Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Front. Comput. Sci.    2025, Vol. 19 Issue (5) : 195902    https://doi.org/10.1007/s11704-024-31063-0
Interdisciplinary
Application of machine learning in drug side effect prediction: databases, methods, and challenges
Haochen ZHAO1,2, Jian ZHONG1,2, Xiao LIANG1,2, Chenliang XIE1,2, Shaokai WANG3()
1. School of Computer Science and Engineering, Central South University, Changsha 410083, China
2. Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
3. David R. Cheriton School of Computer Science, University of Waterloo, Waterloo ON N2L 3G1, Canada
 Download: PDF(4377 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Drug side effects have become paramount concerns in drug safety research, ranking as the fourth leading cause of mortality following cardiovascular diseases, cancer, and infectious diseases. Simultaneously, the widespread use of multiple prescription and over-the-counter medications by many patients in their daily lives has heightened the occurrence of side effects resulting from Drug-Drug Interactions (DDIs). Traditionally, assessments of drug side effects relied on resource-intensive and time-consuming laboratory experiments. However, recent advancements in bioinformatics and the rapid evolution of artificial intelligence technology have led to the accumulation of extensive biomedical data. Based on this foundation, researchers have developed diverse machine learning methods for discovering and detecting drug side effects. This paper provides a comprehensive overview of recent advancements in predicting drug side effects, encompassing the entire spectrum from biological data acquisition to the development of sophisticated machine learning models. The review commences by elucidating widely recognized datasets and Web servers relevant to the field of drug side effect prediction. Subsequently, The study delves into machine learning methods customized for binary, multi-class, and multi-label classification tasks associated with drug side effects. These methods are applied to a variety of representative computational models designed for identifying side effects induced by single drugs and DDIs. Finally, the review outlines the challenges encountered in predicting drug side effects using machine learning approaches and concludes by illuminating important future research directions in this dynamic field.
Keywords machine learning      drug side effects      computational models      databases      Web servers     
Corresponding Author(s): Shaokai WANG   
Just Accepted Date: 03 April 2024   Issue Date: 06 June 2024
 Cite this article:   
Haochen ZHAO,Jian ZHONG,Xiao LIANG, et al. Application of machine learning in drug side effect prediction: databases, methods, and challenges[J]. Front. Comput. Sci., 2025, 19(5): 195902.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-024-31063-0
https://academic.hep.com.cn/fcs/EN/Y2025/V19/I5/195902
Databases Latest update Introduction URL
SIDER [22] 2015/10 V4.1 A database containing information on marketed medicines and their recorded adverse drug reactions. See sideeffects.embl.de/ website
STITCH [23] 2016/01 V5 A database of known and predicted interactions between chemicals and proteins. See stitch.embl.de/ website
BIOSNAP [24] 2018 A biological network collecting various types of interactions between FDA-approved drugs. See snap.stanford.edu/biodata/ website
TWOSIDES [25] 2019/11 A database of DDIs safety signals mined from the FDA’s Adverse Event Reporting System. See tatonettilab.org/resources/nsides/ website
Offsides [25] 2019/11 A database of individual drug side effect signals mined from the FDA’s Adverse Event Reporting System. See tatonettilab.org/resources/nsides/ website
DDInter [26] 2020/09 V1.0 A comprehensive and open-access database providing abundant annotations for each DDI association. See ddinter.scbdd.com/ website
DailyMed [27] 2021/10 A database containing labeling submitted to the Food and Drug Administration (FDA) by companies. See dailymed.nlm.nih.gov/dailymed/ website
MecDDI [28] 2023/02 A database offering the mechanism underlying 110,000 DDIs by explicit description and graphic illustration. See mecddi.idrblab.net/ website
ADReCS [29] 2023/03 V3.2 A comprehensive drug side effect ontology database providing both standardization and hierarchical classification of drug side effect terms. See bioinf.xmu.edu.cn/ADReCS/index.jsp website
ChEMBL [30] 2023/05 V33 A manually curated database of bioactive molecules with drug-like properties, including chemical, bioactivity and genomic data. See ebi.ac.uk/chembl/ website
TTD [31] 2023/07 A database providing information about the therapeutic targets and corresponding drugs information. See db.idrblab.net/ttd/ website
KEGG [32] 2023/09 V108 A database resource for understanding high-level functions and utilities of the biological system. See genome.jp/kegg/drug/ website
PubChem [33] Quarterly update A database containing information physical properties and biological activities of the compounds. See pubchem.ncbi.nlm.nih.gov/ website
PharmGKB [34] Quarterly update A comprehensive resource that curates knowledge about the impact of genetic variation on drug response. See pharmgkb.org/ website
Drugs [35] Quarterly update A database providing accurate and independent information on more than 24,000 prescription drugs, over-the-counter medicines and natural products. See drugs.com/ website
UniProt [36] Quarterly update A database providing protein sequence and functional information. See uniprot.org/ website
FAERS [37] Quarterly update A database containing information on adverse event reports, medication error reports, and product quality complaints resulting in adverse events that were submitted to the FDA. See open.fda.gov/data/faers/ website
Tab.1  Databases for detecting drug side effects
Servers Year Introduction URL
DDI-CPI [38] 2014 A Web server can make real-time for predicting side effects caused by DDIs based only on molecular structure. See cpi.bio-x.cn/ddi/ website
NetInfer [39] 2020 A Web server for predicting targets, therapeutic and adverse effects of drugs via network-based inference methods. See lmmd.ecust.edu.cn/netinfer/ website
DDI-Preditor [40] 2021 A Web server dedicated to quantitative prediction of the impact on drug exposure of DDIs mediated by cytochromes P450 3A4, 2D6, 2C9, 2C19, and 1A2. See www.ddi-predictor.org/ website
BioChemDDI [41] 2022 A Web server for predicting DDI by fusing biochemical information and structural information through self-attention mechanism. See 120.77.11.78/BioChemDDI/ website
DeepLGF [42] 2022 A Web server for predicting DDI by using local-global information calculated based on biological knowledge graph. See 120.77.11.78/DeepLGF/ website
DDI-GCN [43] 2023 A Web server for predicting DDIs by utilizes graph convolutional networks (GCN) based on chemical structures. See wengzq-lab.cn/ddi/ website
MecDDI [28] 2023 A Web server for clarifying the mechanisms underlying >1,78,000 DDIs by explicit descriptions and graphic illustrations and providing a systematic classification for all collected DDIs based on their clarified mechanisms. See mecddi.idrblab.net/ website
Tab.2  Web servers for detecting drug side effects
Fig.1  Overview of drug–side effect association prediction based on machine learning algorithms
MethodYearRemarksValidationDatasetCode or softwareF1AUCPrecisionAUPRACCRecall
Zhao et al. [44]2018Traditional machine learning-based method10-fold cross-validationSIDERNo/0.849////
FGRMF [45]2018Matrix factorization-based method5-fold cross-validationSIDERYes0.4280.9490.4330.4130.9880.424
IC-PNM [46]2019Network-based methodtraining and test setsFAERSNo/0.910////
Liang et al. [47]2020Traditional machine learning-based method10-fold cross-validationSIDERNo0.959/0.997/0.9750.923
Ietswaart et al. [48]2020Traditional machine learning-based method5-fold cross-validationFAERSYes//////
TMF Guo et al. [49]2020Matrix factorization-based method5-fold cross-validationSIDERYes/0.943/0.680//
Dasgupta et al. [50]2021Knowledge graph-based method5-fold cross-validationOMOPNo0.9040.945////
Lee et al. [51]2021Deep learning-based methodtraining and test setsSIDERNo0.6640.8460.925/0.5140.518
Wu et al. [52]2022Traditional machine learning-based method10-fold cross-validationSIDERNo/0.969/0.977//
Yang et al. [53]2022Traditional machine learning-based method5-fold cross-validationSIDERNo/0.875////
Joshi et al. [54]2022Knowledge graph-based method5-fold cross-validationSIDERNo0.8370.9120.821/0.8320.857
MPGNN-DSA [55]2022Network-based method10-fold cross-validationLuo’s dataNo/0.936////
GCRS [56]2022Deep learning-based method5-fold cross-validationSIDERNo/0.957/0.272//
CLMF-NTK [57]2023Matrix factorization-based nethod5-fold cross-validationSIDERNo/0.949/0.678//
MSDSE [58]2023Deep learning-based method10-fold cross-validationSIDERYes0.609//0.676//
TCSD [59]2023Deep learning-based method5-fold cross-validationSIDERNo/0.977/0.351//
GGSC [60]2023Deep learning-based method5-fold cross-validationSIDERNo/0.969/0.340//
Tab.3  Methods for predicting drug-side effect associations with binary classification
Fig.2  Overview of multi-label drug side effect prediction based on machine learning algorithms
MethodYearRemarksValidationDatasetCode or softwareF1AUCPrecisionAUPRACCRecall
Liu et al. [63]2012Traditional machine learning-based method5-fold cross-validationSIDERNo/0.9520.662/0.9670.631
Yamanishi et al. [68]2012Kernel based method5-fold cross-validationSIDERNo///0.209//
Huang et al. [69]2013Traditional machine learning-based method10-fold cross-validationSIDERNo/0.700////
Cheng et al. [70]2013Network-based method10-fold cross- validationCTD, SIDER, OFFSIDESNo/0.902////
FS-MLKNN [71]2015Traditional machine learning-based method5-fold cross-validationSIDERYes/0.875/0.477//
Ngufor et al. [72]2015Traditional machine learning-based methodtraining and test setsSIDER, FAERSNo/0.800////
DSEP [73]2015Deep learning-based method5-fold cross-validationSIDERNo/0.893/0.414//
Rahmani et al. [74]2016Network-based methodLOOCVSIDERNo//////
Raja et al. [75]2017Traditional machine learning-based method10-fold cross-validationBiomedical literatureNo0.900/////
Bean et al. [76]2017Network-based method10-fold cross-validationSIDERYes/0.920////
Dey et al. [77]2018Deep learning-based method10-fold cross-validationSIDERNo////0.977/
SDHINE [78]2018Deep learning-based method10-fold cross-validationSIDER, OFFSIDESNo/0.841////
Ding et al. [79]2018Kernel based method5-fold cross-validationSIDERYes/0.951/0.655//
DeepSide [80]2019Deep learning-based method3-fold cross-validationSIDERYes/0.809////
Wang et al. [81]2019Deep learning-based method5-fold cross-validationSIDERNo/0.844////
Ding et al. [82]2019Kernel based method5-fold cross-validationSIDERNo/0.949/0.672//
Bongini et al. [83]2022Network-based methodtraining, validation and test sets in a 8:1:1 ratioSIDERYes////0.863/
Tab.4  Methods for predicting drug side effects with multi-label classification
Method Year Remarks Code or software AUC AUPR RMSE MAE PCC
Galeano’s model [87] 2020 Matrix factorization-based method Yes 0.907 0.216 1.298 0.953 0.478
MGPred [91] 2021 Deep learning-based method Yes 0.762 0.120 0.643 0.486 0.734
SDPred [92] 2022 Deep learning-based method Yes 0.919 0.230 0.593 0.433 0.781
DSGAT [93] 2022 Deep learning-based method Yes 0.917 0.243 1.031 0.754 0.557
NRFSE [94] 2023 Matrix factorization-based method Yes 0.926 0.272 1.008 0.767 0.580
GCAP [96] 2023 Deep learning-based method Yes 0.956 0.946 / / /
Tab.5  Machine learning methods for predicting frequencies and severity of drug side effects based on SIDER
Method Year Task type Remarks Validation Dataset Code or software Macro-F1 AUROC Macro-precision AUPR ACC Macro-Recall
DeepDDI [97] 2018 Multi-class Structure-based method training, validation and test sets Drugbank Yes / / / / 0.924 /
DDIMDL [22] 2019 Multi-class Structure-based method 5-fold cross-validation Drugbank Yes 0.759 0.998 0.847 0.921 0.885 0.7182
Hou et al. [98] 2019 Multi-class Structure-based method training, validation and test sets in a 6:2:2 ratio Drugbank No / 0.942 / / 0.932 /
SSI-DDI [99] 2021 Multi-class Structure-based method training, validation and test sets Drugbank Yes / 0.984 / 0.981 0.945 /
MDNN [100] 2021 Multi-class KG-based method 5-fold cross-validation Drugbank No 0.830 0.998 0.862 0.967 0.918 0.820
MUFFIN [101] 2021 Multi-class KG-based method 5-fold cross-validation Drugbank Yes 0.950 / 0.957 / 0.965 0.948
SumGNN [102] 2021 Multi-class KG-based method training, validation and test sets in a 7:1:2 ratio. Drugbank Yes 0.869 / / / 0.927 /
MUFFIN [101] 2021 Multi-label KG-based method 6-fold cross-validation TwoSides Yes / 0.916 / 0.703 / /
SumGNN [102] 2021 Multi-label KG-based method training, validation and test sets in a 7:1:2 ratio. TwoSides Yes / 0.949 / 0.934 / /
META-DDIE [103] 2022 Multi-class Structure-based method 5-fold cross-validation Drugbank Yes / / 0.816 / / /
MDDI-SCL [104] 2022 Multi-class Structure-based method 5-fold cross-validation Drugbank Yes 0.876 0.998 0.880 0.978 0.938 0.877
TBPM-DDIE [105] 2022 Multi-class Structure-based method 6-fold cross-validation Drugbank No 0.843 0.999 0.870 0.970 0.920 0.830
DM-DDI [106] 2022 Multi-class Network-based method 5-fold cross-validation Drugbank Yes 0.852 0.999 0.879 0.964 0.908 0.839
MDF-SA-DDI [107] 2022 Multi-class Network-based method 6-fold cross-validation Drugbank Yes 0.888 0.999 0.909 0.974 0.930 0.876
STNN-DDI [108] 2022 Multi-class Structure-based method 5-fold cross-validation Drugbank Yes / / / / / /
GMPNN-CS [84] 2022 Multi-class Structure-based method 3-fold cross-validation Drugbank Yes / 0.985 0.936 0.9794 0.953 0.972
LaGAT [109] 2022 Multi-class KG-based method 4-fold cross-validation Drugbank Yes 0.929 / / / 0.960 /
DeepMDDI [110] 2022 Multi-class Network-based method 5-fold cross-validation Drugbank Yes / 0.983 / 0.792 0.967 /
STNN-DDI [108] 2022 Multi-label Structure-based method 10-fold cross-validation TwoSides Yes / 0.955 0.852 0.921 0.897 /
GMPNN-CS [84] 2022 Multi-label Structure-based method 3-fold cross-validation TwoSides Yes / 0.901 0.784 0.872 0.828 0.837
R2-DDI [111] 2023 Multi-class Structure-based method training, validation and test sets in a 3:1:1 ratio Drugbank Yes 0.982 0.997 / 0.996 0.982 /
DSN-DDI [112] 2023 Multi-class Network-based method 4-fold cross-validation Drugbank Yes 0.969 0.995 / 0.994 0.969 /
ACDGNN [113] 2023 Multi-class Network-based method training, validation and test sets in a 6:2:2 ratio Drugbank Yes 0.941 0.988 0.956 0.984 0.967 0.937
MCFF-MTDDI [114] 2023 Multi-class KG-based method 5-fold cross-validation Drugbank Yes 0.955 0.998 0.972 0.976 0.977 0.946
R2-DDI [111] 2023 Multi-label Structure-based method training, validation and test sets in a 3:1:1 ratio TwoSides Yes 0.873 0.915 / 0.878 0.862 /
DSN-DDI [112] 2023 Multi-label Network-based method 3-fold cross-validation TwoSides Yes 0.988 0.999 / 0.999 0.988 /
MCFF-MTDDI [114] 2023 Multi-label KG-based method 4-fold cross-validation TwoSides Yes / 0.933 / 0.717 / /
Tab.6  Machine learning methods for predicting side effects caused by DDIs
Fig.3  Overview of side effect prediction resulting from DDIs using machine learning algorithms
  
  
  
  
  
1 N, Berdigaliyev M Aljofan . An overview of drug discovery and development. Future Medicinal Chemistry, 2020, 12( 10): 939–947
2 Sun D, Gao W, Hu H, Zhou S. Why 90% of clinical drug development fails and how to improve it? Acta Pharmaceutica Sinica B, 2022, 12(7): 3049−3062
3 Aronson J K. Adverse drug reactions: history, terminology, classification, causality, frequency, preventability. In: Talbot J, Aronson J K, eds. Stephens’ Detection and Evaluation of Adverse Drug Reactions: Principles and Practice. 6th ed. Chichester: John Wiley & Sons, Ltd, 2011, 1−119
4 D L Church . Major factors affecting the emergence and re-emergence of infectious diseases. Clinics in Laboratory Medicine, 2004, 24( 3): 559–586
5 A, Zullo M, Large E, Amoros J L Martin . Estimated number of seriously injured road users admitted to hospital in France between 2010 and 2017, based on medico-administrative data. BMC Public Health, 2021, 21: 649
6 Kinderen R J A, De S M A A, Evers R, Rinkens D, Postulart C I, Vader M H J M, Majoie A P Aldenkamp . Side-effects of antiepileptic drugs: the economic burden. Seizure, 2014, 23( 3): 184–190
7 R B, Mokhtari T S, Homayouni N, Baluch E, Morgatskaya S, Kumar B, Das H Yeger . Combination therapy in combating cancer. Oncotarget, 2017, 8( 23): 38022–38043
8 A, Rao H J Cohen . Symptom management in the elderly cancer patient: fatigue, pain, and depression. JNCI Monographs, 2004, 2004( 32): 150–157
9 A J, Forster J R, Worthington S, Hawken M, Bourke F, Rubens K, Shojania Walraven C Van . Using prospective clinical surveillance to identify adverse events in hospital. BMJ Quality & Safety, 2011, 20( 9): 756–763
10 D, Papaioannou C, Cooper C, Mooney R, Glover E Coates . Adverse event recording failed to reflect potential harms: a review of trial protocols of behavioral, lifestyle and psychological therapy interventions. Journal of Clinical Epidemiology, 2021, 136: 64–76
11 R, Bender L, Beckmann S Lange . Biometrical issues in the analysis of adverse events within the benefit assessment of drugs. Pharmaceutical Statistics, 2016, 15( 4): 292–296
12 A, Rácz D, Bajusz R A, Miranda-Quintana K Héberger . Machine learning models for classification tasks related to drug safety. Molecular Diversity, 2021, 25( 3): 1409–1424
13 K T, Call A A, Riedel K, Hein V, McLoyd A, Petersen M Kipke . Adolescent health and well-being in the twenty-first century: a global perspective. Journal of Research on Adolescence, 2002, 12( 1): 69–98
14 E A, Eisenhauer P J, O’Dwyer M, Christian J S Humphrey . Phase I clinical trial design in cancer drug development. Journal of Clinical Oncology, 2000, 18( 3): 684–684
15 A F, Fliri W T, Loging P F, Thadeio R A Volkmann . Analysis of drug-induced effect patterns to link structure and side effects of medicines. Nature Chemical Biology, 2005, 1( 7): 389–397
16 Dimidjian S, Hollon S D. How would we know if psychotherapy were harmful? American Psychologist, 2010, 65(1): 21−33
17 A, Bugrim T, Nikolskaya Y Nikolsky . Early prediction of drug metabolism and toxicity: systems biology approach and modeling. Drug Discovery Today, 2004, 9( 3): 127–135
18 K E, Flynn J M, Kramer C B, Dombeck K P Weinfurt . Participants’ perspectives on safety monitoring in clinical trials. Clinical Trials, 2013, 10( 4): 552–559
19 C A, Naranjo U, Busto E M Sellers . Difficulties in assessing adverse drug reactions in clinical trials. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 1982, 6(4−6): 651−657
20 K L G, Gomes Silva R E, Da Silva Junior J B, Da C G P, Bosio M R C G Novaes . Post-marketing authorisation safety and efficacy surveillance of advanced therapy medicinal products in brazil, the European union, the united states and japan. Cytotherapy, 2023, 25( 10): 1113–1123
21 I H Sarker . Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2021, 2( 3): 160
22 Y, Deng X, Xu Y, Qiu J, Xia W, Zhang S Liu . A multimodal deep learning framework for predicting drug-drug interaction events. Bioinformatics, 2020, 36( 15): 4316–4322
23 D, Szklarczyk A, Santos Mering C, Von L J, Jensen P, Bork M Kuhn . STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Research, 2016, 44( D1): D380–D384
24 K, Huang C, Xiao L M, Glass J Sun . MolTrans: molecular interaction transformer for drug-target interaction prediction. Bioinformatics, 2021, 37( 6): 830–836
25 N P, Tatonetti G H, Fernald R B Altman . A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports. Journal of the American Medical Informatics Association, 2012, 19( 1): 79–85
26 G, Xiong Z, Yang J, Yi N, Wang L, Wang H, Zhu C, Wu A, Lu X, Chen S, Liu T, Hou D Cao . DDInter: an online drug-drug interaction database towards improving clinical decision-making and patient safety. Nucleic Acids Research, 2022, 50( D1): D1200–D1207
27 K, Shingjergji R, Celebi J, Scholtes M Dumontier . Relation extraction from dailymed structured product labels by optimally combining crowd, experts and machines. Journal of Biomedical Informatics, 2021, 122: 103902
28 W, Hu W, Zhang Y, Zhou Y, Luo X, Sun H, Xu S, Shi T, Li Y, Xu Q, Yang Y, Qiu F, Zhu H Dai . MecDDI: clarified drug-drug interaction mechanism facilitating rational drug use and potential drug-drug interaction prediction. Journal of Chemical Information and Modeling, 2023, 63( 5): 1626–1636
29 M C, Cai Q, Xu Y J, Pan W, Pan N, Ji Y B, Li H J, Jin K, Liu Z L Ji . ADReCS: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms. Nucleic Acids Research, 2015, 43( D1): D907–D913
30 D, Mendez A, Gaulton A P, Bento J, Chambers Veij M, De E, Félix M P, Magariños J F, Mosquera P, Mutowo M, Nowotka M, Gordillo-Marañón F, Hunter L, Junco G, Mugumbate M, Rodriguez-Lopez F, Atkinson N, Bosc C J, Radoux A, Segura-Cabrera A, Hersey A R Leach . ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Research, 2019, 47( D1): D930–D940
31 X, Chen Z L, Ji Y Z Chen . TTD: therapeutic target database. Nucleic Acids Research, 2002, 30( 1): 412–415
32 M, Kanehisa S Goto . KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 2000, 28( 1): 27–30
33 S, Kim P A, Thiessen E E, Bolton J, Chen G, Fu A, Gindulyte L, Han J, He S, He B A, Shoemaker J, Wang B, Yu J, Zhang S H Bryant . PubChem substance and compound databases. Nucleic Acids Research, 2016, 44( D1): D1202–D1213
34 M, Whirl-Carrillo E M, McDonagh J M, Hebert L, Gong K, Sangkuhl C, Thorn R B, Altman T E Klein . Pharmacogenomics knowledge for personalized medicine. Clinical Pharmacology & Therapeutics, 2012, 92( 4): 414–417
35 R, Pinkoh R, Rodsiri S Wainipitapong . Retrospective cohort observation on psychotropic drug-drug interaction and identification utility from 3 databases: drugs. com®, lexicomp®, and epocrates®. PLoS One, 2023, 18( 6): e0287575
36 UniProt Consortium The . UniProt: a worldwide hub of protein knowledge. Nucleic Acids Research, 2019, 47( D1): D506–D515
37 J G, Delfino D M, Krainak S A, Flesher D L Miller . MRI-related FDA adverse event reports: A 10-yr review. Medical Physics, 2019, 46( 12): 5562–5571
38 H, Luo P, Zhang H, Huang J, Huang E, Kao L, Shi L, He L Yang . DDI-CPI, a server that predicts drug-drug interactions through implementing the chemical-protein interactome. Nucleic Acids Research, 2014, 42( W1): W46–W52
39 Z, Wu Y, Peng Z, Yu W, Li G, Liu Y Tang . NetInfer: a Web server for prediction of targets and therapeutic and adverse effects via network-based inference methods. Journal of Chemical Information and Modeling, 2020, 60( 8): 3687–3691
40 Moreau F, Simon N, Walther J, Dambrine M, Kosmalski G, Genay S, Perez M, Lecoutre D, Belaiche S, Rousselière C, Tod M, Décaudin B, Odou P. Does DDI-predictor help pharmacists to detect drug-drug interactions and resolve medication issues more effectively? Metabolites, 2021, 11(3): 173
41 Z H, Ren C Q, Yu L P, Li Z H, You J, Pan Y J, Guan L X Guo . BioChemDDI: Predicting drug-drug interactions by fusing biochemical and structural information through a self-attention mechanism. Biology, 2022, 11( 5): 758
42 Z H, Ren Z H, You C Q, Yu L P, Li Y J, Guan L X, Guo J Pan . A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks. Briefings in Bioinformatics, 2022, 23( 5): bbac363
43 Y, Zhong H, Zheng X, Chen Y, Zhao T, Gao H, Dong H, Luo Z Weng . DDI-GCN: Drug-drug interaction prediction via explainable graph convolutional networks. Artificial Intelligence in Medicine, 2023, 144: 102640
44 X, Zhao L, Chen J Lu . A similarity-based method for prediction of drug side effects with heterogeneous information. Mathematical Biosciences, 2018, 306: 136–144
45 W, Zhang X, Liu Y, Chen W, Wu W, Wang X Li . Feature-derived graph regularized matrix factorization for predicting drug side effects. Neurocomputing, 2018, 287: 154–162
46 X, Ji L, Wang L, Hua X, Wang P, Zhang A, Shendre W, Feng J, Li L Li . Improved adverse drug event prediction through information component guided pharmacological network model (IC-PNM). IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021, 18( 3): 1113–1121
47 H, Liang L, Chen X, Zhao X Zhang . Prediction of drug side effects with a refined negative sample selection strategy. Computational and Mathematical Methods in Medicine, 2020, 2020: 1573543
48 R, Ietswaart S, Arat A X, Chen S, Farahmand B, Kim W, DuMouchel D, Armstrong A, Fekete J J, Sutherland L Urban . Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology. EBioMedicine, 2020, 57: 102837
49 X, Guo W, Zhou Y, Yu Y, Ding J, Tang F Guo . A novel triple matrix factorization method for detecting drug-side effect association based on kernel target alignment. BioMed Research International, 2020, 2020: 4675395
50 S, Dasgupta A, Jayagopal A L J, Hong R, Mariappan V Rajan . Adverse drug event prediction using noisy literature-derived knowledge graphs: algorithm development and validation. JMIR Medical Informatics, 2021, 9( 10): e32730
51 C Y, Lee Y P P Chen . Descriptive prediction of drug side-effects using a hybrid deep learning model. International Journal of Intelligent Systems, 2021, 36( 6): 2491–2510
52 Z, Wu L Chen . Similarity-based method with multiple-feature sampling for predicting drug side effects. Computational and Mathematical Methods in Medicine, 2022, 2022: 9547317
53 J, Yang D, Zhang Y, Cai K, Yu M, Li L, Liu X Chen . Computational prediction of drug phenotypic effects based on substructure-phenotype associations. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023, 20( 1): 256–265
54 P, Joshi V, Masilamani A Mukherjee . A knowledge graph embedding based approach to predict the adverse drug reactions using a deep neural network. Journal of Biomedical Informatics, 2022, 132: 104122
55 Yao W, Zhao W, Jiang X, Shen X, He T. MPGNN-DSA: a meta-path-based graph neural network for drug-side effect association prediction. In: Proceedings of 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2022, 627−632
56 P, Xuan M, Wang Y, Liu D, Wang T, Zhang T Nakaguchi . Integrating specific and common topologies of heterogeneous graphs and pairwise attributes for drug-related side effect prediction. Briefings in Bioinformatics, 2022, 23( 3): bbac126
57 Y, Ding H, Zhou Q, Zou L Yuan . Identification of drug-side effect association via correntropy-loss based matrix factorization with neural tangent kernel. Methods, 2023, 219: 73–81
58 L, Yu Z, Xu W, Qiu X Xiao . MSDSE: predicting drug-side effects based on multi-scale features and deep multi-structure neural network. Computers in Biology and Medicine, 2024, 169: 107812
59 P, Xuan P, Li H, Cui M, Wang T, Nakaguchi T Zhang . Learning multi-types of neighbor node attributes and semantics by heterogeneous graph transformer and multi-view attention for drug-related side-effect prediction. Molecules, 2023, 28( 18): 6544
60 P, Xuan K, Xu H, Cui T, Nakaguchi T Zhang . Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects. Frontiers in Pharmacology, 2023, 14: 1257842
61 M, Kuhn I, Letunic L J, Jensen P Bork . The SIDER database of drugs and side effects. Nucleic Acids Research, 2016, 44( D1): D1075–D1079
62 E, Pauwels V, Stoven Y Yamanishi . Predicting drug side-effect profiles: a chemical fragment-based approach. BMC Bioinformatics, 2011, 12( 1): 169
63 M, Liu Y, Wu Y, Chen J, Sun Z, Zhao X W, Chen M E, Matheny H Xu . Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs. Journal of the American Medical Informatics Association, 2012, 19( e1): e28–e35
64 S, Mizutani E, Pauwels V, Stoven S, Goto Y Yamanishi . Relating drug-protein interaction network with drug side effects. Bioinformatics, 2012, 28( 18): i522–i528
65 B, Perozzi R, Al-Rfou S Skiena . DeepWalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014, 701−710
66 A, Bordes N, Usunier A, Garcia-Durán J, Weston O Yakhnenko . Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th International Conference on Neural Information Processing Systems. 2013, 2787−2795
67 A, Grover J Leskovec . node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016, 855−864
68 Y, Yamanishi E, Pauwels M Kotera . Drug side-effect prediction based on the integration of chemical and biological spaces. Journal of Chemical Information and Modeling, 2012, 52( 12): 3284–3292
69 L C, Huang X, Wu J Y Chen . Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures. Proteomics, 2013, 13( 2): 313–324
70 F, Cheng W, Li X, Wang Y, Zhou Z, Wu J, Shen Y Tang . Adverse drug events: database construction and in silico prediction. Journal of Chemical Information and Modeling, 2013, 53( 4): 744–752
71 W, Zhang F, Liu L, Luo J Zhang . Predicting drug side effects by multi-label learning and ensemble learning. BMC Bioinformatics, 2015, 16( 1): 365
72 C, Ngufor J, Wojtusiak J Pathak . A systematic prediction of adverse drug reactions using pre-clinical drug characteristics and spontaneous reports. In: Proceedings of 2015 International Conference on Healthcare Informatics. 2015, 76−81
73 S Y, Niu M Y, Xin J, Luo M Y, Liu Z R Jiang . DSEP: A tool implementing novel method to predict side effects of drugs. Journal of Computational Biology, 2015, 22( 12): 1108–1117
74 H, Rahmani G, Weiss O, Méndez-Lucio A Bender . ARWAR: a network approach for predicting adverse drug reactions. Computers in Biology and Medicine, 2016, 68: 101–108
75 K, Raja M, Patrick J T, Elder L C Tsoi . Machine learning workflow to enhance predictions of adverse drug reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases. Scientific Reports, 2017, 7( 1): 3690
76 D M, Bean H, Wu E, Iqbal O, Dzahini Z M, Ibrahim M, Broadbent R, Stewart R J B Dobson . Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records. Scientific Reports, 2017, 7( 1): 16416
77 S, Dey H, Luo A, Fokoue J, Hu P Zhang . Predicting adverse drug reactions through interpretable deep learning framework. BMC Bioinformatics, 2018, 19( 21): 476
78 B, Hu H, Wang L, Wang W Yuan . Adverse drug reaction predictions using stacking deep heterogeneous information network embedding approach. Molecules, 2018, 23( 12): 3193
79 Y, Ding J, Tang F Guo . Identification of drug-side effect association via semisupervised model and multiple kernel learning. IEEE Journal of Biomedical and Health Informatics, 2019, 23( 6): 2619–2632
80 Uner O C, Cinbis R G, Tastan O, Cicek A E. DeepSide: a deep learning framework for drug side effect prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022, 20(1): 330−339.
81 C S, Wang P J, Lin C L, Cheng S H, Tai Y H K, Yang J H Chiang . Detecting potential adverse drug reactions using a deep neural network model. Journal of Medical Internet Research, 2019, 21( 2): e11016
82 Y, Ding J, Tang F Guo . Identification of drug-side effect association via multiple information integration with centered kernel alignment. Neurocomputing, 2019, 325: 211–224
83 P, Bongini F, Scarselli M, Bianchini G M, Dimitri N, Pancino P Lió . Modular multi-source prediction of drug side-effects with DruGNN. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023, 20( 2): 1211–1220
84 A K, Nyamabo H, Yu Z, Liu J Y Shi . Drug-drug interaction prediction with learnable size-adaptive molecular substructures. Briefings in Bioinformatics, 2022, 23( 1): bbab441
85 D S, Wishart Y D, Feunang A C, Guo E J, Lo A, Marcu J R, Grant T, Sajed D, Johnson C, Li Z, Sayeeda N, Assempour I, Iynkkaran Y, Liu A, Maciejewski N, Gale A, Wilson L, Chin R, Cummings D, Le A, Pon C, Knox M Wilson . DrugBank 5. 0: a major update to the DrugBank database for 2018. Nucleic Acids Research, 2018, 46( D1): D1074–D1082
86 M, Habib M, Faris A, Alomari H Faris . AltibbiVec: a word embedding model for medical and health applications in the Arabic language. IEEE Access, 2021, 9: 133875–133888
87 D, Galeano S, Li M, Gerstein A Paccanaro . Predicting the frequencies of drug side effects. Nature Communications, 2020, 11( 1): 4575
88 Y, Koren R, Bell C Volinsky . Matrix factorization techniques for recommender systems. Computer, 2009, 42( 8): 30–37
89 M D, Ekstrand J T, Riedl J A Konstan . Collaborative filtering recommender systems. Foundations and Trends® in Human-Computer Interaction, 2011, 4( 2): 81–173
90 Ma H, Zhou D, Liu C, Lyu M R, King I. Recommender systems with social regularization. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 2011, 287−296
91 H, Zhao K, Zheng Y, Li J Wang . A novel graph attention model for predicting frequencies of drug-side effects from multi-view data. Briefings in Bioinformatics, 2021, 22( 6): bbab239
92 H, Zhao S, Wang K, Zheng Q, Zhao F, Zhu J Wang . A similarity-based deep learning approach for determining the frequencies of drug side effects. Briefings in Bioinformatics, 2022, 23( 1): bbab449
93 X, Xu L, Yue B, Li Y, Liu Y, Wang W, Zhang L Wang . DSGAT: predicting frequencies of drug side effects by graph attention networks. Briefings in Bioinformatics, 2022, 23( 2): bbab586
94 L, Wang C, Sun X, Xu J, Li W Zhang . A neighborhood-regularization method leveraging multiview data for predicting the frequency of drug-side effects. Bioinformatics, 2023, 39( 9): btad532
95 D D, Lee H S Seung . Algorithms for non-negative matrix factorization. In: Proceedings of the 13th International Conference on Neural Information Processing Systems, 2000, 535–541
96 H, Zhao P, Ni Q, Zhao X, Liang D, Ai S, Erhardt J, Wang Y, Li J Wang . Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework. Communications Biology, 2023, 6( 1): 870
97 J Y, Ryu H U, Kim S Y Lee . Deep learning improves prediction of drug-drug and drug-food interactions. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115( 18): E4304–E4311
98 Hou X, You J, Hu P. Predicting drug-drug interactions using deep neural network. In: Proceedings of the 11th International Conference on Machine Learning and Computing. 2019, 168−172
99 A K, Nyamabo H, Yu J Y Shi . SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction. Briefings in Bioinformatics, 2021, 22( 6): bbab133
100 Lyu T, Gao J, Tian L, Li Z, Zhang P, Zhang J. MDNN: A multimodal deep neural network for predicting drug-drug interaction events. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence. 2021, 3536−3542
101 Y, Chen T, Ma X, Yang J, Wang B, Song X Zeng . MUFFIN: multi-scale feature fusion for drug-drug interaction prediction. Bioinformatics, 2021, 37( 17): 2651–2658
102 Y, Yu K, Huang C, Zhang L M, Glass J, Sun C Xiao . SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization. Bioinformatics, 2021, 37( 18): 2988–2995
103 Y, Deng Y, Qiu X, Xu S, Liu Z, Zhang S, Zhu W Zhang . META-DDIE: predicting drug-drug interaction events with few-shot learning. Briefings in Bioinformatics, 2022, 23( 1): bbab514
104 S, Lin W, Chen G, Chen S, Zhou D Q, Wei Y Xiong . MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning. Journal of Cheminformatics, 2022, 14( 1): 81
105 Shao Z, Qian Y, Dou L. TBPM-DDIE: transformer based pretrained method for predicting drug-drug interactions events. In: Proceedings of the 46th IEEE Annual Computers, Software, and Applications Conference (COMPSAC). 2022, 229−234
106 L P, Kang K B, Lin P, Lu F, Yang J P Chen . Multitype drug interaction prediction based on the deep fusion of drug features and topological relationships. PLoS One, 2022, 17( 8): e0273764
107 S, Lin Y, Wang L, Zhang Y, Chu Y, Liu Y, Fang M, Jiang Q, Wang B, Zhao Y, Xiong D Q Wei . MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism. Briefings in Bioinformatics, 2022, 23( 1): bbab421
108 H, Yu S, Zhao J Shi . STNN-DDI: a substructure-aware tensor neural network to predict drug-drug interactions. Briefings in Bioinformatics, 2022, 23( 4): bbac209
109 Y, Hong P, Luo S, Jin X Liu . LaGAT: link-aware graph attention network for drug-drug interaction prediction. Bioinformatics, 2022, 38( 24): 5406–5412
110 Y H, Feng S W, Zhang Q Q, Zhang C H, Zhang J Y Shi . deepMDDI: A deep graph convolutional network framework for multi-label prediction of drug-drug interactions. Analytical Biochemistry, 2022, 646: 114631
111 J, Lin L, Wu J, Zhu X, Liang Y, Xia S, Xie T, Qin T Y Liu . R2-DDI: relation-aware feature refinement for drug-drug interaction prediction. Briefings in Bioinformatics, 2023, 24( 1): bbac576
112 Z, Li S, Zhu B, Shao X, Zeng T, Wang T Y Liu . DSN-DDI: an accurate and generalized framework for drug-drug interaction prediction by dual-view representation learning. Briefings in Bioinformatics, 2023, 24( 1): bbac597
113 H, Yu K, Li W, Dong S, Song C, Gao J Shi . Attention-based cross domain graph neural network for prediction of drug-drug interactions. Briefings in Bioinformatics, 2023, 24( 4): bbad155
114 C D, Han C C, Wang L, Huang X Chen . MCFF-MTDDI: multi-channel feature fusion for multi-typed drug-drug interaction prediction. Briefings in Bioinformatics, 2023, 24( 4): bbad215
115 J, Zhu Y, Liu C Wen . MTMA: Multi-task multi-attribute learning for the prediction of adverse drug-drug interaction. Knowledge-Based Systems, 2020, 199: 105978
116 J, Lu J, Yang D, Batra D Parikh . Hierarchical question-image co-attention for visual question answering. In: Proceedings of the 30th International Conference on Neural Information Processing Systems. 2016, 289−297
117 Lin X, Quan Z, Wang Z J, Ma T, Zeng X. KGNN: Knowledge graph neural network for drug-drug interaction prediction. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence. 2021, 2739−2745
118 K, Tang J, Huang H Zhang . Long-tailed classification by keeping the good and removing the bad momentum causal effect. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. 2020, 1513−1524
119 Benítez J M, Castro J L, Requena I. Are artificial neural networks black boxes? IEEE Transactions on Neural Networks, 1997, 8(5): 1156−1164
[1] Mengting NIU, Yaojia CHEN, Chunyu WANG, Quan ZOU, Lei XU. Computational approaches for circRNA-disease association prediction: a review[J]. Front. Comput. Sci., 2025, 19(4): 194904-.
[2] Wenzheng BAO, Bin YANG. Protein acetylation sites with complex-valued polynomial model[J]. Front. Comput. Sci., 2024, 18(3): 183904-.
[3] Yan LIN, Jiashu WANG, Xiaowei LIU, Xueqin XIE, De WU, Junjie ZHANG, Hui DING. A computational model to identify fertility-related proteins using sequence information[J]. Front. Comput. Sci., 2024, 18(1): 181902-.
[4] Bin-Bin JIA, Jun-Ying LIU, Jun-Yi HANG, Min-Ling ZHANG. Learning label-specific features for decomposition-based multi-class classification[J]. Front. Comput. Sci., 2023, 17(6): 176348-.
[5] Lerina AVERSANO, Mario Luca BERNARDI, Marta CIMITILE, Martina IAMMARINO, Debora MONTANO. Forecasting technical debt evolution in software systems: an empirical study[J]. Front. Comput. Sci., 2023, 17(3): 173210-.
[6] Zhengxiong HOU, Hong SHEN, Xingshe ZHOU, Jianhua GU, Yunlan WANG, Tianhai ZHAO. Prediction of job characteristics for intelligent resource allocation in HPC systems: a survey and future directions[J]. Front. Comput. Sci., 2022, 16(5): 165107-.
[7] Zhen SONG, Yu GU, Zhigang WANG, Ge YU. DRPS: efficient disk-resident parameter servers for distributed machine learning[J]. Front. Comput. Sci., 2022, 16(4): 164321-.
[8] Yu OU, Lang LI. Side-channel analysis attacks based on deep learning network[J]. Front. Comput. Sci., 2022, 16(2): 162303-.
[9] Suyu MEI. A framework combines supervised learning and dense subgraphs discovery to predict protein complexes[J]. Front. Comput. Sci., 2022, 16(1): 161901-.
[10] Xinyu TONG, Ziao YU, Xiaohua TIAN, Houdong GE, Xinbing WANG. Improving accuracy of automatic optical inspection with machine learning[J]. Front. Comput. Sci., 2022, 16(1): 161310-.
[11] Yi REN, Ning XU, Miaogen LING, Xin GENG. Label distribution for multimodal machine learning[J]. Front. Comput. Sci., 2022, 16(1): 161306-.
[12] Xiaobing SUN, Tianchi ZHOU, Rongcun WANG, Yucong DUAN, Lili BO, Jianming CHANG. Experience report: investigating bug fixes in machine learning frameworks/libraries[J]. Front. Comput. Sci., 2021, 15(6): 156212-.
[13] Xia-an BI, Yiming XIE, Hao WU, Luyun XU. Identification of differential brain regions in MCI progression via clustering-evolutionary weighted SVM ensemble algorithm[J]. Front. Comput. Sci., 2021, 15(6): 156903-.
[14] Yan-Ping SUN, Min-Ling ZHANG. Compositional metric learning for multi-label classification[J]. Front. Comput. Sci., 2021, 15(5): 155320-.
[15] Jian SUN, Pu-Feng DU. Predicting protein subchloroplast locations: the 10th anniversary[J]. Front. Comput. Sci., 2021, 15(2): 152901-.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed