Design of a predictive model to optimize the solubility of oxaprozin as a nonsteroidal anti-inflammatory drug

  • Zeng, X., Tu, X., Liu, Y., Fu, X. & Su, Y. Towards better drug discovery with a knowledge graph. Running. Notice. Structure. Biol. 72114–126 (2022).

    CAS Google Scholar Article

  • Zhuang, W., Hachem, K., Bokov, D., Ansari, MJ & Nakhjiri, AT Ionic liquids in the pharmaceutical industry: a systematic review of applications and future perspectives. J.Mol. Liquids 349118145 (2021).

    Google Scholar article

  • Chakravarty, P., Famili, A., Nagapudi, K. & Al-Sayah, MA Using supercritical fluid technology as a green alternative when preparing drug delivery systems. Pharmacy 11629 (2019).

    CAS Google Scholar Article

  • Savjani, KT, Gajjar, AK and Savjani, JK Drug solubility: importance and techniques for improvement. Int. Sch. Res. Not. 2012 (2012).

  • Greenblatt, D. et al. Pharmacokinetics of oxaprozin in the elderly. Br.J.Clin. Pharmacol. 19373–378 (1985).

    CAS Google Scholar Article

  • Kean, WF Oxaprozin: kinetic and dynamic profile in the treatment of pain 1275-1277 (Taylor & Francis, 2004).

    Google Scholar

  • Fischer, J. & Ganellin, CR Discovery of analogue-based drugs. Chem. Int. Newsmag. IUPAC 3212–15 (2010).

    Google Scholar

  • Ganellin, CR Analog-Based Drug Discovery II (Wiley, 2010).

    Google Scholar

  • Miller, LG Oxaprozin: A once-daily nonsteroidal anti-inflammatory drug. Clin. Pharma. 11591–603 (1992).

    CAS PubMed Google Scholar

  • File:Oxaprozin ball molecule.png. Wikimedia Commons, the free media repository (2022). Retrieved May 17, 2022, 10:52 a.m. from https://commons.wikimedia.org/w/index.php?title=File:Oxaprozin_molecule_ball.png&oldid=644050983.

  • Baldelli, A., Boraey, MA, Nobes, DS & Vehring, R. Analysis of the particle formation process of structured microparticles. Mol. Pharma. 122562-2573 (2015).

    CAS Google Scholar Article

  • Misra, SK & Pathak, K. Supercritical fluid technology for the solubilization of poorly water-soluble drugs via the generation of micro and naonotic particles. ADMIT DMPK 8355–374 (2020).

    PubMed PubMed Central Google Scholar

  • Bahramifar, N., Yamini, Y. & Shamsipur, M. Investigation of supercritical carbon dioxide extraction of some polar drugs from enriched matrices and tablets. J. Superscript. Fluids 35205-211 (2005).

    CAS Google Scholar Article

  • Khosmaram, A. et al. Supercritical process for the preparation of nanomedicine: case study of oxaprozine. Chem. Eng. Technology. 44208-212 (2021).

    CAS Google Scholar Article

  • Chinh Nguyen, H. et al. Computational prediction of drug solubility in supercritical carbon dioxide: thermodynamic modeling and artificial intelligence. J.Mol. Liquids 354118888 (2022).

    CAS Google Scholar Article

  • Bishop, CM Pattern recognition. Mach. Learn. 128 (2006).

  • Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M. & Chica-Rivas, M. Predictive Machine Learning Models for Mineral Prospectivity: An Assessment of Neural Networks, Random Forests, regression trees and support vector machines. Ore Geol. Round. 71804–818 (2015).

    Google Scholar article

  • Goodfellow, I., Bengio, Y. & Courville, A. Basics of machine learning. Deep learning. 198-164 (2016).

    MATH Google Scholar

  • Breiman, L., Friedman, JH, Olshen, RA, and Stone, CJ Classification and regression trees (Routledge, 2017).

    Book Google Scholar

  • Xue, M., Su, Y., Li, C., Wang, S., and Yao, H. Identification of potential type II diabetes in a large-scale Chinese population using a machine learning framework systematic. J. Diabetes Res. 2020 (2020).

  • Breiman, L. Bagging predictors. Mach. Learn. 24123–140 (1996).

    MATH Google Scholar

  • Borra, S. & Di Ciaccio, A. Improvement of nonparametric regression methods by bagging and boosting. Calculation. Statistics Data analysis. 38407–420 (2002).

    MathSciNet ArticleGoogle Scholar

  • Freund, Y. & Schapire, RE Experiment with a new boosting algorithm. In ICML 148-156 (Citeseer, 1996).

  • Mason, L., Baxter, J., Bartlett, P. & Frean, M. Boosting algorithms as gradient descent. