · see citing works
Machine Learning Approaches to Improve Prediction of Target‐Drug Interactions
Balatti, G. E., Barletta, P. G., D. Perez, A., Giudicessi, S. L., & Martínez‐Ceron, M. C. (2022) · Wiley eBooks · doi:10.1002/9781394167258.ch2 · Cited by 9 · see citing works
Allosteric control of ACE2 peptidase domain dynamics
Trozzi, F., Karki, N., Song, Z., Verma, N., Kraka, E., Zoltowski, B. D., & Tao, P. (2022) · Organic & Biomolecular Chemistry · doi:10.1039/d2ob00606e · Cited by 7 · see citing works
Self-citationCan artificial intelligence transform antiviral drug discovery?
Tirosyan, I., Gabrielyan, Y., Petrosyan, V., Vignuzzi, M., & Zakaryan, H. S. (2026) · Drug Discovery Today · doi:10.1016/j.drudis.2026.104648 · Cited by 6 · see citing works
Artificial intelligence and machine learning responses to COVID-19 related inquiries
Zaeri, N. (2023) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2024.2321846 · Cited by 6 · see citing works
Advances and critical assessment of machine learning techniques for prediction of docking scores
Bučinský, L., Gall, M., Matúška, J., Pitoňák, M., & Štekláč, M. (2023) · International Journal of Quantum Chemistry · doi:10.1002/qua.27110 · Cited by 6 · see citing works
Harnessing Deep Learning for Omics in an Era of COVID-19
Jahanyar, B., Tabatabaee, H., & Rowhanimanesh, A. (2023) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2022.0155 · Cited by 4 · see citing works
COVID-19 Drug Repositioning: Present Status and Prospects
Mohanty, C., Vinod, C., Acharya, S., & Mahapatra, N. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-72834-2_19 · Cited by 3 · see citing works
SchNetPack Hyperparameter Optimization for a More Reliable Top Docking Scores Prediction
Matúška, J., Bučinský, L., Gall, M., Pitoňák, M., & Štekláč, M. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c00296 · Cited by 2 · see citing works
Vashishth, T. K., Sharma, V., Sharma, K. K., Vidyant, S., & Bhardwaj, A. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-3212-2.ch012 · Cited by 2 · see citing works
The Integration of Artificial Intelligence in Drug Discovery and Development : Novel Approach
Ujjwal, A. (2024) · International Journal of Scientific Research in Science and Technology · doi:10.32628/ijsrst24116175 · Cited by 2 · see citing works
Deep Learning based analysis of Covid-19 mortality risk
Iqba, T., & Qureshi, S. (2022) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/120101 · Cited by 1 · see citing works
Advancing Antiviral Design: Integrating Natural Products, Computation and Targeted Delivery
Lanrewaju, A. A., Enitan, A. M., Sabiu, S., & Swalaha, F. M. (2026) · Chemical Biology & Drug Design · doi:10.1111/cbdd.70365 · Cited by 2 · see citing works
Thangaraju, P., Y, S. S. T., Velmurugan, H., Thangaraju, E., Chirumamilla, K., Chakrabarti, T., & Chakrabarti, P. (2023) · Coronaviruses · doi:10.2174/2666796704666230908124854 · Cited by 1 · see citing works
Huang, Y., Luo, R., Tian, C., Zu, D., Yang, J., Chen, W., Huang, D., Duan, S., Yan, S., Yuan, Y., et al. (2024) · Phytochemical Analysis · doi:10.1002/pca.3467 · Cited by 1 · see citing works
Fucic, A., Mantovani, A., & ten Tusscher, G. W. (2021) · Medicina · doi:10.3390/medicina57040382 · Cited by 1 · see citing works
Using Deep Learning to tackle Covid-19
Iqbal, T., & Qureshi, S. (2021)
Khoshroudi, S. H. N., Safaei, A. A., & Soleimanjahi, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-05746-y
- Authors
- Karki, N., Verma, N., Trozzi, F., Tao, P., Kraka, E., & Zoltowski, B.
- Affiliations
- Southern Methodist University (US)
- Corresponding author
- Zoltowski, Brian
- Venue
- International Journal of Molecular Sciences
- Publisher
- MDPI AG
- Published
- 2021-02-04
- Volume
- 22
- Issue
- 4
- Pages
- 1573
- ISSN
- 1422-0067 (ISSN-L)
- PMC
- PMC7915186
- OpenAlex
- W3090504776
- Cited by
- 43
- References
- 75
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 7 · 2023: 10 · 2022: 10 · 2021: 10 · 2020: 2
- FWCI
- 4.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Molecular Sciences (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by-nc-nd)
- Funders
- National Science Foundation (CHE 1464906, MCB 1613643)
- National Institutes of Health (R15GM122013, 2R15GM109282)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Keywords
- SARS-CoV-2, COVID-19 pandemic, drug repurposing, de novo drug discovery, molecular docking, virtual screening, ACE2
- MeSH terms
- Machine Learning, Angiotensin-Converting Enzyme 2, COVID-19, SARS-CoV-2, COVID-19 Drug Treatment, Algorithms, Antiviral Agents, Drug Evaluation, Preclinical, Humans, Neural Networks, Computer, Drug Discovery, Drug Repositioning, Molecular Docking Simulation, Databases, Pharmaceutical, Spike Glycoprotein, Coronavirus
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
