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Cited by in F6Publishing
For: 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: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Huoyu R, Zhiqiang Z, Guofang J, Zhanggao L, Zhenzhen X. Quantitative Structure-Property Relationship for Critical Temperature of Alkenes with Quantum-Сhemical and Topological Indices. Russ J Phys Chem 2022;96:2329-2334. [DOI: 10.1134/s0036024422110267] [Reference Citation Analysis]
2 Nguyen TH, Nguyen LH, Truong TN. Application of Machine Learning in Developing Quantitative Structure–Property Relationship for Electronic Properties of Polyaromatic Compounds. ACS Omega. [DOI: 10.1021/acsomega.2c02650] [Reference Citation Analysis]
3 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] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]