BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Qin Z, Xi Y, Zhang S, Tu G, Yan A. Classification of Cyclooxygenase-2 Inhibitors Using Support Vector Machine and Random Forest Methods. J Chem Inf Model 2019;59:1988-2008. [PMID: 30762371 DOI: 10.1021/acs.jcim.8b00876] [Cited by in Crossref: 4] [Cited by in F6Publishing: 10] [Article Influence: 1.3] [Reference Citation Analysis]
Number Citing Articles
1 Yang J, Chen Q, Wang H, Hu X, Guo Y, Chen J. Reliable CA-(Q)SAR generation based on entropy weight optimized by grid search and correction factors. Computers in Biology and Medicine 2022;146:105573. [DOI: 10.1016/j.compbiomed.2022.105573] [Reference Citation Analysis]
2 Li R, Tian Y, Yang Z, Ji Y, Ding J, Yan A. Classification models and SAR analysis on HDAC1 inhibitors using machine learning methods. Mol Divers 2022. [PMID: 35737257 DOI: 10.1007/s11030-022-10466-w] [Reference Citation Analysis]
3 Vetrivel A, Ramasamy J, Natchimuthu S, Senthil K, Ramasamy M, Murugesan R. Combined machine learning and pharmacophore based virtual screening approaches to screen for antibiofilm inhibitors targeting LasR of Pseudomonas aeruginosa. J Biomol Struct Dyn 2022;:1-19. [PMID: 35451916 DOI: 10.1080/07391102.2022.2064331] [Reference Citation Analysis]
4 Pan Y, He L, Ren Y, Wang W, Wang T. Analysis of Influencing Factors on the Gas Separation Performance of Carbon Molecular Sieve Membrane Using Machine Learning Technique. Membranes 2022;12:100. [DOI: 10.3390/membranes12010100] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Dibia KT, Igbokwe PK, Ezemagu GI, Asadu CO. Exploration of the quantitative Structure-Activity relationships for predicting Cyclooxygenase-2 inhibition bioactivity by Machine learning approaches. Results in Chemistry 2022;4:100272. [DOI: 10.1016/j.rechem.2021.100272] [Reference Citation Analysis]
6 Huo D, Wang S, Kong Y, Qin Z, Yan A. Discovery of Novel Epidermal Growth Factor Receptor (EGFR) Inhibitors Using Computational Approaches. J Chem Inf Model 2021. [PMID: 34931847 DOI: 10.1021/acs.jcim.1c00884] [Reference Citation Analysis]
7 Titi A, Almutairi SM, Touzani R, Messali M, Tillard M, Hammouti B, Kodadi ME, Eddike D, Zarrouk A, Warad I. A new mixed pyrazole-diamine/Ni(II) complex, Crystal structure, physicochemical, thermal and antibacterial investigation. Journal of Molecular Structure 2021;1236:130304. [DOI: 10.1016/j.molstruc.2021.130304] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
8 Luque Ruiz I, Gómez-nieto MÁ. Improvement the performance of the classification models of Cyclooxygenase-2 inhibitors using undersampling methods based on the rivality and reliability indexes. J Math Chem 2021;59:131-60. [DOI: 10.1007/s10910-020-01184-5] [Reference Citation Analysis]
9 Pandya PN, Kumar SP, Bhadresha K, Patel CN, Patel SK, Rawal RM, Mankad AU. Identification of promising compounds from curry tree with cyclooxygenase inhibitory potential using a combination of machine learning, molecular docking, dynamics simulations and binding free energy calculations. Molecular Simulation 2020;46:812-22. [DOI: 10.1080/08927022.2020.1764552] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
10 Wahab HA, Amaro RE, Cournia Z. A Celebration of Women in Computational Chemistry. J Chem Inf Model 2019;59:1683-92. [DOI: 10.1021/acs.jcim.9b00368] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 0.7] [Reference Citation Analysis]