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For: 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: 12] [Article Influence: 2.5] [Reference Citation Analysis]
Number Citing Articles
1 Sellami A, Réau M, Montes M, Lagarde N. Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns. Front Endocrinol 2022;13:986016. [DOI: 10.3389/fendo.2022.986016] [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 Song WS, Koh DH, Kim EY. Orthogonal assay for validation of Tox21 PPARγ data and applicability to in silico prediction model. Toxicol In Vitro 2022;84:105445. [PMID: 35863590 DOI: 10.1016/j.tiv.2022.105445] [Reference Citation Analysis]
4 Jeong J, Choi J. Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications. Environ Sci Technol 2022. [PMID: 35666838 DOI: 10.1021/acs.est.1c07413] [Reference Citation Analysis]
5 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]
6 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]
7 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 F6Publishing: 2] [Reference Citation Analysis]
8 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: 2] [Article Influence: 1.0] [Reference Citation Analysis]
9 Pérez Santín E, Rodríguez Solana R, González García M, García Suárez MDM, Blanco Díaz GD, Cima Cabal MD, Moreno Rojas JM, López Sánchez JI. Toxicity prediction based on artificial intelligence: A multidisciplinary overview. WIREs Comput Mol Sci 2021;11. [DOI: 10.1002/wcms.1516] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 8.0] [Reference Citation Analysis]
10 Yoshimori A. Prediction of Molecular Properties Using Molecular Topographic Map. Molecules 2021;26:4475. [PMID: 34361624 DOI: 10.3390/molecules26154475] [Reference Citation Analysis]
11 Martínez-Rojo E, Berumen LC, García-Alcocer G, Escobar-Cabrera J. The Role of Androgens and Androgen Receptor in Human Bladder Cancer. Biomolecules 2021;11:594. [PMID: 33919565 DOI: 10.3390/biom11040594] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
12 Lovrić M, Malev O, Klobučar G, Kern R, Liu JJ, Lučić B. Predictive Capability of QSAR Models Based on the CompTox Zebrafish Embryo Assays: An Imbalanced Classification Problem. Molecules 2021;26:1617. [PMID: 33803931 DOI: 10.3390/molecules26061617] [Cited by in F6Publishing: 4] [Reference Citation Analysis]