Network-based approach to prediction and population-based validation of in silico drug repurposing
Open accessAbstract
Here we identify hundreds of new drug-disease associations for over 900 FDA-approved drugs by quantifying the network proximity of disease genes and drug targets in the human (protein–protein) interactome. We select four network-predicted associations to test their causal relationship using large healthcare databases with over 220 million patients and state-of-the-art pharmacoepidemiologic analyses. Using propensity score matching, two of four network-based predictions are validated in patient-level data: carbamazepine is associated with an increased risk of coronary artery disease (CAD) [hazard ratio (HR) 1.56, 95% confidence interval (CI) 1.12–2.18], and hydroxychloroquine is associated with a decreased risk of CAD (HR 0.76, 95% CI 0.59–0.97). In vitro experiments show that hydroxychloroquine attenuates pro-inflammatory cytokine-mediated activation in human aortic endothelial cells, supporting mechanistically its potential beneficial effect in CAD. In summary, we demonstrate that a unique integration of protein-protein interaction network proximity and large-scale patient-level longitudinal data complemented by mechanistic in vitro studies can facilitate drug repurposing.
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Because gene expression data can be noisy and variable, recent work fuses co-expression matrices with existing biomedical networks such as GO annotations and PPI, then feeds the resulting graph into graph convolution layers.
“Because gene expression data can be noisy and variational, recent advances include fusing the co-expression matrices with existing biomedical networks, such as GO annotations and PPI, and feeding the resulting graph into graph convolution layers (Section 2.2) [150, 151, 152].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [152]
This fusion has enabled more interpretable disease classification models, for example by weighting gene interactions using existing biological knowledge.
“Doing so has enabled more interpretable disease classification models (e.g., weighting gene interactions based on existing biological knowledge).”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [152]
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Cardiovascular risk factors in epilepsy patients taking levetiracetam, carbamazepine or lamotrigine
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“(131,919) (47,787) Total population considered for inclusion”
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The Future of Cardiovascular Therapeutics
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Self-citationPharmacogenetic potential biomarkers for carbamazepine adverse drug reactions and clinical response
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“with structural heart disease (42, 43) and adverse cardiac events (44, 45).”
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Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2
Zhou, Y., Hou, Y., Shen, J., Huang, Y., Martin, W., & Cheng, F. (2020) · Cell Discovery · doi:10.1038/s41421-020-0153-3 · Cited by 1,588 · see citing works
Self-citationNetwork-based prediction of drug combinations
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Self-citationArtificial intelligence in COVID-19 drug repurposing
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deepDR: a network-based deep learning approach toin silicodrug repositioning
Zeng, X., Zhu, S., Liu, X., Zhou, Y., Nussinov, R., & Cheng, F. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz418 · Cited by 532 · see citing works
Self-citationCOVID-19 and Cardiovascular Disease
Chung, M. K., Zidar, D. A., Bristow, M. R., Cameron, S. J., Chan, T. A.-T., Harding, C. V., Kwon, D. H., Singh, T. K., Tilton, J. C., Tsai, E. J., et al. (2021) · Circulation Research · doi:10.1161/circresaha.121.317997 · Cited by 371 · see citing works
Self-citationTarget identification among known drugs by deep learning from heterogeneous networks
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Self-citationMachine learning identifies candidates for drug repurposing in Alzheimer’s disease
Rodriguez, S., Hug, C., Todorov, P., Moret, N., Boswell, S. A., Evans, K., Zhou, G., Johnson, N. T., Hyman, B. T., Sorger, P. K., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21330-0 · Cited by 299 · see citing works
A knowledge graph to interpret clinical proteomics data
Santos, A., Colaço, A. R., Nielsen, A. B., Niu, L., Strauss, M. T., Geyer, P. E., Coscia, F., Albrechtsen, N. J. W., Mundt, F. O., Jensen, L. J., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01145-6 · Cited by 345 · see citing works
“Graph-based predictions have been used in multiple scenarios, including drug repurposing, protein–protein interaction (PPI) prediction, disease comorbidity risks or diet-based cancer therapy associations 78–80 .”
