Artificial intelligence in COVID-19 drug repurposing
Open accessAbstract
Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timelines and overall costs. In the big data era, artificial intelligence (AI) and network medicine offer cutting-edge application of information science to defining disease, medicine, therapeutics, and identifying targets with the least error. In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing. Rapidly developing, powerful, and innovative AI and network medicine technologies can expedite therapeutic development. This Review provides a strong rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic.
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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 [28]
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 [28]
AI-assisted tools for drugs and repayment for human diseases, including COVID-19, are strongly proved.
“The study is a strong proof for the use of AI-assisted tools for drugs and repayment for human diseases, including COVID-19 in times of fatal disease [11].”
From Influential Usage of Big Data and Artificial Intelligence in Healthcare (2021) · cites this paper as [11]
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“The key to identify therapeutic targets and discover drugs using ML-based biology analysis is to make use of network features in biological networks.”
BackgroundMethodDeep Learning applications for COVID-19
Shorten, C., Khoshgoftaar, T. M., & Furht, B. (2021) · Journal of Big Data · doi:10.1186/s40537-020-00392-9 · Cited by 350 · see citing works
“We refer readers to the illustrative diagram from their study [122] describing the integration of different data sources for Precision Diagnostics, shown in Fig.”
BackgroundMethodInfluentialZeng, X., Xiang, H., Yu, L., Wang, J., Li, K., Nussinov, R., & Cheng, F. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00557-6 · Cited by 310 · see citing works
“Benchmark evaluation of ImageMol We first evaluated the performance of ImageMol using eight types of benchmark dataset for drug discovery (Supplementary Tables 1–4): (1) molecular targets—human immunodeficiency virus (HIV), beta-secretase (BACE, a key target in Alzheimer’s disease) and maximum unbiased validation (17…”
MethodDeep generative molecular design reshapes drug discovery
Zeng, X., Wang, F., Luo, Y., Kang, S.-G., Tang, J., Lightstone, F. C., Fang, E. F., Cornell, W., Nussinov, R., & Cheng, F. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100794 · Cited by 295 · see citing works
Vișan, A. I., & Neguț, I. (2024) · Life · doi:10.3390/life14020233 · Cited by 321 · see citing works
“Yadi et al. [150] introduce guidelines on how to use AI for accelerating drug repurposing or repositioning.”
BackgroundA definition, benchmark and database of AI for social good initiatives
Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00296-0 · Cited by 212 · see citing works
Drug repurposing for COVID-19 via knowledge graph completion
Zhang, R., Hristovski, D., Schutte, D., Kastrin, A., Marcelo Fiszman, B., & Halil Kilicoglu, B. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103696 · Cited by 224 · see citing works
“The potential of recent advances in artificial intelligence (AI) and machine learning for COVID-19 drug repurposing has also been highlighted [12] and several studies using these techniques have reported promising re-sults [13 – 16].”
Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review
Khan, M., Mehran, M. T., Haq, Z. U., Ullah, Z., Naqvi, S. R., Ihsan, M., & Abbas, H. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115695 · Cited by 206 · see citing works
“It was identified by the network medicine analysis that Toremifene can significantly be used for treating COVID-19 disease (Zhou et al., 2020).”
Zhou, Y., Liu, Y., Gupta, S., Paramo, M. I., Hou, Y., Mao, C., Luo, Y., Judd, J., Wierbowski, S. D., Bertolotti, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01474-0 · Cited by 203 · see citing works
AlphaFold, Artificial Intelligence (AI), and Allostery
Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04346 · Cited by 190 · see citing works
Self-citationRobotics and artificial intelligence in healthcare during COVID-19 pandemic: a systematic review
Sarker, S., Jamal, L., Ahmed, S. F., & Irtisam, N. (2021) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2021.103902 · Cited by 199 · see citing works
Deep learning for drug repurposing: Methods, databases, and applications
Pan, X., Lin, X., Cao, D., Zeng, X., Yu, P. S., He, L., Nussinov, R., & Cheng, F. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1597 · Cited by 193 · see citing works
“[2] Effective identification of new indications from approved or well-established clinical drugs plays an essential part in drug discovery.[3]-[6] Such a process is also known as drug repositioning or reprofiling, and it can bypass many preapproval tests required for newly developed therapeutic compounds.”
