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Cited by in F6Publishing
For: Matsuzaka Y, Totoki S, Handa K, Shiota T, Kurosaki K, Uesawa Y. Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure-Activity Relationship System. Int J Mol Sci 2021;22:10821. [PMID: 34639159 DOI: 10.3390/ijms221910821] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Pantic I, Paunovic J, Cumic J, Valjarevic S, Petroianu GA, Corridon PR. Artificial neural networks in contemporary toxicology research. Chemico-Biological Interactions 2022. [DOI: 10.1016/j.cbi.2022.110269] [Reference Citation Analysis]
2 Wang J, Lou C, Liu G, Li W, Wu Z, Tang Y. Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening. Brief Bioinform 2022:bbac351. [PMID: 35998896 DOI: 10.1093/bib/bbac351] [Reference Citation Analysis]
3 Matsuzaka Y, Uesawa Y. A Deep Learning-Based Quantitative Structure-Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance. Int J Mol Sci 2022;23:2141. [PMID: 35216254 DOI: 10.3390/ijms23042141] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]