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For: Przybyłek M, Jeliński T, Słabuszewska J, Ziółkowska D, Mroczyńska K, Cysewski P. Application of Multivariate Adaptive Regression Splines (MARSplines) Methodology for Screening of Dicarboxylic Acid Cocrystal Using 1D and 2D Molecular Descriptors. Crystal Growth & Design 2019;19:3876-87. [DOI: 10.1021/acs.cgd.9b00318] [Cited by in Crossref: 12] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Zheng L, Zhu B, Wu Z, Liang F, Hong M, Liu G, Li W, Ren G, Tang Y. SMINBR: An Integrated Network and Chemoinformatics Tool Specialized for Prediction of Two-Component Crystal Formation. J Chem Inf Model 2021;61:4290-302. [PMID: 34436889 DOI: 10.1021/acs.jcim.1c00601] [Reference Citation Analysis]
2 Yuan Y, Li D, Wang C, Chen S, Kong M, Deng Z, Sun CC, Zhang H. Structural Features of Sulfamethizole and Its Cocrystals: Beauty Within. Crystal Growth & Design 2019;19:7185-92. [DOI: 10.1021/acs.cgd.9b01060] [Cited by in Crossref: 7] [Article Influence: 2.3] [Reference Citation Analysis]
3 Mswahili ME, Lee M, Martin GL, Kim J, Kim P, Choi GJ, Jeong Y. Cocrystal Prediction Using Machine Learning Models and Descriptors. Applied Sciences 2021;11:1323. [DOI: 10.3390/app11031323] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
4 Wang D, Yang Z, Zhu B, Mei X, Luo X. Machine-Learning-Guided Cocrystal Prediction Based on Large Data Base. Crystal Growth & Design 2020;20:6610-21. [DOI: 10.1021/acs.cgd.0c00767] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
5 Fornari F, Montisci F, Bianchi F, Cocchi M, Carraro C, Cavaliere F, Cozzini P, Peccati F, Mazzeo PP, Riboni N, Careri M, Bacchi A. Chemometric-assisted cocrystallization: supervised pattern recognition for predicting the formation of new functional cocrystals. Chemometrics and Intelligent Laboratory Systems 2022;226:104580. [DOI: 10.1016/j.chemolab.2022.104580] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
6 Mukherjee RK, Kumar V, Roy K. Ecotoxicological QSTR and QSTTR Modeling for the Prediction of Acute Oral Toxicity of Pesticides against Multiple Avian Species. Environ Sci Technol 2021. [PMID: 34905924 DOI: 10.1021/acs.est.1c05732] [Reference Citation Analysis]
7 Heng T, Yang D, Wang R, Zhang L, Lu Y, Du G. Progress in Research on Artificial Intelligence Applied to Polymorphism and Cocrystal Prediction. ACS Omega 2021;6:15543-50. [PMID: 34179597 DOI: 10.1021/acsomega.1c01330] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
8 Cysewski P, Walczak P, Ziółkowska D, Grela I, Przybyłek M. Experimental and theoretical studies on the Sulfamethazine-Urea and Sulfamethizole-Urea solid-liquid equilibria. Journal of Drug Delivery Science and Technology 2021;61:102186. [DOI: 10.1016/j.jddst.2020.102186] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
9 Przybyłek M, Walczak P, Ziółkowska D, Grela I, Cysewski P. Studies on the solid–liquid equilibria and intermolecular interactions Urea binary mixtures with Sulfanilamide and Sulfacetamide. The Journal of Chemical Thermodynamics 2021;153:106308. [DOI: 10.1016/j.jct.2020.106308] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
10 Kurotani A, Kakiuchi T, Kikuchi J. Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN). ACS Omega 2021;6:14278-87. [PMID: 34124451 DOI: 10.1021/acsomega.1c01035] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
11 Lee M, Kim J, Kim P, Lee I, Mswahili ME, Jeong Y, Choi GJ. Novel Cocrystals of Vonoprazan: Machine Learning-Assisted Discovery. Pharmaceutics 2022;14:429. [DOI: 10.3390/pharmaceutics14020429] [Reference Citation Analysis]