MethodGraph representation learning in biomedicine and healthcare
Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294 · see citing works
Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning
Zeng, X., Song, X. H., Ma, T., Pan, X., Zhou, Y., Hou, Y., Zhang, Z., Li, K., Karypis, G., & Cheng, F. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00316 · Cited by 258 · see citing works
Self-citationGAP43-dependent mitochondria transfer from astrocytes enhances glioblastoma tumorigenicity
Watson, D. C., Bayık, D., Storevik, S., Moreino, S. S., Sprowls, S. A., Han, J., Augustsson, M. T., Lauko, A. J., Palavalasa, S., Røsland, G. V., et al. (2023) · Nature Cancer · doi:10.1038/s43018-023-00556-5 · Cited by 254 · see citing works
Self-citationMyeloid-Derived Suppressor Cell Subsets Drive Glioblastoma Growth in a Sex-Specific Manner
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Self-citationComprehensive characterization of protein–protein interactions perturbed by disease mutations
Cheng, F., Zhao, J., Wang, Y., Lü, W., Liu, Z., Zhou, Y., Martin, W. R., Wang, R., Huang, J. Y., Hao, T., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00774-y · Cited by 216 · see citing works
“Previous studies have demonstrated that the human protein–protein interactome provides a powerful network-based tool to quantify disease–disease 6 and drug–disease 7–9 relationships; however, the functional network consequences of disease-associated mutations remain largely unknown.”
BackgroundSelf-citationReducing acetylated tau is neuroprotective in brain injury
Shin, M.-K., Vázquez-Rosa, E., Koh, Y., Dhar, M., Chaubey, K., Cintrón-Pérez, C. J., Barker, S., Miller, E., Franke, K., Noterman, M. F., et al. (2021) · Cell · doi:10.1016/j.cell.2021.03.032 · Cited by 226 · see citing works
“Two cohort analyses were conducted to evaluate the predicted association, based on individual level of longitudinal patient data and pharmacoepidemiologic methods, as previously established (Cheng et al., 2018).”
BackgroundMethodSelf-citationComputational network biology: Data, models, and applications
Liu, C., Ma, Y., Zhao, J., Nussinov, R., Zhang, Y., Cheng, F., & Zhang, Z. (2019) · Physics Reports · doi:10.1016/j.physrep.2019.12.004 · Cited by 226 · see citing works
Self-citationDeep Learning-Based Prediction of Drug-Induced Cardiotoxicity
Cai, C., Guo, P., Zhou, Y., Zhou, J., Wang, Q., Zhang, F., Fang, J., & Cheng, F. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00769 · Cited by 219 · see citing works
Self-citationA genome-wide positioning systems network algorithm for in silico drug repurposing
Cheng, F., Lu, W., Liu, C., Fang, J., Hou, Y., Handy, D. E., Wang, R., Zhao, Y., Yang, Y., Huang, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10744-6 · Cited by 215 · see citing works
“…doxorubicin, and tamoxifen) de fi ned by the fi rst-level of the Anatomical Therapeutic Chemical Classi fi cation codes are correctly predicted by the network proximity approach compared to GSEA, revealing the power of the network approach for drug repurposing, consistent with our recent study 8 .”
BackgroundMethodResultInfluentialSelf-citationKong, J., Lee, H., Kim, D., Han, S. K., Ha, D., Shin, K., & Kim, S. (2020) · Nature Communications · doi:10.1038/s41467-020-19313-8 · Cited by 219 · see citing works
Molecular networks in Network Medicine: Development and applications
Silverman, E. K., Schmidt, H. H. H. W., Anastasiadou, E., Altucci, L., Angelini, M., Badimón, L., Balligand, J., Benincasa, G., Capasso, G., Conte, F., et al. (2020) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1489 · Cited by 211 · see citing works
“Furthermore, a systems pharmacology-based platform measuring the distance between disease proteins and drug targets in the human PPI was developed (Cheng et al., 2018).”
BackgroundMethodInfluentialSelf-citationZhou, 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
Self-citationFang, J., Zhang, P., Zhou, Y., Chiang, C.-W., Tan, J., Hou, Y., Stauffer, S., Li, L., Pieper, A. A., Cummings, J., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00138-z · Cited by 206 · see citing works
Self-citationZhou, 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
“the network-based relationships between COVID-19 and human diseases in the human interactome model using state-of-the-art network proximity analysis [32,33,49].”
BackgroundMethodInfluentialSelf-citationGan, X., Shu, Z., Wang, X., Yan, D., Li, J., Ofaim, S., Albert, R., Li, X., Liu, B., Zhou, X., et al. (2023) · Science Advances · doi:10.1126/sciadv.adh0215 · Cited by 178 · see citing works
“…and shorter network distance between two disease modules is indicative of their comorbidity ( 16 ); more-over, drug efficacy can be predicted by leveraging the network relation between drug targets and disease modules ( 17 , 18 ), leading to the development of drug-repurposing methodologies ( 19 ).”
BackgroundIdentification of disease treatment mechanisms through the multiscale interactome
Ruiz, C., Zitnik, M., & Leskovec, J. (2021) · Nature Communications · doi:10.1038/s41467-021-21770-8 · Cited by 172 · see citing works
“Construct a reference distribution of shortest paths for these two protein pairs by following 762 the approach in [10, 13].”
BackgroundMoving Beyond the Sarcomere to Explain Heterogeneity in Hypertrophic Cardiomyopathy
Maron, B. J., Maron, M. S., Maron, B. A., & Loscalzo, J. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.01.061 · Cited by 161 · see citing works
Self-citationZhou, 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
“Therefore, degree-controlled permutation tests were based on gene sets that had similar degree distributions to the gene sets-of-interest to reduce the effect of literature-based degree biases based on our previous studies [33, 38, 56].”
BackgroundMethodInfluentialSelf-citationTargeting the Immune System in Atherosclerosis
Zhao, T. X., & Mallat, Z. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.12.083 · Cited by 158 · see citing works
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Self-citationExploration of databases and methods supporting drug repurposing: a comprehensive survey
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The multiplex network of human diseases
Halu, A., Domenico, M. D., Arenas, À., & Sharma, A. (2019) · npj Systems Biology and Applications · doi:10.1038/s41540-019-0092-5 · Cited by 133 · see citing works
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“Repositioning for Rare Diseases Identify potential drug candidates for treating rare diseases by analyzing molecular profiles, gene expression data, and clinical information.(124)”
BackgroundSinha, S. K., Prasad, S. K., Islam, M. A., Gurav, S. S., Patil, R. B., AlFaris, N. A., Aldayel, T. S., AlKehayez, N. M., Wabaidur, S. M., & Shakya, A. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1779132 · Cited by 131 · see citing works
“In principle, the cost-effective and time-efficient computational technique provides a powerful network-based tool (Cheng et al., 2018) to test a novel hypotheses conceptualizing systematic drug repositioning strategy for rapid identification of therapeutic lead and/or potential leads combinations…”
BackgroundFang, J., Zhang, P., Wang, Q., Chiang, C.-W., Zhou, Y., Hou, Y., Xu, J., Chen, R., Zhang, B., Lewis, S. J., et al. (2022) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00951-z · Cited by 140 · see citing works
“To examine the potential application of ARGs on AD drug repurposing, we applied a network proximity approach [15] to quantify the interplay between AD modules from ARGs and drug targets in the human interactome network.”
BackgroundMethodResultInfluentialSelf-citationMercurio, I., Tragni, V., Busto, F., Grassi, A., & Pierri, C. L. (2020) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-020-03580-1 · Cited by 127 · see citing works
Robustness and lethality in multilayer biological molecular networks
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Self-citationLessons from the COVID-19 pandemic for advancing computational drug repurposing strategies
Galindez, G., Matschinske, J., Rose, T. D., Sadegh, S., Salgado-Albarrán, M., Späth, J., Baumbach, J., & Pauling, J. K. (2021) · Nature Computational Science · doi:10.1038/s43588-020-00007-6 · Cited by 121 · see citing works
“Network proximity relies on the principle that a drug can be effective if it targets proteins within the neighborhood of disease-associated proteins in the interactome 67 .”
Backgrounddo Valle, Í. F., Roweth, H. G., Malloy, M. W., Moco, S., Barron, D., Battinelli, E. M., Loscalzo, J., & Barabási, A.-Ĺ. (2021) · Nature Food · doi:10.1038/s43016-021-00243-7 · Cited by 118 · see citing works
Self-citationCurrent and future directions in network biology
Žitnik, M., Li, M. M., Wells, A., Glass, K., Gysi, D. M., Krishnan, A., Murali, T. M., Predrag Radivojac, A., Roy, S., Baudot, A., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae099 · Cited by 118 · see citing works
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Self-citationMoingeon, P. E., Kuenemann, M. A., & Guedj, M. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.09.006 · Cited by 119 · see citing works
Advancing pharmaceutical research: A comprehensive review of cutting-edge tools and technologies
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Digital twins as global learning health and disease models for preventive and personalized medicine
Li, X., Loscalzo, J., Mahmud, A. K. M. F., Aly, D. M., Rzhetsky, A., Žitnik, M., & Benson, M. (2025) · Genome Medicine · doi:10.1186/s13073-025-01435-7 · Cited by 124 · see citing works
Self-citationNetwork medicine framework for identifying drug-repurposing opportunities for COVID-19
Morselli Gysi, D., do Valle, Í., Zitnik, M., Ameli, A., Gan, X., Varol, O., Ghiassian, S. D., Patten, J. J., Davey, R. A., Loscalzo, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025581118 · Cited by 107 · see citing works
“(1) First, it uses the current aggregated ranking R to update the importance weights K r for each ranked list.”
MethodSelf-citationYang, J., Tian, S., Zhao, J., & Zhang, W. (2020) · Pharmacological Research · doi:10.1016/j.phrs.2020.105034 · Cited by 106 · see citing works
Network-based multi-omics integrative analysis methods in drug discovery: a systematic review
Jiang, W., Ye, W., Tan, X., & Bao, Y. (2025) · BioData Mining · doi:10.1186/s13040-025-00442-z · Cited by 107 · see citing works
“…inheritance patterns, curated genetic data [39] CIVIC Clinical interpretations Clinical evidence summaries, drug-disease relationships, evidence quality scoring [29] hubs often has far-reaching consequences, potentially leading to systemic dysfunction and disease manifestation [50, 51].”
SAveRUNNER: A network-based algorithm for drug repurposing and its application to COVID-19
Fiscon, G., Conte, F., Farina, L., & Paci, P. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008686 · Cited by 106 · see citing works
“This randomization procedure allows to preserve the biological meaning of the network connections between any pair of nodes [14, 18].”
BackgroundMethodResultInfluentialNetwork Medicine in Pathobiology
Lee, L. Y.-H., & Loscalzo, J. (2019) · The American Journal of Pathology · doi:10.1016/j.ajpath.2019.03.009 · Cited by 95 · see citing works
“107 Cheng et al 16 examined new drug-disease interactions involving over 900 FDA-approved drugs by measuring the network proximity between known drug targets and disease proteins in the human interactome.”
BackgroundSelf-citationDing, J., Blencowe, M., Nghiem, T. X., Ha, S. M., Chen, Y.-W., Li, G., & Yang, X. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab405 · Cited by 96 · see citing works
“Network-based drug repositioning ranks drugs based on the degree of connectivity of genes influenced by drug treatments to disease gene signatures in a given gene network model (44).”
BackgroundHarnessing endophenotypes and network medicine for Alzheimer's drug repurposing
Fang, J., Pieper, A. A., Nussinov, R., Lee, G., Bekris, L. M., Leverenz, J. B., Cummings, J. L., & Cheng, F. (2020) · Medicinal Research Reviews · doi:10.1002/med.21709 · Cited by 93 · see citing works
“In recent years, various bioinformatic or network‐based approaches have provided tools for mechanistic examination of endophenotypes implicated in AD.(19,142) For example, the Multi‐objective Analyzer for Genetic Marker Acquisition (MAGMA) algorithm was utilized to explore whether genetic risk of AD (such as AD GWAS c…”
BackgroundMethodInfluentialSelf-citationA paradigm shift in medicine: A comprehensive review of network-based approaches
Conte, F., Fiscon, G., Licursi, V., Bizzarri, D., D'Antò, T., Farina, L., & Paci, P. (2019) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2019.194416 · Cited by 95 · 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
“PPI data gathered from 15 commonly used databases was retrieved from [171].”
Computational systems biology in disease modeling and control, review and perspectives
Yue, R., & Dutta, A. (2022) · npj Systems Biology and Applications · doi:10.1038/s41540-022-00247-4 · Cited by 98 · see citing works
“Drug-target interaction (DTI) networks have been applied to study the prediction of drug response 51 , interaction pro fi les of new drug-target pairs 16,26,27,52 , and side effects of unknown drug combination 22,53,54 .”
MethodWang, Z., Zhou, M., & Arnold, C. W. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa437 · Cited by 91 · see citing works
“Network-based approaches (Cheng et al., 2018, 2019; Guney et al., 2016; Luo et al., 2016; Zeng et al., 2019) model graphstructured information among different biological networks to boost the performance for DR. Typically, in these models, the nodes in the networks represent either drug, disease or gene products and e…”
BackgroundInfluentialPaci, P., Fiscon, G., Conte, F., Wang, R., Farina, L., & Loscalzo, J. (2021) · npj Systems Biology and Applications · doi:10.1038/s41540-020-00168-0 · Cited by 88 · see citing works
Self-citationDeepScreening: a deep learning-based screening web server for accelerating drug discovery
Liu, Z., Du, J., Fang, J., Yin, Y., Xu, G., & Xie, L. (2019) · Database · doi:10.1093/database/baz104 · Cited by 87 · see citing works
Integrative proteomics and phosphoproteomics in pulmonary arterial hypertension
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Self-citationZhou, 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
Self-citationAutomated data-adaptive analytics for electronic healthcare data to study causal treatment effects
Schneeweiß, S. (2018) · Clinical Epidemiology · doi:10.2147/clep.s166545 · Cited by 81 · see citing works
Repurposing Medications for Treatment of Pulmonary Arterial Hypertension: What's Old Is New Again
Prins, K. W., Thenappan, T., Weir, Ε. K., Kalra, R., PRITZKER, M. R., & Archer, S. L. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.118.011343 · Cited by 79 · see citing works
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“Recent advances in systems biology and omics technologies have enabled the development of in silico - network-based (Cheng et al., 2018; Cheng et al., 2019a; Cheng et al., 2012b), genomics-based (Jahchan et al.”
BackgroundMethodResultInfluentialSelf-citationPharmacogenomics of COVID-19 therapies
Takahashi, T., Luzum, J. A., Nicol, M. R., & Jacobson, P. A. (2020) · npj Genomic Medicine · doi:10.1038/s41525-020-00143-y · Cited by 76 · see citing works
High-dimensional role of AI and machine learning in cancer research
Capobianco, E. (2022) · British Journal of Cancer · doi:10.1038/s41416-021-01689-z · Cited by 76 · see citing works
“A few major directions are: (i) repurposing existing drugs for treatment decisions supported by multi-sourced protein–protein interaction networks [4], (ii) developing ML algorithms predictive of anticancer drug efficacy [5], (iii) advancing semantics to deploy public cancer genomic datasets as linked data and offer a…”
BackgroundSAveRUNNER: 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
Sarno, F., Benincasa, G., List, M., Barabási, A.-L., Baumbach, J., Ciardiello, F., Filetti, S., Glass, K., Loscalzo, J., Marchese, C., et al. (2021) · Clinical Epigenetics · doi:10.1186/s13148-021-01047-z · Cited by 73 · see citing works
Self-citationHost metabolic reprogramming in response to SARS-CoV-2 infection: A systems biology approach
Moolamalla, S. T. R., Balasubramanian, R., Chauhan, R., Priyakumar, U. D., & Vinod, P. K. U. (2021) · Microbial Pathogenesis · doi:10.1016/j.micpath.2021.105114 · Cited by 73 · see citing works
Wang, X., Cheng, Y., Yang, Y., Yu, Y., Li, F., & Peng, S. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00640-6 · Cited by 73 · see citing works
“To improve the efficiency of drug discovery, a great number of researchers have devoted to developing or leveraging deep learning to speed up its intermediate steps, such as molecular property prediction [3-4], drug-target interaction (DTI) predictions [5-8], and drug-drug interaction (DDI) predictions [9-11].”
A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome
Misselbeck, K., Parolo, S., Lorenzini, F., Savoca, V., Leonardelli, L., Bora, P., Morine, M. J., Mione, M., Domenici, E., & Priami, C. (2019) · Nature Communications · doi:10.1038/s41467-019-13208-z · Cited by 73 · see citing works
Comprehensive germline genomic profiles of children, adolescents and young adults with solid tumors
Akhavanfard, S., Padmanabhan, R., Yehia, L., Cheng, F., & Eng, C. (2020) · Nature Communications · doi:10.1038/s41467-020-16067-1 · Cited by 71 · see citing works
“Other solid tumor (10) 3 4 3 10 Non-malignant tumor (4) 3 1 4”
Self-citationHigh-throughput target trial emulation for Alzheimer’s disease drug repurposing with real-world data
Zang, C., Zhang, H., Xu, J., Zhang, H., Fouladvand, S., Havaldar, S., Cheng, F., Chen, K., Chen, Y., Glicksberg, B. S., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43929-1 · Cited by 69 · see citing works
“These variabilities can become big challenges for existing observational studies that use a single controlgroup 41 orasinglewayofbuildingmultiplecontrolgroups(e.g. only random control groups) 7 .”
Self-citationWu, 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].”
BackgroundAmbati, J., Magagnoli, J., Leung, H., Wang, S.-B., Andrews, C. A., Fu, D., Pandey, A., Sahu, S., Narendran, S., Hirahara, S., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18528-z · Cited by 66 · see citing works
Host-directed therapy, an untapped opportunity for antimalarial intervention
Wei, L., Adderley, J., Leroy, D., Drewry, D. H., Wilson, D. W., Kaushansky, A., & Doerig, C. (2021) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100423 · Cited by 62 · see citing works
“In addition to their therapeutic applications in cancers and coronary artery disease, 88,89 network-based approaches have facilitated drug discovery and repurposing to treat infectious diseases such as pandemic and seasonal respiratory infections by redirecting the usage of known drugs to target…”
Signature-based approaches for informed drug repurposing: targeting CNS disorders
Shukla, R., Henkel, N., Alganem, K., Hamoud, A.-R. A., Reigle, J. K., Alnafisah, R. S., Eby, H., Imami, A. S., Creeden, J. F., Miruzzi, S. A., et al. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0752-6 · Cited by 65 · see citing works
“While comparison of across studies and species is a common practice for in silico approaches, the most rigorous in silico validation could include using large-scale patient-level routine health care data involving the repositioned drug [141].”
BackgroundAhmed, F., Lee, J. W., Samantasinghar, A., Kim, Y. S., Kim, K. H., Kang, I. S., Memon, F. H., Lim, J., & Choi, K. H. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.902123 · Cited by 65 · see citing works
Wang, B., Zhang, T., Liu, Q., Sutcharitchan, C., Zhou, Z., Zhang, D., & Li, S. (2024) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2024.101144 · Cited by 68 · see citing works
“By quantifying the network proximity of disease genes and drug targets within the human protein-protein interactome, Cheng et al. [38] identify hundreds of new drug-disease associations for over 900 U.S. Food and Drug Administration (U.S. FDA)-approved drugs.”
A computational approach to drug repurposing using graph neural networks
Doshi, S., & Chepuri, S. P. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105992 · Cited by 63 · see citing works
Systems Biology Approaches to Understanding the Human Immune System
Dhillon, B. K., Smith, M. L., Baghela, A. S., Lee, A. H., & Hancock, R. E. W. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01683 · Cited by 62 · see citing works
“As such, along with numerous other studies [e.g., (89–92)], it has been shown that integrating omics datasets using resources such as InnateDB and DrugBank can reveal potential drug targets for improved therapies.”
ResultTopological network measures for drug repositioning
Badkas, A., Landtsheer, S. D., & Sauter, T. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa357 · Cited by 61 · see citing works
“This approach was used to suggest associations between several approved drugs and diseases, validating two of them via patient records and in vitro experiments [79].”
MethodCheng, 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
Self-citationPersonal Mutanomes Meet Modern Oncology Drug Discovery and Precision Health
Cheng, F., Liang, H., Butte, A. J., Eng, C., & Nussinov, R. (2018) · Pharmacological Reviews · doi:10.1124/pr.118.016253 · Cited by 58 · see citing works
Self-citationLoucera, C., Esteban‐Medina, M., Rian, K., Falco, M. M., Dopazo, J., & Peña-Chilet, M. (2020) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00417-y · Cited by 59 · see citing works
“In recent years, approaches based on network biology have demonstrated to be superior to genecentric ones.(1) Here, we use an innovative methodology that combines mechanistic modeling of the signal transduction circuits related to SARS-CoV-2 infection (the COVID-19 disease map) with a machine-learning algorithm that l…”
MethodClinical Knowledge Graph Integrates Proteomics Data into Clinical Decision-MakingPreprint
Santos, A., Colaço, A. R., Nielsen, A. B., Niu, L., Geyer, P. E., Coscia, F., Albrechtsen, N. J. W., Mundt, F. O., Jensen, L. J., & Mann, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.09.084897 · Cited by 59 · see citing works
“10 interaction (PPI) prediction, disease comorbidity risks or diet-based cancer therapy associations (Cheng et al., 2018; Halu et al., 2019; Menche et al., 2015; Veselkov et al., 2019).”
BackgroundLoscalzo, J. (2022) · The FASEB Journal · doi:10.1096/fj.202201683r · Cited by 55 · see citing works
Self-citationPredicting Herb-disease Associations Through Graph Convolutional Network
Hu, X., Lu, Y., Tian, G., Bing, P., Wang, B., & He, B. (2023) · Current Bioinformatics · doi:10.2174/1574893618666230504143647 · Cited by 56 · see citing works
Infante, T., Viscovo, L. D., Rimini, M. L. D., Padula, S., Caso, P., & Claudio Napoli, A. (2019) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.52407 · Cited by 56 · see citing works
Mapping the perturbome network of cellular perturbations
Caldera, M., Müller, F., Kaltenbrunner, I., Licciardello, M. P., Lardeau, C.-H., Kubicek, S., & Menche, J. (2019) · Nature Communications · doi:10.1038/s41467-019-13058-9 · Cited by 56 · 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
“We re-computed the kernel-based scores using the recently released HuRI PPI 50 as well as the interactome compiled by Cheng et al. 30 Results are presented in Note S7.”
Lv, S., Wang, Q., Zhang, X., Ning, F., Liu, W., Cui, M., & Xu, Y. (2023) · Phytomedicine · doi:10.1016/j.phymed.2023.155231 · Cited by 55 · see citing works
Smith, I. N., Thacker, S., Seyfi, M., Cheng, F., & Eng, C. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.03.009 · Cited by 54 · see citing works
Self-citationA new precision medicine initiative at the dawn of exascale computing
Nussinov, R., Jang, H., Nir, G., Tsai, C., & Cheng, F. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00420-3 · Cited by 54 · see citing works
Self-citationMa, C., Yan, K., Wang, Z., Zhang, Q., Gao, L., Xu, T., Sai, J., Cheng, F., & Du, Y. (2021) · Bioengineered · doi:10.1080/21655979.2021.1933302 · Cited by 53 · see citing works
“The human protein-protein interaction (PPI) network was constructed from Professor Barabasi’s team [16], which was integrated with 15 commonly used protein-protein interactome databases and in-house data.”
MethodSelf-citationDissecting the m6A methylation affection on afatinib resistance in non-small cell lung cancer
Meng, Q., Wang, S., Zhou, S., Liu, H., Ma, X., Zhou, X., Liu, H., Xu, C., & Jiang, W. (2019) · The Pharmacogenomics Journal · doi:10.1038/s41397-019-0110-4 · Cited by 51 · see citing works
Results of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD—What, Why, and How
Fiocchi, C., Dragoni, G., Iliopoulos, D., Katsanos, K. H., Hernández, V., Suzuki, K., Verstockt, B., Fiocchi, C., Torres, J., & Scharl, M. M. (2021) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjab051 · Cited by 52 · see citing works
Lal, J. C., Mao, C., Zhou, Y., Gore-Panter, S. R., Rennison, J. H., Lovano, B. S., Castel, L., Shin, J., Gillinov, A. M., Smith, J. D., et al. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100749 · Cited by 47 · see citing works
“In prior work, we described a human 2 Cell Reports Medicine 3, 100749, October 18, 2022 protein-protein interactome generated from five types of PPIs.20,24,25,30,31 To depict the human interactome network, we gathered associated proteins from five levels of evidence: (1) binary PPIs tested by high-throughput yeast two…”
BackgroundResultInfluentialSelf-citationRepurposed drug candidates for antituberculosis therapy
An, Q., Li, C., Chen, Y., Deng, Y., Yang, T., & Luo, Y. (2020) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2020.112175 · Cited by 50 · see citing works
Integrated transcriptomic correlation network analysis identifies COPD molecular determinants
Paci, P., Fiscon, G., Conte, F., Licursi, V., Morrow, J. D., Hersh, C. P., Cho, M. H., Castaldi, P. J., Glass, K., Silverman, E. K., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-60228-7 · Cited by 49 · see citing works
Drug Repurposing: A Leading Strategy for New Threats and Targets
Mishra, A. S., Vasanthan, M., & Malliappan, S. P. (2024) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.3c00361 · Cited by 48 · see citing works
Drug Repurposing: A Network-based Approach to Amyotrophic Lateral Sclerosis
Fiscon, G., Conte, F., Amadio, S., Volonté, C., & Paci, P. (2021) · Neurotherapeutics · doi:10.1007/s13311-021-01064-z · Cited by 47 · see citing works
“The rationale behind SAveRUNNER lies in the hypothesis that, for a drug to be effective against a specific disease, its associated targets (drug module) and the disease-specific associated genes (disease module) should be nearby in the human interactome [21].”
BackgroundMethodInfluential
- Authors
- Cheng, F., Desai, R. J., Handy, D. E., Wang, R., Schneeweiss, S., Barabási, A.-L., & Loscalzo, J.
- Affiliations
- Northeastern University (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Loscalzo, Joseph
- Venue
- Nature Communications
- Published
- 2018-07-12
- Volume
- 9
- Issue
- 1
- Pages
- 2691-2691
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6043492
- OpenAlex
- W2826750344
- Cited by
- 552
- References
- 90
- Citations by year
- 2026: 47 · 2025: 66 · 2024: 60 · 2023: 58 · 2022: 88 · 2021: 97 · 2020: 86 · 2019: 43 · 2018: 5
- FWCI
- 43.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Open Research (Auckland University of Technology) (submittedVersion, cc-by)
- Funders
- U.S. Department of Health & Human Services | National Institutes of Health (HL106373, HG007690, GM 107618, HL016795, HL108630)
- Patient-Centered Outcomes Research Institute (ME-1303-5638)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- drug repurposing, protein-protein interaction network, network proximity, drug-disease associations, coronary artery disease, carbamazepine, hydroxychloroquine, pharmacoepidemiology, propensity score matching, endothelial inflammation, inflammatory cytokines, FDA-approved drugs
- MeSH terms
- Carbamazepine, Computer Simulation, Coronary Artery Disease, Humans, Hydroxychloroquine, Prognosis, Risk Factors, Reproducibility of Results, Proportional Hazards Models, Databases, Factual, Risk Assessment, Propensity Score, Drug Repositioning, Protein Interaction Maps
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