Publisher Correction: Network-based prediction of drug combinations

Artificial intelligence in COVID-19 drug repurposing

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A foundation model for clinician-centered drug repurposing

Huang, K., Chandak, P., Wang, Q., Havaldar, S., Vaid, A., Leskovec, J., Nadkarni, G. N., Glicksberg, B. S., Gehlenborg, N., & Zitnik, M. (2024) · Nature Medicine · doi:10.1038/s41591-024-03233-x · Cited by 249

MatchMaker: A Deep Learning Framework for Drug Synergy Prediction

Kuru, H. İ., Taştan, Ö., & Çiçek, A. E. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3086702 · Cited by 192

A review of artificial intelligence applications for antimicrobial resistance

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Network-based modeling of herb combinations in traditional Chinese medicine

Wang, Y., Yang, H., Chen, L., Jafari, M., & Tang, J. P. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab106 · Cited by 160

A Comprehensive Review of Computational Methods For Drug-Drug Interaction Detection

Qiu, Y., Zhang, Y., Deng, Y., Liu, S., & Zhang, W. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3081268 · Cited by 128

GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction

Yang, J., Xu, Z., Wu, W. K. K., Chu, Q., & Zhang, Q. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab162 · Cited by 121

Machine learning approaches for drug combination therapies

Paltun, B. G., Samuel Kaski, A., & Mamitsuka, H. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab293 · Cited by 108

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 108

Exploring the mechanism of TCM formulae in the treatment of different types of coronary heart disease by network pharmacology and machining learning

Yang, J., Tian, S., Zhao, J., & Zhang, W. (2020) · Pharmacological Research · doi:10.1016/j.phrs.2020.105034 · Cited by 106

Multi-way relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction

Liu, X., Song, C., Liu, S., Li, M., Zhou, X., & Zhang, W. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac579 · Cited by 102

Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing

Wang, Z., Zhou, M., & Arnold, C. W. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa437 · Cited by 91

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network

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DeepTraSynergy: drug combinations using multimodal deep learning with transformers

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Next-generation drug repurposing using human genetics and network biology

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The Clinical Kinase Index: A Method to Prioritize Understudied Kinases as Drug Targets for the Treatment of Cancer

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Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside

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Multitask joint strategies of self-supervised representation learning on biomedical networks for drug discovery

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Comprehensive germline genomic profiles of children, adolescents and young adults with solid tumors

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Drug–drug similarity measure and its applications

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Current and prospective computational approaches and challenges for developing COVID-19 vaccines

Hwang, W., Lei, W., Katritsis, N. M., MacMahon, M., Chapman, K. L., & Han, N. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.02.004 · Cited by 51

KGANSynergy: knowledge graph attention network for drug synergy prediction

Zhang, G., Gao, Z., Yan, C., Wang, J., Liang, W., Luo, J., & Luo, H. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad167 · Cited by 48

DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer’s disease

Li, V. O. K., Yang, H., Kaistha, T., Zhang, Q., Downey, J., Gozes, I., & Lam, J. C. K. (2025) · Scientific Reports · doi:10.1038/s41598-025-85947-7 · Cited by 60

Deep learning and multi-omics approach to predict drug responses in cancer

Wang, C., Lye, X., Kaalia, R., Kumar, P., & Rajapakse, J. C. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04964-9 · Cited by 48

MEGACare: Knowledge-guided multi-view hypergraph predictive framework for healthcare

Wu, J., He, K., Mao, R., Li, C., & Cambria, E. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101939 · Cited by 51

An enhanced cascade-based deep forest model for drug combination prediction

Lin, W., Wu, L., Zhang, Y., Wen, Y., Yan, B., Dai, C., Liu, K., He, S., & Bo, X. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab562 · Cited by 44

Signal Detection in Pharmacovigilance: A Review of Informatics-driven Approaches for the Discovery of Drug-Drug Interaction Signals in Different Data Sources

Ibrahim, H. E., Abdo, A., Kerdawy, A. M. E., & Eldin, A. S. (2021) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2021.100005 · Cited by 43

Network-based drug repurposing for HPV-associated cervical cancer

Ahmed, F., Yang, Y. J., Samantasinghar, A., Kim, Y., Ko, J.-B., & Choi, K. H. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.10.038 · Cited by 41

Similarity-based machine learning framework for predicting safety signals of adverse drug–drug interactions

Ibrahim, H. E., Kerdawy, A. M. E., Abdo, A., & Eldin, A. S. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100699 · Cited by 39

Systems Theory-Driven Framework for AI Integration into the Holistic Material Basis Research of Traditional Chinese Medicine

Zeng, J., & Jia, X. (2024) · Engineering · doi:10.1016/j.eng.2024.04.009 · Cited by 46

Network-Based Analysis of Fatal Comorbidities of COVID-19 and Potential Therapeutics

Chakrabarty, B., Das, D., Bulusu, G., & Roy, A. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3075299 · Cited by 38

Deep learning identifies explainable reasoning paths of mechanism of action for drug repurposing from multilayer biological network

Yang, J., Li, Z., Wu, W. K. K., Shi, Y., Xu, Z., Chu, Q., & Zhang, Q. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac469 · Cited by 41

Dual Attention and Patient Similarity Network for drug recommendation

Wu, J., Dong, Y., Gao, Z., Gong, T., & Li, C. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad003 · Cited by 37

A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer

Zhang, L., Fan, S., Vera, J., & Lai, X. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.037 · Cited by 37

CDCDB: A large and continuously updated drug combination database

Shtar, G., Azulay, L., Nizri, O., Rokach, L., & Shapira, B. (2022) · Scientific Data · doi:10.1038/s41597-022-01360-z · Cited by 34

Leveraging Multiple Types of Domain Knowledge for Safe and Effective Drug Recommendation

Wu, J., Qian, B., Li, Y., Gao, Z., Ju, M., Yang, Y., Zheng, Y., Gong, T., Li, C., & Zhang, X. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557380 · Cited by 34

Drug2ways: Reasoning over causal paths in biological networks for drug discovery

Rivas-Barragan, D., Mubeen, S., Guim, F., Hofmann‐Apitius, M., & Domingo‐Fernándéz, D. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008464 · Cited by 28

Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning

Mohammadi, E., Benfeitas, R., Türkez, H., Borén, J., Nielsen, J., Uhlén, M., & Mardinoğlu, A. (2020) · Cancers · doi:10.3390/cancers12092694 · Cited by 25

Drug-target interactions prediction based on network topology feature representation embedded deep forest

Lian, M., Wang, X., & Du, W. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126509 · Cited by 25

Optimized Combination of HDACI and TKI Efficiently Inhibits Metabolic Activity in Renal Cell Carcinoma and Overcomes Sunitinib Resistance

Rausch, M., Weiss, A., Zoetemelk, M., Piersma, S. R., Jiménez, C. R., B, J. R. V. B., & Nowak-Sliwinska, P. M. (2020) · Cancers · doi:10.3390/cancers12113172 · Cited by 24

Proteostasis and Proteotoxicity in the Network Medicine Era

Lualdi, M., Alberio, T., & Fasano, M. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21176405 · Cited by 24

Incorporating Clinical, Chemical and Biological Information for Predicting Small Molecule-microRNA Associations Based on Non-Negative Matrix Factorization

Luo, J., Shen, C., Lai, Z., Cai, J., & Ding, P. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.2975780 · Cited by 21

Network pharmacological prediction and molecular docking analysis of the combination of Atractylodes macrocephala Koidz. and Paeonia lactiflora Pall. in the treatment of functional constipation and its verification

Meng, Y., Li, X., Wang, X., Zhang, L., & Guan, J. (2022) · Animal Models and Experimental Medicine · doi:10.1002/ame2.12226 · Cited by 20

Medication Combination Prediction Using Temporal Attention Mechanism and Simple Graph Convolution

Wang, H., Wu, Y., Gao, C., Deng, Y., Zhang, F., Huang, J., & Liu, J. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3082548 · Cited by 21

Applications of machine learning approaches to combat COVID-19: A survey

Tiwari, S. M., Doğan, O., Jabbar, M. A., Shandilya, S. K., Ortiz, F., Bajpai, S., & Banerjee, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-99878-9.00014-5 · Cited by 19

BETA: a comprehensive benchmark for computational drug–target prediction

Zong, N., Li, N., Wen, A., Ngo, V., Yu, Y., Huang, M. S., Chowdhury, S., Jiang, C., Fu, S., Weinshilboum, R. M., et al. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac199 · Cited by 18

RECOVER identifies synergistic drug combinations in vitro through sequential model optimization

Bertin, P., Rector-Brooks, J., Sharma, D. K., Gaudelet, T., Anighoro, A., Groß, T., Martínez‐Peña, F., Tang, E. L., Suraj, M. S., Regep, C., et al. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100599 · Cited by 25

Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries

Masoudi‐Sobhanzadeh, Y., Salemi, A., Pourseif, M. M., Jafari, B., Omidi, Y., & Masoudi‐Nejad, A. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab113 · Cited by 18

Computational models, databases and tools for antibiotic combinations

Lv, J. Z., Liu, G., Hao, J., Ju, Y., Sun, B., & Sun, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac309 · Cited by 17

A Middle-Out Modeling Strategy to Extend a Colon Cancer Logical Model Improves Drug Synergy Predictions in Epithelial-Derived Cancer Cell Lines

Tsirvouli, E., Touré, V., Niederdorfer, B., Vázquez, M., Flobak, Å., & Kuiper, M. T. R. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.502573 · Cited by 14

Drug repositioning by merging active subnetworks validated in cancer and COVID-19

Lucchetta, M., & Pellegrini, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-99399-2 · Cited by 15

Integrating multi-source drug information to cluster drug-drug interaction network

Lv, J. Z., Liu, G., Ju, Y., Sun, B., Huang, H., & Sun, Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107088 · Cited by 16

A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks

Amiri, R., Razmara, J., Parvizpour, S., & Izadkhah, H. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05572-x · Cited by 19

Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes

Islam, M. B., Chowdhury, U. N., Nain, Z., Uddin, S., Ahmed, M. B., & Moni, M. A. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104668 · Cited by 15

Molecular Interaction Networks and Cardiovascular Disease Risk: The Role of Food Bioactive Small Molecules

Nasirian, F., & Menichetti, G. (2023) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.122.318332 · Cited by 13

AADB: A Manually Collected Database for Combinations of Antibiotics With Adjuvants

Lv, J. Z., Liu, G., Ju, Y., Huang, H., & Sun, Y. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3283221 · Cited by 14

DTSEA: A network-based drug target set enrichment analysis method for drug repurposing against COVID-19

Su, Y., Wu, J., Li, X., Li, J., Zhao, X., Pan, B., Huang, J., Kong, Q., & Han, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106969 · Cited by 14

Complex graph neural networks for medication interaction verification

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High-throughput screening for drug discovery targeting the cancer cell-microenvironment interactions in hematological cancers

Giri, A. K., & Ianevski, A. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2022.1991306 · Cited by 15

Bioinformatics and system biology approaches to identify molecular pathogenesis of polycystic ovarian syndrome, type 2 diabetes, obesity, and cardiovascular disease that are linked to the progression of female infertility

Hossain, M. A., Amin, M. A., Hasan, M. I., Sohel, M., Ahammed, M. A., Mahmud, S. M. H., Rahman, M. R., & Rahman, M. H. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.100960 · Cited by 13

Coadministration of Compound Danshen dripping pills and bezafibrate has a protective effect against diabetic retinopathy

Liu, L., Li, X., Cai, W., Guo, K., Shi, X., Tan, L., Yao, Z., Jing, X.-S., Wang, W., Zhou, S., et al. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.1014991 · Cited by 12

Automated and miniaturized screening of antibiotic combinations via robotic-printed combinatorial droplet platform

Shao, F., Li, H., Hsieh, K., Zhang, P., Li, S., & Wang, T. (2023) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2023.11.027 · Cited by 9

On the complexity of quantum link prediction in complex networks

Moutinho, J. P., Magano, D., & Coutinho, B. C. (2024) · Scientific Reports · doi:10.1038/s41598-023-49906-4 · Cited by 12

Baicalin: a potential therapeutic agent for acute kidney injury and renal fibrosis

Li, X., Xu, R., Zhang, D., Ji, C., Zhou, H., Song, T., Wang, X., Kong, Q., Li, L., Liu, Z., et al. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1511083 · Cited by 14

Computational modeling approaches and regulatory pathways for drug combinations

Fillinger, L., Walter, S., Ley, M., Kęska, K., Dehkordi, L. G., Kratochwill, K., & Perco, P. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104345 · Cited by 10

Recent Advances in System Based Study for Anti-Malarial Drug Development Process

Yadav, B. S., Chaturvedi, N., & Marina, N. (2019) · Current Pharmaceutical Design · doi:10.2174/1381612825666190902162105 · Cited by 10

Probing Synergistic Targets by Natural Compounds for Hepatocellular Carcinoma

Gao, J., Yin, Z., Wu, Z., Sheng, Z., Ma, C., Chen, R., Zhang, X., Tang, K., Fei, J., & Cao, Z. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.715762 · Cited by 9

Human/SARS-CoV-2 genome-scale metabolic modeling to discover potential antiviral targets for COVID-19

Wang, F., Chen, K., & Chu, S.-W. (2022) · Journal of the Taiwan Institute of Chemical Engineers · doi:10.1016/j.jtice.2022.104273 · Cited by 10

Identification of Drug Combination Therapies for SARS-CoV-2: A Molecular Dynamics Simulations Approach

Abdel‐Halim, H., Hajar, M., Hasouneh, L., & Abdelmalek, S. M. A. (2022) · Drug Design Development and Therapy · doi:10.2147/dddt.s366423 · Cited by 10

Pharmacological Approaches and Innovative Strategies for Individualized Patient Care

Verma, A. K., Singh, K., Gupta, J. K., Kumar, S., & Jain, D. (2025) · Recent Patents on Biotechnology · doi:10.2174/0118722083359334250116063638 · Cited by 11

Advancing network pharmacology with artificial intelligence: the next paradigm in traditional Chinese medicine

Shao, X., Chen, Y., Zhang, J., Zhang, X., Dai, Y., Peng, X., & Fan, X. (2025) · Chinese Journal of Natural Medicines · doi:10.1016/s1875-5364(25)60941-1 · Cited by 9

Artificial intelligence in traditional Chinese medicine: from systems biological mechanism discovery, real-world clinical evidence inference to personalized clinical decision support

Yan, D., Zheng, Q., Chang, K., Hua, R., Liu, Y., Xue, J., Shu, Z., Hu, Y., Yang, P., Wei, Y., et al. (2025) · Chinese Journal of Natural Medicines · doi:10.1016/s1875-5364(25)60983-6 · Cited by 8

Network medicine and artificial intelligence in cancer precision therapy: Path to prevent drug-induced toxic side effect

Das, A. B. (2024) · Current Opinion in Toxicology · doi:10.1016/j.cotox.2024.100476 · Cited by 8

CDCS-TCM: A framework based on complex network theory to analyze the causality and dynamic correlation of substances in the metabolic process of traditional Chinese medicine

Gao, J., Xiang, X., Yan, Q., et al. (2024) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2024.118100 · Cited by 7

Simultaneous determination of Rosuvastatin calcium and Bempedoic acid in fixed-dose combination pharmaceutical dosage forms using RP-HPLC method

Hadawale, S., & Sharma, S. (2024) · Microchemical Journal · doi:10.1016/j.microc.2024.111418 · Cited by 7

Network Medicine: From Conceptual Frameworks to Applications and Future Trends

Ayar, E. S., Dadmand, S., & Tunçbağ, N. (2023) · IEEE Transactions on Molecular Biological and Multi-Scale Communications · doi:10.1109/tmbmc.2023.3308689 · Cited by 9

Network Science and Machine Learning for Precision Nutrition

Sebek, M., & Menichetti, G. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15315-0.00012-2 · Cited by 6

DCGG: drug combination prediction using GNN and GAE

Ziaee, S. S., Rahmani, H., Tabatabaei, M., Vlot, A. H. C., & Bender, A. (2024) · Progress in Artificial Intelligence · doi:10.1007/s13748-024-00314-3 · Cited by 6

Predicting effective drug combinations for cancer treatment using a graph-based approach

Wang, Q., Liu, X., & Yan, G. (2024) · Synthetic and Systems Biotechnology · doi:10.1016/j.synbio.2024.09.003 · Cited by 6

A Transcriptome- and Interactome-Based Analysis Identifies Repurposable Drugs for Human Breast Cancer Subtypes

Conte, F., Sibilio, P., Fiscon, G., & Paci, P. (2022) · Symmetry · doi:10.3390/sym14112230 · Cited by 6

Identification of potential pan-coronavirus therapies using a computational drug repurposing platform

Hwang, W., & Han, N. (2021) · Methods · doi:10.1016/j.ymeth.2021.11.002 · Cited by 6

A Network-Based Analysis of Disease Modules From a Taxonomic Perspective

Grani, G., Madeddu, L., & Velardi, P. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3106787 · Cited by 6

Network analyses reveal new insights into the effect of multicomponent Tr14 compared to single-component diclofenac in an acute inflammation model

Hoch, M., Smita, S., Cesnulevicius, K., Schultz, M., Lescheid, D., Wolkenhauer, O., & Gupta, S. K. (2023) · Journal of Inflammation · doi:10.1186/s12950-023-00335-0 · Cited by 8

Discovery of the anti-influenza A virus activity of SB216763 and cyclosporine A by mining infected cells and compound cellular signatures

Tang, K., Wu, Y., Chen, S., Xin, Y., & Guo, Y. (2021) · Chinese Chemical Letters · doi:10.1016/j.cclet.2021.09.017 · Cited by 5

Predicting combinations of drugs by exploiting graph embedding of heterogeneous networks

Song, F., Tan, S., Dou, Z., Liu, X., & Ma, X. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04567-4 · Cited by 5

Potential senotherapeutic candidates and their combinations derived from transcriptional connectivity and network measures

Rad, A. N., Shams, G., Avelar, R. A., Morowvat, M. H., & Ghasemi, Y. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.100920 · Cited by 4

MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

Pio‐Lopez, L., Valdeolivas, A., Tichit, L., Rémy, É., & Baudot, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-87987-1 · Cited by 54

A comprehensive tool for tumor precision medicine with pharmaco-omics data analysis

Liu, Y., Song, F., Li, Z., et al. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1085765 · Cited by 4

Integration of metabolomics and network pharmacology for enhancing mechanism understanding and medication combination recommendation for diabetes mellitus and diabetic nephropathy

Xiao, M., Liu, W., Shi, X., Wu, J., Shen, G., & Feng, J. (2023) · Analytical Methods · doi:10.1039/d3ay00560g · Cited by 4

Time-stage driven pathfinding framework for optimized medical treatments

Gishyan, K., Sahakyan, H., & Aslanyan, L. H. (2023) · Cogent Engineering · doi:10.1080/23311916.2023.2249258 · Cited by 4

Whole genome analysis and homology modeling of SARS-CoV-2 Indian isolate reveals potent FDA approved drug choice for treating COVID-19

Velu, P., Rathinavel, T., Kumarasamy, S., et al. (2022) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2022.2038272 · Cited by 3

Universal multilayer network embedding reveals a causal link between GABA neurotransmitter and cancer

Pio‐Lopez, L., & Levin, M. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06158-5 · Cited by 4

Precision oncology informatics for anticancer drug combination responses: A systematic review

Rahaman, S., Wang, P., Yu, J., Rahman, T., Toseef, M., Sayem, I. M., Li, X., & Wong, K. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110788 · Cited by 3

Reconsideration of drug repurposing through artificial intelligence program for the treatment of the novel coronavirus

Gunturu, L. N., Dornadula, G., & Nimbagal, R. N. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90531-2.00009-6 · Cited by 3

Systems medicine framework for repurposable drug combinations for COVID-19 comorbidities

Saranya, S. V., Thamanna, L., & Chellapandi, P. (2024) · Medicine in Omics · doi:10.1016/j.meomic.2024.100038 · Cited by 3

Drug Repositioning Using Multiplex-Heterogeneous Network Embedding: A Case Study on SARS-CoV2

Pio‐Lopez, L. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-93413-2_60 · Cited by 3

Emerging Machine Learning Techniques in Predicting Adverse Drug Reactions

Zhong, Y., Wang, S., Li, G., Ji, Y., Weng, Z., & Luo, H. (2023) · Computational methods in engineering & the sciences · doi:10.1007/978-3-031-20730-3_3 · Cited by 2

Network Pharmacology and Modern Drug R&D Cases

Zhang, W., & Zhao, J. (2021) · Springer eBooks · doi:10.1007/978-981-16-0753-0_6 · Cited by 2

Advances in Network-Based Drug Repositioning

Pellegrini, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-55248-9_5 · Cited by 1

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