BackgroundMethodInfluentialDrug combination therapy for emerging viral diseases
Shyr, Z. A., Cheng, Y., Lo, D. C., & Zheng, W. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.05.008 · Cited by 191 · see citing works
Zhou, Y., Hou, Y., Shen, J., Mehra, R., Kallianpur, A., Culver, D. A., Gack, M. U., Farha, S., Zein, J., Comhair, S., et al. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000970 · Cited by 180 · see citing works
“Knowledge of the complex interplays between SARS-CoV-2 targets and human diseases indicate possibilities of drug repurposing, as the drugs that target other diseases could potentially target SARS-CoV-2 through the shared functional PPI networks [23].”
BackgroundSelf-citationTribulations 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
“Numerous other examples highlight the capacity of AI-based methods to expedite drug screening and discovery [92] and enhance the development of more effective therapies, drug combinations for drug synergies [93, 94], and drug repurposing [88, 95–103] for various other diseases [104–110].”
BackgroundShaping the future of AI in healthcare through ethics and governance
Bouderhem, R. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-02894-w · Cited by 195 · see citing works
“Researchers discussed how AI systems could be used for repurposing or repositioning existing drugs which can help design new drug molecules or identify potential treatments for COVID-19 for instance (Zhou et al. 2020; Mohanty et al. 2020; Floresta et al. 2022).”
PharmKG: a dedicated knowledge graph benchmark for bomedical data mining
Zheng, S., Rao, J., Ying, S., Zhang, J., Xiao, X., Fang, E. F., Yang, Y., & Niu, Z. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa344 · Cited by 166 · see citing works
“By integration with the disease-specific context, PharmKG can be further utilized to accelerate drug repurposing and mechanism analysis for emerging human diseases, such as COVID-19 [ 90].”
MethodZhou, Y., Xu, J., Hou, Y., Leverenz, J. B., Kallianpur, A. R., Mehra, R., Liu, Y., Yu, H., Pieper, A. A., Jehi, L. E., et al. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00850-3 · Cited by 160 · see citing works
Drug repurposing for COVID-19: Approaches, challenges and promising candidates
Ng, Y.-L., Salim, C. K., & Chu, J. (2021) · Pharmacology and Therapeutics · doi:10.1016/j.pharmthera.2021.107930 · Cited by 124 · see citing works
“Pathway mapping has proved to be a major cornerstone in drug repurposing during this pandemic (Mousavi, Rahmanian, & Sami, 2020; Zhou et al., 2020).”
The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic
Piccialli, F., Cola, V. S. D., Giampaolo, F., & Cuomo, S. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10131-x · Cited by 153 · see citing works
Translational precision medicine: an industry perspective
Hartl, D., Luca, V. D., Kostikova, A., Laramie, J. M., Kennedy, S., Ferrero, E., Siegel, R. W., Fink, M., Ahmed, S., Millholland, J. M., et al. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-021-02910-6 · Cited by 153 · see citing works
“development concepts are getting higher traction; (ii) large longitudinal datasets collected from COVID19 patients are systematically analyzed and offer great potential for multi-omics [173–175] and AI-based analyses [169, 176–178], supporting diagnosis, basic disease understanding, endotyping, image analysis, drug ta…”
BackgroundGkeredakis, M., Lifshitz-Assaf, H., & Barrett, M. (2021) · Information and Organization · doi:10.1016/j.infoandorg.2021.100344 · Cited by 173 · see citing works
“In particular, as de-novo drug discovery had been perceived as almost impossible, there was an increase in development of AI for drug repurposing (Zhou, Wang, Tang, Nussinov, & Cheng, 2020).”
Bang, D., Lim, S., Lee, S., & Kim, S. (2023) · Nature Communications · doi:10.1038/s41467-023-39301-y · Cited by 131 · see citing works
Repurposing of CNS drugs to treat COVID-19 infection: targeting the sigma-1 receptor
Hashimoto, K. (2021) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-020-01231-x · Cited by 118 · see citing works
Current Strategies of Antiviral Drug Discovery for COVID-19
Mei, M., & Tan, X. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.671263 · Cited by 110 · see citing works
Biomolecular modeling thrives in the age of technology
Tamar Schlick, A., & Portillo‐Ledesma, S. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00060-9 · Cited by 110 · see citing works
Face mask detection using MobileNet and Global Pooling Block
Isunuri, B. V., Kakarla, J., & Prakash, S. (2020) · IEEE Conference on Information and Communication Technology (CICT) · doi:10.1109/cict51604.2020.9312083 · Cited by 108 · see citing works
“[3] have presented how to use artificial intelligence for accelerating drug repurposing or repositioning.”
