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Cited by in F6Publishing
For: Matsuzaka Y, Uesawa Y. DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance. Front Bioeng Biotechnol 2019;7:485. [PMID: 32039185 DOI: 10.3389/fbioe.2019.00485] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 3.5] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 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] [Reference Citation Analysis]
3 Peña‐guerrero J, Nguewa PA, García‐sosa AT. Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases. WIREs Comput Mol Sci 2021;11. [DOI: 10.1002/wcms.1513] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 5.0] [Reference Citation Analysis]
4 Mamada H, Nomura Y, Uesawa Y. Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning. ACS Omega 2022;7:17055-62. [PMID: 35647436 DOI: 10.1021/acsomega.2c00261] [Reference Citation Analysis]
5 Grzegorzewska AK, Grot E, Sechman A. Sodium Fluoride In Vitro Treatment Affects the Expression of Gonadotropin and Steroid Hormone Receptors in Chicken Embryonic Gonads. Animals (Basel) 2021;11:943. [PMID: 33810503 DOI: 10.3390/ani11040943] [Reference Citation Analysis]
6 Matsuzaka Y, Hosaka T, Ogaito A, Yoshinari K, Uesawa Y. Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap-Deep Learning. Molecules 2020;25:E1317. [PMID: 32183141 DOI: 10.3390/molecules25061317] [Cited by in Crossref: 7] [Cited by in F6Publishing: 2] [Article Influence: 3.5] [Reference Citation Analysis]
7 Matsuzaka Y, Uesawa Y. Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library. Molecules 2020;25:E2764. [PMID: 32549344 DOI: 10.3390/molecules25122764] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
8 Green AJ, Mohlenkamp MJ, Das J, Chaudhari M, Truong L, Tanguay RL, Reif DM. Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology. PLoS Comput Biol 2021;17:e1009135. [PMID: 34214078 DOI: 10.1371/journal.pcbi.1009135] [Reference Citation Analysis]
9 Mamada H, Nomura Y, Uesawa Y. Prediction Model of Clearance by a Novel Quantitative Structure-Activity Relationship Approach, Combination DeepSnap-Deep Learning and Conventional Machine Learning. ACS Omega 2021;6:23570-7. [PMID: 34549154 DOI: 10.1021/acsomega.1c03689] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]