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For: Shankar S, Bhandari I, Okou DT, Srinivasa G, Athri P. Predicting adverse drug reactions of two-drug combinations using structural and transcriptomic drug representations to train an artificial neural network. Chem Biol Drug Des 2021;97:665-73. [PMID: 33006799 DOI: 10.1111/cbdd.13802] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.7] [Reference Citation Analysis]
Number Citing Articles
1 Vo TH, Nguyen NTK, Kha QH, Le NQK. On the road to explainable AI in drug-drug interactions prediction: a systematic review. Computational and Structural Biotechnology Journal 2022. [DOI: 10.1016/j.csbj.2022.04.021] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Kim E, Nam H. DeSIDE-DDI: interpretable prediction of drug-drug interactions using drug-induced gene expressions. J Cheminform 2022;14:9. [PMID: 35246258 DOI: 10.1186/s13321-022-00589-5] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Datta TT, Shill PC, Al Nazi Z. BERT-D2: Drug-Drug Interaction Extraction using BERT. 2022 International Conference for Advancement in Technology (ICONAT) 2022. [DOI: 10.1109/iconat53423.2022.9725979] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Yu Z, Wu Z, Li W, Liu G, Tang Y. ADENet: a novel network-based inference method for prediction of drug adverse events. Brief Bioinform 2022:bbab580. [PMID: 35039845 DOI: 10.1093/bib/bbab580] [Reference Citation Analysis]
5 Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou MM, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021;41:1427-73. [PMID: 33295676 DOI: 10.1002/med.21764] [Cited by in Crossref: 43] [Cited by in F6Publishing: 42] [Article Influence: 21.5] [Reference Citation Analysis]