Abu-Salih, B., Al‐Qurishi, M., Alweshah, M., AL-Smadi, M., Alfayez, R., & Saadeh, H. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00774-9 · Cited by 121 · see citing works
“It aims to reuse existing drugs to treat emerging diseases (such as COVID-19) thereby reducing both drug development timelines and the associated costs [54].”
BackgroundCovid-19 and Artificial Intelligence: Genome sequencing, drug development and vaccine discovery
Bagabir, S. A., Ibrahim, N. K., Bagabir, H. A., & Ateeq, R. H. (2022) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2022.01.011 · Cited by 106 · see citing works
“In addition, AI is a core of advanced technologies where the discovered therapy would be available for a clinical trial, and when it is approved it uses for health care [33].”
The metaverse in current digital medicine
Sun, M., Xie, L., Liu, Y., Li, K., Jiang, B., Lu, Y., Yang, Y., Yu, H., Song, Y., Bai, C., et al. (2022) · Clinical eHealth · doi:10.1016/j.ceh.2022.07.002 · Cited by 104 · see citing works
Arora, G., Joshi, J., Mandal, R. S., Shrivastava, N., Virmani, R., & Sethi, T. (2021) · Pathogens · doi:10.3390/pathogens10081048 · Cited by 111 · see citing works
A heterogeneous network embedding framework for predicting similarity-based drug-target interactions
An, Q., & Yu, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab275 · Cited by 99 · see citing works
Alsalem, M. A., Mohammed, R. T., Albahri, O. S., Zaidan, A. A., Alamoodi, A. H., Dawood, K. A., Alnoor, A., Albahri, A. S., Zaidan, B. B., Aickelin, U., et al. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22699 · Cited by 94 · see citing works
Ahmed, F., Soomro, A. M., Salih, A. R. C., Samantasinghar, A., Asif, A., Kang, I. S., & Choi, K. H. (2022) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2022.113350 · Cited by 92 · see citing works
Naik, N., Hameed, B. M. Z., Sooriyaperakasam, N., Vinayahalingam, S., Patil, V., Smriti, K., Saxena, J., Shah, M., Ibrahim, S., Singh, A., et al. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.919985 · Cited by 93 · see citing works
Abdeldayem, O. M., Dabbish, A. M., Habashy, M. M., Mostafa, M. K., Elhefnawy, M., Amin, L., Al-Sakkari, E. G., Ragab, A., & Rene, E. R. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.149834 · Cited by 90 · see citing works
“Furthermore, ML and deep learning (DL) models can help fast drug repurposing or even drug discovery for a novel disease, such as in COVID-19 (Ivanov et al., 2020; Jain et al., 2021; Mohanty et al., 2020; Zhou et al., 2020).”
Blockchain, artificial intelligence, and healthcare: the tripod of future—a narrative review
Bathula, A., Gupta, S. K., Merugu, S., Saba, L., Khanna, N. N., Laird, J. R., Sanagala, S. S., Singh, R. K., Garg, D., Fouda, M. M., et al. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10873-5 · Cited by 100 · see citing works
Zhou, Y., Fang, J., Bekris, L. M., Kim, Y. H., Pieper, A. A., Leverenz, J. B., Cummings, J. L., & Cheng, F. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-020-00760-w · Cited by 86 · see citing works
Ünal, M. A., Bayrakdar, F., Nazır, H., Beşbinar, Ö., Gürcan, C., Lozano, N., Arellano, L. M., Yalçın, S., Panatli, O., Çelik, D., et al. (2021) · Small · doi:10.1002/smll.202101483 · Cited by 82 · see citing works
Repurposing drugs for the management of COVID-19
Cusinato, J., Cau, Y., Calvani, A. M., & Mori, M. (2020) · Expert Opinion on Therapeutic Patents · doi:10.1080/13543776.2021.1861248 · Cited by 79 · see citing works
Shahid, O., Nasajpour, M., Pouriyeh, S., Parizi, R. M., Han, M., Valero, M., Li, F., Aledhari, M., & Sheng, Q. Z. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103751 · Cited by 75 · see citing works
“We have already seen ML methods and techniques like SVM, RNN and Bayesian Classifiers being used for drug discovery and repurposing [41,244].”
SAveRUNNER: an R-based tool for drug repurposing
Fiscon, G., & Paci, P. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04076-w · Cited by 74 · see citing works
The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype
Abdulkareem, M., & Petersen, S. E. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.652669 · Cited by 78 · see citing works
Dong, J., Wu, H., Zhou, D., Li, K., Zhang, Y., Ji, H., Tong, Z., Lou, S., & Liu, Z. (2021) · Journal of Medical Systems · doi:10.1007/s10916-021-01757-0 · Cited by 74 · see citing works
Fan, K., Cheng, L., & Li, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab271 · Cited by 74 · see citing works
Manjunath, S. H., & Thimmulappa, R. K. (2021) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2021.09.009 · Cited by 73 · see citing works
“Based on the knowledge gained from a mechanistic understanding of COVID-19 pathogenesis and big-data analysis using arti fi cial intelligence, several approved drugs are being repositioned for COVID-19 treatment [6].”
Rodríguez‐Rodríguez, I., Rodríguez, J., Shirvanizadeh, N., Ortíz, A., & Pardo-Quiles, D. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18168578 · Cited by 77 · see citing works
The machine learning life cycle and the cloud: implications for drug discovery
Spjuth, O., Frid, J., & Hellander, A. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1932812 · Cited by 67 · see citing works
“It is envisioned that AI will increase productivity and reduce attrition in drug discovery [12], a hypothesis that has been strengthened during the COVID-19 pandemic [13–15].”
BackgroundThe Potential Applications of Artificial Intelligence in Drug Discovery and Development
Farghali, H., Kutinová Canová, N., & Arora, M. (2021) · Physiological Research · doi:10.33549/physiolres.934765 · Cited by 72 · see citing works
Artificial Intelligence in Drug Design: Algorithms, Applications, Challenges and Ethics
Arabi, A. A. (2021) · Future Drug Discovery · doi:10.4155/fdd-2020-0028 · Cited by 74 · see citing works
Tripathi, N., Goshisht, M. K., Sahu, S. K., & Arora, C. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10237-z · Cited by 74 · see citing works
Kalakoti, Y., Yadav, S., & Sundar, D. (2022) · ACS Omega · doi:10.1021/acsomega.1c05203 · Cited by 73 · see citing works
Drug Design and Disease Diagnosis: The Potential of Deep Learning Models in Biology
Sreeraman, S., Mayuri, K., Kushwah, R. B. S., Sundaram, V., Veluchamy, A., Anand, T., & Saravanan, K. M. (2023) · Current Bioinformatics · doi:10.2174/1574893618666230227105703 · Cited by 70 · see citing works
Influential Usage of Big Data and Artificial Intelligence in Healthcare
Yang, Y. C., Islam, S. U., Noor, A., Khan, S., Afsar, W., & Nazir, S. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/5812499 · Cited by 68 · see citing works
“The study is a strong proof for the use of AI-assisted tools for drugs and repayment for human diseases, including COVID-19 in times of fatal disease [11].”
MethodRetractedWu, H., Lu, X., & Wang, H. (2023) · JMIR AI · doi:10.2196/38397 · Cited by 69 · see citing works
“At the molecular level, protein structure prediction, novel nucleic acid testing, drug repurposing, and drug discovery all rely on AI and deep-learning algorithms [7-9].”
BackgroundAhmed, F., Kang, I. S., Kim, K. H., Asif, A., Rahim, C. S. A., Samantasinghar, A., Memon, F. H., & Choi, K. H. (2023) · Journal of Medical Virology · doi:10.1002/jmv.28693 · Cited by 75 · see citing works
“In this section ML, DL, and other approaches ‐ based drug repurposing approaches 24,171 – 173 are mentioned along with some examples.”
BackgroundThe Role of Chatbots in Enhancing Customer Experience: Literature Review
Bakkouri, B. E., Raki, S., & Belgnaoui, L. T. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.07.057 · Cited by 88 · see citing works
Padhi, A., Agarwal, A., Saxena, S. K., & Katoch, C. D. S. (2023) · VirusDisease · doi:10.1007/s13337-023-00841-y · Cited by 67 · see citing works
“The integration of AI not only accelerated the identification process but also reduced the dependency on time-consuming wet-lab experiments, signifying a more efficient approach towards therapeutic discoveries [27].”
BackgroundMachine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges
Qi, X., Zhao, Y., Qi, Z., Hou, S., & Chen, J. (2024) · Molecules · doi:10.3390/molecules29040903 · Cited by 75 · see citing works
“The template-based approach involves systematically comparing the target molecule with a set of templates, each representing alternative substructure patterns that occur during a chemical reaction [105].”
BackgroundMethodApplication of Artificial Intelligence in COVID-19 Pandemic: Bibliometric Analysis
Islam, M. M., Poly, T. N., Alsinglawi, B. S., Lin, L., Chien, S.-C., Liu, J., & Jian, W. (2021) · Healthcare · doi:10.3390/healthcare9040441 · Cited by 66 · see citing works
“Rank Author Journal Citations, n Strength 1 Huang Chaolin et al. 2020 Lancet 150 905 2 Wei-jie Guan et al. 2020 The New England Journal of Medicine 81 403 3 Kaiming He et al. 2016 IEEE Conference on Computer Vision and Pattern Recognition 78 688 4 Tao Ai et al. 2020 Radiology 75 616 5 Nanshan Chen et al. 2020 Lancet 7…”
InfluentialAntisense Oligonucleotide-Based Therapy of Viral Infections
Tarn, W., Cheng, Y., Ko, S.-H., & Huang, L. (2021) · Pharmaceutics · doi:10.3390/pharmaceutics13122015 · Cited by 64 · see citing works
Expert-Augmented computational drug repurposing identified baricitinib as a treatment for COVID-19
Smith, D. P., Oechsle, O., Rawling, M. J., Savory, E., Lacoste, A. M. B., & Richardson, P. J. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.709856 · Cited by 62 · see citing works
Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic
El-Rashidy, N. M., Abdelrazek, S. M., Abuhmed, T., Amer, E., Ali, F., Hu, J. W., & El–Sappagh, S. (2021) · Diagnostics · doi:10.3390/diagnostics11071155 · Cited by 61 · see citing works
“AI also can speed up the pace by extracting useful data for drug repurposing [160].”
BackgroundKhoury, P., Srinivasan, R., Kakumanu, S., Ochoa, S., Keswani, A., Sparks, R., & Rider, N. L. (2022) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2022.01.047 · Cited by 64 · see citing works
“ML approaches are being leveraged to classify immune cell stress features and predict antiviral responses for drug repurposing efforts.(33,34) Lastly, ML is being coupled with reverse vaccinology to predict viral-host protein interactions and identify druggable targets and vaccine epitopes.”
BackgroundCheng, F., Wang, F., Tang, J., Zhou, Y., Fu, Z., Zhang, P., Haines, J. L., Leverenz, J. B., Gan, L., Hu, J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101379 · Cited by 63 · see citing works
Development and application of therapeutic antibodies against COVID-19
Ning, L.-J., Abagna, H. B., Jiang, Q., Liu, S., & Huang, J. (2021) · International Journal on Biological Sciences · doi:10.7150/ijbs.59149 · Cited by 52 · see citing works
Emerging Therapeutic Approaches to Combat COVID-19: Present Status and Future Perspectives
Vivekanandhan, K., Poornima, S., Barabadi, H., Arumugam, V., Raj, D. D. R. D. P., Manikandan, S., Subbaiya, R., Anand, K., Pandi, B., Chandrasekaran, B., et al. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.604447 · Cited by 58 · see citing works
Bompelli, A., Wang, Y., Wan, R., Singh, E., Zhou, Y., Xu, L., Oniani, D., Kshatriya, B. S. A., Balls-Berry, J. (. E., & Zhang, R. (2021) · Health Data Science · doi:10.34133/2021/9759016 · Cited by 58 · see citing works
“Afshar et al. [46] used LDA to identify subtypes of patients with opioid misuse whereas Cui et al. [67] used K-means clustering and principal component analysis (PCA) to analyze and discover latent clusters in COVID-19 patients.”
MethodInfluentialMajeed, A., & Hwang, S. O. (2021) · Symmetry · doi:10.3390/sym14010016 · Cited by 62 · see citing works
Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2
Prasad, K., & Kumar, V. (2021) · Current Research in Pharmacology and Drug Discovery · doi:10.1016/j.crphar.2021.100042 · Cited by 58 · see citing works
Machine learning and network medicine approaches for drug repositioning for COVID-19
Santos, S. D. S., Torres, M., Galeano, D., Sánchez, M. D. M., Cernuzzi, L., & Paccanaro, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100396 · Cited by 55 · see citing works
Abbas, S. R., Seol, H., Abbas, Z., & Lee, S. W. (2025) · Healthcare · doi:10.3390/healthcare13141642 · Cited by 55 · see citing works
“This level of functionality is inspired by cognitive psychology, where ToM is central to empathetic and cooperative behavior [88].”
Data science approaches to confronting the COVID-19 pandemic: a narrative review
Zhang, Q., Gao, J., Wu, J. T. K., Cao, Z., & Zeng, D. D. (2021) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2021.0127 · Cited by 52 · see citing works
Partner-Specific Drug Repositioning Approach Based on Graph Convolutional Network
Sun, X., Wang, B., Zhang, J., & Li, M. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3194891 · Cited by 53 · see citing works
“disease (COVID-19) [2], [3], suggesting that drug repositioning is a promising way to fight against diseases with no curative treatment.”
BackgroundTutsoy, Ö. (2021) · IEEE Access · doi:10.1109/access.2021.3078080 · Cited by 53 · see citing works
“…from the chest Computed Tomography (CT) [19], to speed up the Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) tests used for the diagnosis of the COVID-19 [20], to halt viral replication by designing new molecules [21], and to re-evaluate the drugs used for the COVID-19 treatments [22].”
BackgroundMethodRecent advances from computer-aided drug design to artificial intelligence drug design
Wang, K., Huang, Y., Wang, Y., You, Q., & Wang, L. (2024) · RSC Medicinal Chemistry · doi:10.1039/d4md00522h · Cited by 54 · see citing works
A BioID-Derived Proximity Interactome for SARS-CoV-2 Proteins
May, D. G., Martin‐Sancho, L., Anschau, V., Liu, S. N., Chrisopulos, R. J., Scott, K. L., Halfmann, C. T., Peña, R. D., Pratt, D. R., Campos, A. R., et al. (2022) · Viruses · doi:10.3390/v14030611 · Cited by 50 · see citing works
HGTDR: Advancing drug repurposing with heterogeneous graph transformers
Gharizadeh, A., Abbasi, K., Ghareyazi, A., Mofrad, M. R. K., & Rabiee, H. R. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae349 · Cited by 49 · see citing works
“Drug repurposing, defined as identifying new uses for approved drugs, has emerged as a viable solution to these issues (Zhou et al. 2020).”
BackgroundEl-Behery, H., Attia, A.-F. A., El-Fishawy, N. A., & Torkey, H. (2021) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2021.107536 · Cited by 45 · see citing works
“Another research in (Zhou et al., 2020) provides guidelines on how to use artificial intelligence (AI) to accelerate the redefinition or repositioning of drugs and show which AI approaches are not only massive but also necessary.”
BackgroundArtificial Intelligence in Accelerating Drug Discovery and Development
Tripathi, A., Misra, K., Dhanuka, R., & Singh, J. P. (2022) · Recent Patents on Biotechnology · doi:10.2174/1872208316666220802151129 · Cited by 45 · see citing works
Ebrahimi, K. S., Ansari, M., Moghaddam, M. S. H., Ebrahimi, Z., Salehi, Z., Shahlaei, M., & Moradi, S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104613 · Cited by 45 · see citing works
Artificial intelligence for COVID-19: battling the pandemic with computational intelligence
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Genome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets
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- Authors
- Zhou, Y., Wang, F., Tang, J., Nussinov, R., & Cheng, F.
- Affiliations
- Cleveland Clinic (US)
- Cornell University (US)
- HEC Montréal (CA)
- Tel Aviv University (IL)
- Corresponding author
- Cheng, Feixiong
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 2
- Issue
- 12
- Pages
- e667-e676
- ISSN
- 2589-7500 (ISSN-L)
- PMC
- PMC7500917
- OpenAlex
- W3087156149
- Cited by
- 636
- References
- 89
- Citations by year
- 2026: 50 · 2025: 74 · 2024: 73 · 2023: 131 · 2022: 117 · 2021: 173 · 2020: 18
- FWCI
- 9.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- reponame:Expeditio Repositorio Institucional UJTL
- Funders
- National Science Foundation (2027970, 1750326, HHSN261200800001E)
- Cleveland Clinic
- Frederick National Laboratory for Cancer Research (HHSN261200800001E.)
- National Institutes of Health (#HHSN261200800001E, COVID-19, HHSN261200800001C)
- National Institute on Aging (R01AG066707)
- National Heart, Lung, and Blood Institute (R00HL138272, HHSN261200800001E)
- Office of Naval Research (N00014-18-1-2585, N00014)
- National Cancer Institute (HHSN261200800001E))
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Keywords
- drug repurposing, COVID-19 pandemic, artificial intelligence, network medicine, precision medicine, drug target identification, drug development acceleration, big data
- MeSH terms
- SARS-CoV-2, COVID-19 Drug Treatment, Algorithms, Antiviral Agents, Artificial Intelligence, Drug Therapy, Combination, Humans, Treatment Outcome, Drug Discovery, Drug Repositioning
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