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Cited by in F6Publishing
For: Yuan J, Liu X, Wang S, Chang C, Zeng Q, Song Z, Jin Y, Zeng Q, Sun G, Ruan S, Greenwell C, Abramov YA. Virtual coformer screening by a combined machine learning and physics-based approach. CrystEngComm 2021;23:6039-44. [DOI: 10.1039/d1ce00587a] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Shah HS, Chaturvedi K, Kuang S, Wang J. Accelerating pre-formulation investigations in early drug product life cycles using predictive methodologies and computational algorithms. Ther Deliv 2021;12:789-97. [PMID: 34792419 DOI: 10.4155/tde-2021-0043] [Reference Citation Analysis]
2 Abramov YA, Sun G, Zeng Q. Emerging Landscape of Computational Modeling in Pharmaceutical Development. J Chem Inf Model 2022. [PMID: 35226809 DOI: 10.1021/acs.jcim.1c01580] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Sugden IJ, Braun DE, Bowskill DH, Adjiman CS, Pantelides CC. Efficient Screening of Coformers for Active Pharmaceutical Ingredient Cocrystallization. Crystal Growth & Design. [DOI: 10.1021/acs.cgd.2c00433] [Reference Citation Analysis]
4 Cappuccino C, Cusack D, Flanagan J, Harrison C, Holohan C, Lestari M, Walsh G, Lusi M. How Many Cocrystals Are We Missing? Assessing Two Crystal Engineering Approaches to Pharmaceutical Cocrystal Screening. Crystal Growth & Design. [DOI: 10.1021/acs.cgd.1c01342] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]