Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information

DrugBank 5.0: a major update to the DrugBank database for 2018

Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1037 · Cited by 8,404 · see citing works

KEGG for representation and analysis of molecular networks involving diseases and drugs

Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., & Hirakawa, M. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp896 · Cited by 2,322 · see citing works

Systems Analysis of Adverse Drug Events

Leape, L. L. (1995) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.1995.03530010049034 · Cited by 1,404 · see citing works

Trends in Prescription Drug Use Among Adults in the United States From 1999-2012

Kantor, E. D., Rehm, C. D., Haas, J. S., Chan, A. T., & Giovannucci, E. L. (2015) · JAMA · doi:10.1001/jama.2015.13766 · Cited by 1,219 · see citing works

Data-driven prediction of drug effects and interactions

Tatonetti, N. P., Ye, P. P., Daneshjou, R., & Altman, R. B. (2012) · Science Translational Medicine · doi:10.1126/scitranslmed.3003377 · Cited by 936 · see citing works

Clinical Pharmacokinetics and Pharmacodynamics of Tacrolimus in Solid Organ Transplantation

Staatz, C. E., & Tett, S. E. (2004) · Clinical Pharmacokinetics · doi:10.2165/00003088-200443100-00001 · Cited by 920 · see citing works

SNAP

Leskovec, J., & Sosič, R. (2016) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2898361 · Cited by 885 · see citing works

Deep learning improves prediction of drug–drug and drug–food interactions

Ryu, J. Y., Kim, H. U., & Lee, S. Y. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1803294115 · Cited by 662 · see citing works

Hospital Admissions Associated with Adverse Drug Reactions: A Systematic Review of Prospective Observational Studies

Kongkaew, C., Noyce, P., & Ashcroft, D. M. (2008) · Annals of Pharmacotherapy · doi:10.1345/aph.1l037 · Cited by 631 · see citing works

A multimodal deep learning framework for predicting drug–drug interaction events

Deng, Y., Xu, X., Qiu, Y., Xia, J., Zhang, W., & Liu, S. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa501 · Cited by 457 · see citing works

SemMedDB: a PubMed-scale repository of biomedical semantic predications

Halil Kilicoglu, B., Shin, D., Marcelo Fiszman, B., Rosemblat, G., & Rindflesch, T. C. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts591 · Cited by 374 · see citing works

Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties

Cheng, F., & Zhao, Z. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002512 · Cited by 374 · see citing works

Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data

Zhang, W., Chen, Y., Liu, F., Luo, F., Tian, G., & Li, X. (2017) · BMC Bioinformatics · doi:10.1186/s12859-016-1415-9 · Cited by 356 · see citing works

Drug—drug interaction through molecular structure similarity analysis

Vilar, S., Harpaz, R., Uriarte, E., Santana, L., Rabadán, R., & Friedman, C. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-000935 · Cited by 290 · see citing works

INDI: a computational framework for inferring drug interactions and their associated recommendations

Gottlieb, A., Stein, G. Y., Oron, Y., Ruppin, E., & Sharan, R. (2012) · Molecular Systems Biology · doi:10.1038/msb.2012.26 · Cited by 277 · see citing works

Metabolism of the immunosuppressant tacrolimus in the small intestine: cytochrome P450, drug interactions, and interindividual variability.

Lampen, A., Christians, U., Guengerich, F. P., Watkins, P. B., Kolars, J. C., Bader, A., Gonschior, A. K., Dralle, H., Hackbarth, I., & Sewing, K. ‐. (1995) · Drug Metabolism and Disposition · doi:10.1016/s0090-9556(25)06862-x · Cited by 273 · see citing works

Metabolism of Tacrolimus (FK506) and Recent Topics in Clinical Pharmacokinetics

Iwasaki, K. (2007) · Drug Metabolism and Pharmacokinetics · doi:10.2133/dmpk.22.328 · Cited by 264 · see citing works

Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects

Zhang, P., Wang, F., Hu, J., & Sorrentino, R. (2015) · Scientific Reports · doi:10.1038/srep12339 · Cited by 244 · see citing works

Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction

Wang, Y., Min, Y., Chen, X. W., & Wu, J. Y. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449786 · Cited by 203 · see citing works

Clinical Studies on Drug–Drug Interactions Involving Metabolism and Transport: Methodology, Pitfalls, and Interpretation

Tornio, A., Filppula, A. M., Niemi, M., & Backman, J. T. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1435 · Cited by 199 · see citing works

Computational prediction of drug-drug interactions based on drugs functional similarities

Ferdousi, R., Safdari, R., & Omidi, Y. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.04.021 · Cited by 192 · see citing works

Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity

Rohani, N., & Eslahchi, C. (2019) · Scientific Reports · doi:10.1038/s41598-019-50121-3 · Cited by 178 · see citing works

Dosing equation for tacrolimus using genetic variants and clinical factors

Passey, C., Birnbaum, A. K., Brundage, R. C., Oetting, W. S., Israni, A. K., & Jacobson, P. A. (2011) · British Journal of Clinical Pharmacology · doi:10.1111/j.1365-2125.2011.04039.x · Cited by 169 · see citing works

Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning

Kastrin, A., Ferk, P., & Leskošek, B. L. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196865 · Cited by 163 · see citing works

Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network

Karim, M. R., Cochez, M., Jares, J., Uddin, M., Beyan, O., & Decker, S. (2019) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3307339.3342161 · Cited by 161 · see citing works

Systematic Prediction of Pharmacodynamic Drug-Drug Interactions through Protein-Protein-Interaction Network

Huang, J., Niu, C., Green, C. D., Yang, L., Mei, H., & Han, J. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002998 · Cited by 156 · see citing works

Novel deep learning model for more accurate prediction of drug-drug interaction effects

Lee, G., Park, C., & Ahn, J. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3013-0 · Cited by 155 · see citing works

SFLLN: A sparse feature learning ensemble method with linear neighborhood regularization for predicting drug–drug interactions

Zhang, W., Jing, K., Huang, F. F., Chen, Y., -, L. B., Li, J., & Gong, J. (2019) · Information Sciences · doi:10.1016/j.ins.2019.05.017 · Cited by 148 · see citing works

A probabilistic approach for collective similarity-based drug–drug interaction prediction

Sridhar, D., Fakhraei, S., & Getoor, L. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw342 · Cited by 132 · see citing works

MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions

Xu, N., Wang, P., Chen, L., Tao, J., & Zhao, J. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/551 · Cited by 126 · see citing works

Rhabdomyolysis due to interaction of simvastatin with mibefradil

Schmassmann-Suhijar, D., Bullingham, R. E. S., Gasser, R., Schmutz, J., & Haefeli, W. E. (1998) · The Lancet · doi:10.1016/s0140-6736(05)78613-x · Cited by 118 · see citing works

Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization

Yu, H., Mao, K.-T., Shi, J., Huang, H., Chen, Z., Dong, K., & Yiu, S. (2018) · BMC Systems Biology · doi:10.1186/s12918-018-0532-7 · Cited by 110 · see citing works

Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings

Çelebi, R., Uyar, H. T., Yasar, E., Gümüş, Ö., Dikenelli, O., & Dumontier, M. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3284-5 · Cited by 107 · see citing works

Predicting Pharmacodynamic Drug-Drug Interactions through Signaling Propagation Interference on Protein-Protein Interaction Networks

Park, K., Kim, D., Ha, S., & Lee, D. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140816 · Cited by 91 · see citing works

A machine learning framework for predicting drug–drug interactions

Mei, S., & Zhang, K. (2021) · Scientific Reports · doi:10.1038/s41598-021-97193-8 · Cited by 83 · see citing works

VA National Drug File Reference Terminology: a cross-institutional content coverage study.

Brown, S. H., Elkin, P. L., Rosenbloom, S. T., Husser, C. S., Bauer, B. A., Lincoln, M. J., Carter, J. S., Erlbaum, M. S., & Tuttle, M. S. (2004) · PubMed · Cited by 82 · see citing works

Pharmacointeraction Network Models Predict Unknown Drug-Drug Interactions

Cami, A., Manzi, S. F., Arnold, A. D., & Reis, B. Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061468 · Cited by 79 · see citing works

KRGDB: the large-scale variant database of 1722 Koreans based on whole genome sequencing

Jung, K. S., Hong, K., Jo, H. Y., Choi, J., Ban, H., Cho, S. B., & Chung, M. (2020) · Database · doi:10.1093/database/baz146 · Cited by 77 · see citing works

INTRA‐ AND INTERINDIVIDUAL VARIATION IN THE PHARMACOKINETICS OF TACROLIMUS (FK506) IN KIDNEY TRANSPLANT RECIPIENTS—IMPORTANCE OF TROUGH LEVEL AS A PRACTICAL INDICATOR

Ihara, H., Shinkuma, D., Ichikawa, Y., Nojima, M., Nagano, S., & Ikoma, F. (1995) · International Journal of Urology · doi:10.1111/j.1442-2042.1995.tb00444.x · Cited by 74 · see citing works

Drug–drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings

Dai, Y., Guo, C., Guo, W., & Eickhoff, C. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa256 · Cited by 74 · see citing works

Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies

Song, D., Chen, Y., Min, Q., Sun, Q., Ye, K., Zhou, C., Yuan, S., Sun, Z., & Liao, J. (2018) · Journal of Clinical Pharmacy and Therapeutics · doi:10.1111/jcpt.12786 · Cited by 71 · see citing works

KRGDB: the large-scale variant database of 1722 Koreans based on whole genome sequencing

Jung, K. S., Hong, K., Jo, H. Y., Choi, J., Ban, H., Cho, S. B., & Chung, M. (2020) · Database · doi:10.1093/database/baaa030 · Cited by 70 · see citing works

Drug interaction between voriconazole and calcineurin inhibitors in allogeneic hematopoietic stem cell transplant recipients

Mori, T., Aisa, Y., Kato, J., Nakamura, Y., Ikeda, Y., & Okamoto, S. (2009) · Bone Marrow Transplantation · doi:10.1038/bmt.2009.38 · Cited by 65 · see citing works

GCN-BMP: Investigating graph representation learning for DDI prediction task

Chen, X. W., Liu, X., & Wu, J. Y. (2020) · Methods · doi:10.1016/j.ymeth.2020.05.014 · Cited by 57 · see citing works

Data-driven prediction of adverse drug reactions induced by drug-drug interactions

Liu, R., AbdulHameed, M. D. M., Kumar, K., Yu, X., Wallqvist, A., & Jaques Reifman, A. (2017) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-017-0153-6 · Cited by 55 · see citing works

A meta-learning framework using representation learning to predict drug-drug interaction

Deepika, S. S., & Geetha, T. V. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.06.015 · Cited by 55 · see citing works

Structural Network Embedding using Multi-modal Deep Auto-encoders for Predicting Drug-drug Interactions

Liu, S., Huang, Z., Qiu, Y., Chen, Y. P., & Zhang, W. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983337 · Cited by 38 · see citing works

Drug-Drug Interactions Prediction Based on Drug Embedding and Graph Auto-Encoder

Purkayastha, S., Mondal, I., Sarkar, S., Goyal, P., & Pillai, J. K. (2019) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2019.00104 · Cited by 37 · see citing works

Cardiac arrest in a young woman with the long QT syndrome and concomitant astemizole ingestion.

Broadhurst, P., & Nathan, A. W. (1993) · Heart · doi:10.1136/hrt.70.5.469 · Cited by 36 · see citing works

Evaluation of drug–drug interactions for oncology therapies: in vitro–in vivo extrapolation model‐based risk assessment

Waters, N. J. (2014) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.12563 · Cited by 31 · see citing works

Drug-drug Interaction Prediction with Graph Representation Learning

Chen, X. W., Liu, X., & Wu, J. Y. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983416 · Cited by 29 · see citing works

Predicting Drug-Drug Interactions Using Deep Neural Network

Hou, X., You, J., & Hu, P. (2019) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3318299.3318323 · Cited by 25 · see citing works

MTMA: Multi-task multi-attribute learning for the prediction of adverse drug–drug interaction

Zhu, J., Liu, Y., & Wen, C. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105978 · Cited by 23 · see citing works

Prediction of Side Effects Using Comprehensive Similarity Measures

Seo, S., Lee, T., Kim, M., & Yoon, Y. (2020) · BioMed Research International · doi:10.1155/2020/1357630 · Cited by 21 · see citing works

The Interplay between Pharmacokinetics and Pharmacodynamics

Sandritter, T. L., McLaughlin, M., Artman, M., & Lowry, J. A. (2017) · Pediatrics in Review · doi:10.1542/pir.2016-0101 · Cited by 20 · see citing works

Whole exome sequencing for the identification of CYP3A7 variants associated with tacrolimus concentrations in kidney transplant patients

Sohn, M., Kim, M. G., Han, N., Kim, I.-W., Gim, J., Min, S., Song, E. Y., Kim, Y. S., Jung, H. S., Shin, Y. K., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-36085-w · Cited by 19 · see citing works

Comparison of the Static In Vivo Approach to a Physiologically Based Pharmacokinetic Approach for Metabolic Drug–Drug Interactions Prediction

Tod, M. M., Pierrillas, P. B., Bourguignon, L., & Goutelle, S. (2016) · International Journal of Pharmacokinetics · doi:10.4155/ipk.16.2 · Cited by 16 · see citing works

A Generic Model for Quantitative Prediction of Interactions Mediated by Efflux Transporters and Cytochromes: Application to P-Glycoprotein and Cytochrome 3A4

Tod, M. M., Goutelle, S., Bleyzac, N., & Bourguignon, L. (2018) · Clinical Pharmacokinetics · doi:10.1007/s40262-018-0711-0 · Cited by 12 · see citing works

An Integrated Local Classification Model of Predicting Drug-Drug Interactions via Dempster-Shafer Theory of Evidence

Shi, J., Shang, X., Gao, K., Zhang, S., & Yiu, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-30189-z · Cited by 10 · see citing works

CNN-DDI: A novel deep learning method for predicting drug-drug interactions

Zhang, C., & Zang, T. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313404 · Cited by 10 · see citing works

Quantitative Prediction of Interactions Mediated by Transporters and Cytochromes: Application to Organic Anion Transporting Polypeptides, Breast Cancer Resistance Protein and Cytochrome 2C8

Tod, M. M., Bourguignon, L., Bleyzac, N., & Goutelle, S. (2019) · Clinical Pharmacokinetics · doi:10.1007/s40262-019-00853-2 · Cited by 8 · see citing works

Drug-drug Interaction Prediction with Common Structural Patterns

Zhang, J., Yang, X., & Qian, Y. (2021) · doi:10.1109/ijcnn52387.2021.9533382 · Cited by 2 · see citing works

AttentionDDI: Siamese attention-based deep learning method for drug–drug interaction predictions

Schwarz, K., Allam, A., Gonzalez, N. A. P., & Krauthammer, M. O. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04325-y

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

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

Learning motif-based graphs for drug–drug interaction prediction via local–global self-attention

Zhong, Y., Li, G., Ji, Y., Zheng, H., Yu, Y., Zhang, J., Luo, H., Wang, B., & Weng, Z. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00888-6 · Cited by 63 · see citing works

Artificial intelligence generates novel 3D printing formulations

Elbadawi, M., Li, H., Sun, S., Alkahtani, M. E., Basit, A. W., & Gaisford, S. (2024) · Applied Materials Today · doi:10.1016/j.apmt.2024.102061 · Cited by 52 · see citing works

Pharmacokinetic and Pharmacodynamic Drug–Drug Interactions: Research Methods and Applications

Sun, L., Mi, K., Hou, Y. J., Hui, T., Zhang, L., Tao, Y., Liu, Z., & Huang, L. (2023) · Metabolites · doi:10.3390/metabo13080897 · Cited by 49 · see citing works

Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug–drug interaction prediction

Gill, J., Moullet, M., Martinsson, A., Miljković, F. N., Williamson, B., Arends, R. H., & Reddy, V. P. (2022) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12870 · Cited by 40 · see citing works

Prediction of drug permeation through microneedled skin by machine learning

Yuan, Y., Han, Y., Yap, C. W., Kochhar, J. S., Li, H., Xiang, X., & Kang, L. (2023) · Bioengineering & Translational Medicine · doi:10.1002/btm2.10512 · Cited by 39 · see citing works

Artificial Intelligence-Driven Innovations in Oncology Drug Discovery: Transforming Traditional Pipelines and Enhancing Drug Design

Albani, F. G., Alghamdi, S. S., Almutairi, M. M., & Alqahtani, T. (2025) · Drug Design Development and Therapy · doi:10.2147/dddt.s509769 · Cited by 42 · see citing works

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

Agu, P. C., & Obulose, C. N. (2024) · Drug Development Research · doi:10.1002/ddr.22159 · Cited by 33 · see citing works

Recent trends and perspectives of artificial intelligence-based machine learning from discovery to manufacturing in biopharmaceutical industry

Maharjan, R., Lee, J., Lee, K., Han, H., Kim, K. H., & Jeong, S. H. (2023) · Journal of Pharmaceutical Investigation · doi:10.1007/s40005-023-00637-8 · Cited by 31 · see citing works

New revolution for quality control of TCM in industry 4.0: Focus on artificial intelligence and bioinformatics

Li, Y., Fan, J., Cheng, X., Jin, H., Wang, Y., Wei, F., An, F., & Ma, S. (2024) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2024.118023 · Cited by 28 · see citing works

An accurate prediction of drug–drug interactions and side effects by using integrated convolutional and BiLSTM networks

Ali, S., Alam, W., Tyara, H., & Chong, K. T. (2024) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2024.105304 · Cited by 23 · see citing works

Artificial Intelligence in Biomedical Engineering and Its Influence on Healthcare Structure: Current and Future Prospects

Tripathi, D., Hajra, K., Mulukutla, A., Shreshtha, R., & Maity, D. (2025) · Bioengineering · doi:10.3390/bioengineering12020163 · Cited by 22 · see citing works

MecDDI: Clarified Drug–Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug–Drug Interaction Prediction

Hu, W., Zhang, W., Zhou, Y., Luo, Y., Sun, X., Xu, H., Shi, S., Li, T., Xu, Y., Yang, Q., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01656 · Cited by 21 · see citing works

A hierarchical attention network integrating multi-scale relationship for drug response prediction

Wang, X., Wen, Y., Zhang, Y., Dai, C., Yang, Y., Bo, X., He, S., & Peng, S. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102485 · Cited by 21 · see citing works

Insights into artificial intelligence utilisation in drug discovery

Hajal, A. A., & Meslamani, A. Z. A. (2024) · Journal of Medical Economics · doi:10.1080/13696998.2024.2315864 · Cited by 20 · see citing works

Artificial Intelligence: Future Aspects in the Pharmaceutical Industry an Overview

Bairagi, A., Singhai, A., & Jain, A. (2024) · Asian Journal of Pharmacy and Technology · doi:10.52711/2231-5713.2024.00039 · Cited by 5 · see citing works

Barriers to Implementing Effective Healthcare Practices for the Aging Population: Approaches to Identification and Management

Theodorakis, N., Kollia, Z., Christodoulou, M., Nella, I., Spathara, A., Athinaou, S., Triantafylli, G., Hitas, C., Anagnostou, D., Kreouzi, M., et al. (2025) · Cureus · doi:10.7759/cureus.79590 · Cited by 17 · see citing works

Pharmacokinetics–Pharmacodynamics Modeling for Evaluating Drug–Drug Interactions in Polypharmacy: Development and Challenges

Zhao, D., Huang, P. C., Yu, L., & He, Y. T. (2024) · Clinical Pharmacokinetics · doi:10.1007/s40262-024-01391-2 · Cited by 14 · see citing works

A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review

Marzouk, N. H., Selim, S., Elattar, M., Mabrouk, M. S., & Mysara, M. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01093-2 · Cited by 11 · see citing works

A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application

Malarvannan, M., Monohar, S., Kate, S. S., Taneja, I., Jaiswal, S., Pratap, B., Rathi, P. C., Thakur, S., Paul, D., & Wahajuddin, M. (2026) · Drug Metabolism and Disposition · doi:10.1016/j.dmd.2026.100240 · Cited by 10 · see citing works

CA-SQBG: Cross-attention guided Siamese quantum BiGRU for drug-drug interaction extraction

Zhang, T., Yu, C.-Q., & Zhang, S. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109655 · Cited by 10 · see citing works

MTrans: M-Transformer and Knowledge Graph-Based Network for Predicting Drug–Drug Interactions

Wu, S., Liu, B., Zhang, X., Shao, X., & Lin, C. (2024) · Electronics · doi:10.3390/electronics13152935 · Cited by 8 · see citing works

AI in Clinical Trials and Drug Development: Challenges and Potential Advancements

Gupta, D., Wal, P., Wal, A., R, S., Kumar, M., Panda, K. C., & Sharma, M. C. (2024) · Current Drug Discovery Technologies · doi:10.2174/0115701638314252241016165345 · Cited by 8 · see citing works

Evolution of human pharmacokinetics prediction methods: from allometry to artificial intelligence

Choi, N., Shin, B. S., & Shin, S. (2026) · Journal of Pharmaceutical Investigation · doi:10.1007/s40005-026-00805-6 · Cited by 8 · see citing works

PillHarmonics: An Orchestrated Pharmacogenetics Medication Clinical Decision Support Service

Dolin, R. H., Shenvi, E. C., Alvarez, C., Barrows, R. C., Boxwala, A. A., Lee, B., Nathanson, B. H., Kleyner, Y., Hagemann, R., Hongsermeier, T., et al. (2024) · Applied Clinical Informatics · doi:10.1055/a-2274-6763 · Cited by 8 · see citing works

Drugs inhibition prediction in P-gp enzyme: a comparative study of machine learning and graph neural network

Maryam, M., Rehman, M. U., Chong, K. T., & Tayara, H. (2025) · Computational Toxicology · doi:10.1016/j.comtox.2025.100344 · Cited by 7 · see citing works

Acercando la inteligencia artificial a los servicios de farmacia hospitalaria

González-Pérez, Y., Delgado, A. M., & Sesmero, J. M. M. (2024) · Farmacia Hospitalaria · doi:10.1016/j.farma.2024.02.007 · Cited by 12 · see citing works

Kolmogorov-Arnold networks for predicting drug-gene associations of HDAC1 inhibitors in periodontitis

Yadalam, P. K., Ayyachamy, S., Barbosa, F. T., & Natarajan, P. M. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108451 · Cited by 6 · see citing works

Machine Learning Applications in Drug Discovery

Ugurlu, S. Y., McDonald, D., & He, S. (2025) · French-Ukrainian Journal of Chemistry · doi:10.17721/fujcv13i1p1-66 · Cited by 6 · see citing works

Advancing Drug–Drug Interaction Prediction with Biomimetic Improvements: Leveraging the Latest Artificial Intelligence Techniques to Guide Researchers in the Field

Marqas, R. B., Simó, Z., Mousa, A., Fatih, Ö., & Iantovics, L. B. (2026) · Biomimetics · doi:10.3390/biomimetics11010039 · Cited by 5 · see citing works

Analysis and identification of drug similarity through drug side effects and indications data

Torab-Miandoab, A., Asghari, M. P., Hashemzadeh, N., & Ferdousi, R. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02133-3 · Cited by 5 · see citing works

A novel deep sequential learning architecture for drug drug interaction prediction using DDINet

Halder, A., Saha, B., Roy, M., & Majumder, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-93952-z · Cited by 5 · see citing works

Next-generation experimental and computational strategies for drug-drug interaction prophecy

Kanukolanu, A., Nalla, L. V., & Gajula, S. N. R. (2025) · Drug Metabolism and Disposition · doi:10.1016/j.dmd.2025.100150 · Cited by 4 · see citing works

A Systematic Review on Drug-to-Drug Interaction Prediction and Cryptographic Mechanism for Secure Drug Discovery Using AI Techniques

Sharmila, K. S., Revathi, S. T., & Pokkuluri, K. S. (2024) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213024500039 · Cited by 4 · see citing works

IIB-DDI: Invariant Information Bottleneck Theory for Out-of-Distribution Drug-Drug Interaction Prediction

Zhang, J., Zhang, S., Li, X., Wu, D., Wang, S., Li, L., Du, W., & Wang, Y. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3543884 · Cited by 3 · see citing works

[Translated article] Introducing artificial intelligence to hospital pharmacy departments

González-Pérez, Y., Delgado, A. M., & Sesmero, J. M. M. (2024) · Farmacia Hospitalaria · doi:10.1016/j.farma.2024.04.001 · Cited by 6 · see citing works

Artificial Intelligence and Machine Learning in Integrated Diagnostic

Milan, L. (2023) · Springer eBooks · doi:10.1007/978-3-031-35213-3_2 · Cited by 4 · see citing works

Comparison of classical Machine Learning-based algorithms to predict Triplex Forming Oligonucleotides

Hincapié-López, M., Vrebosch, J., García-Zapirain, B., Pinzón-Reyes, E. H., & Pabón-Martínez, Y. V. (2024) · Computational and Structural Biotechnology Reports · doi:10.1016/j.csbr.2024.100013 · Cited by 3 · see citing works

Future Trends and Challenges in Pharmaceutical Microbiology with the Integration of Artificial Intelligence

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

Accelerating Pharmaceutical Innovation Through AI: A Systematic Review of Machine Learning and Deep Learning Technologies for Drug-Drug Interaction Prediction

BARUT, Z., & Bilgin, T. T. (2026) · IEEE Access · doi:10.1109/access.2026.3670441 · Cited by 2 · see citing works

Recent Knowledge in Drug Design and Development: Automation and Advancement

Gurung, K., Mishra, S. K., Chhetri, T., Roy, S., Balakrishnan, A., & Georrge, J. J. (2025) · doi:10.1002/9781394280735.ch9 · Cited by 2 · see citing works

Clinical Research and Artificial Intelligence: How AI Is Changing Clinical Research

Majid, Z., & Islam, M. S. (2025) · American Journal of Artificial Intelligence · doi:10.11648/j.ajai.20250901.17 · Cited by 1 · see citing works

Bridging the gap between model performance and clinical practice: A scoping review of artificial intelligence models for drug–drug interaction prediction

Reta, D. Y., Mohammed, M., & Martini, N. D. (2026) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2026.08.002 · Cited by 1 · see citing works

How next generation digital technologies are transforming the pharmaceutical industry

Tsenov, S. (2022) · Annual for Hospital Pharmacy · doi:10.14748/ahp.v8i1.8608 · Cited by 1 · see citing works

Recommendation System Based on Clustering Techniques Using Collaborative Filtering Method

Mirthika, G. L. S., & Sivakumar, B. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-1726-6_3 · Cited by 1 · see citing works

Transcending Molecules: Paving the Way from Lab to Life in Drug Transport Innovation

Verma, A., Chauhan, A., & Awasthi, A. (2024) · Current Drug Targets · doi:10.2174/0113894501305312240414073623 · Cited by 1 · see citing works

NLP Applications—Other Biomedical Texts

Roberts, K. E., Xu, H., & Fushman, D. D. (2024) · Cognitive informatics in biomedicine and healthcare · doi:10.1007/978-3-031-55865-8_15

AI Doctors in Healthcare: A Comparative Journey through Diagnosis, Treatment, Care, Drug Development, and Health Analysis

Kaur, G., Singh, H., Mehta, S., & ., K. (2024) · International Conference on Computational Intelligence and Communication Technologies (CCICT) · doi:10.1109/ccict62777.2024.00081

KNN for Exploratory Drug Risk Analysis and Patient Well-Being: Enhanced Healthcare

Raj, V. A., Raman, V. R. M., & Shidaganti, G. I. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-7178-3_2

Predicting Drug-Drug Interaction Side Effects Using Label Propagation and Fire Hawk Optimization with Inceptionnet-Based Event Characterization

Kuma, M. (2025) · Journal of Information Systems Engineering & Management · doi:10.52783/jisem.v10i35s.6286

SMVSNN: An Intelligent Framework for Anticancer Drug–Drug Interaction Prediction Utilizing Spiking Multi-view Siamese Neural Networks

Tan, G., Liu, Y., Ye, W., Liang, Z., Lin, W., & Ding, F. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02205

The Multiple Approaches for Drug-Drug Interaction Extraction using Machine learning and transformer based ModelPreprint

Singh, G., & Thiyagarajan, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.25.684550

Catalyzing Drug Discovery Using Artificial Intelligence With Traditional Medicine

Taktaz, F., & Ghoran, S. H. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5258-9.ch002

Predicting Drug Responses Using Synthetic Populations and Machine Learning

Saran, S. L., Manaseswaran, S., Gowri, R., Kandavel, N., Palaniappan, K., & Saraswathi, V. (2025) · International Conference on Smart Structures and Systems (ICSSS) · doi:10.1109/icsss66939.2025.11346350

Comparing Neural Networks and Naive Bayes in the Prediction of Drug Gene Interactions of Type 4 Collagenase for Gingival Epithelialization

Arya, S., Arumuganainar, D., Yadalam, P. K., & Ardila, C. M. (2026) · International Journal of Dentistry · doi:10.1155/ijod/7501904

Conception et évaluation d’un outil d’aide informatisée au bilan de médication

Mouazer, A. (2023) · doi:10.70675/7a7a83ccz143dz4582z8929zc9a1102eca25

Analysis of Drug-Drug Interactions Using Machine Learning

Thiab, S. H. (2025) · International Conference on Business Intelligence for Technology Innovation (ICBITI) · doi:10.1109/icbiti65527.2025.11500864

AI Regulations in Medical Devices

Khinvasara, T. (2026) · Springer handbooks · doi:10.1007/978-3-032-01636-2_40

Interpretable Models for Pharmacokinetic Interaction Magnitude Using Enzyme, Transporter, and Dose Features

Benali, A., Boudiaf, K., & Touati, S. (2025) · Pharmacophore · doi:10.51847/x9byggpsgq

Deciphering Mechanistic Signatures in Drug-Drug Interactions with Dual Topology GraphsPreprint

Ma, W., Bi, X., Jiang, H., Lu, W., Nie, J., Lin, S., Lin, J., Wei, Z., Zhang, H., & Zhang, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.23.753684

Protocolo de optimización farmacoterapéutica y deprescripción

Chumbes, L. D. M., Rodríguez-Miñón, I., Alcalde, E. M., Guevara, L. M. B., Caro, M. C. D. A., & Pascual, D. (2026) · Medicine - Programa de Formación Médica Continuada Acreditado · doi:10.1016/j.med.2026.09.007

A semantic-aware knowledge graph neural network for metabolism-mediated drug–drug interaction prediction

Lin, W., Zhu, G., & Niu, Y. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.115050

Improved Label Propagation Model to Predict Drug - drug Interactions

Yan, Z., Zhao, L., Wei, X., & Zhang, Q. (2018) · MATEC Web of Conferences · doi:10.1051/matecconf/201817302007 · Cited by 2 · see citing works

Drug-drug interactions: A machine learning approach

Mensah, W. W. (2011) · doi:10.33915/etd.4753

DDI-HierPred: An Artificial Intelligence-Based Hierarchical PK/PD Platform for Drug–Drug Interaction Prediction

Ouassaf, M., & Alhatlani, B. Y. (2026) · Pharmaceutics · doi:10.3390/pharmaceutics18081042

Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects

Zhang, P., Wang, F., Hu, J., & Sorrentino, R. (2015) · Scientific Reports · doi:10.1038/srep12339 · Cited by 244 · see citing works

Network based prediction of drug-drug interactions

Gunawardena, S. D. L., Weerasinghe, A. R., & Wijesinghe, M. W. A. C. R. (2017) · International Conference on Advances in ICT for Emerging Regions · doi:10.1109/icter.2017.8257794 · Cited by 1 · see citing works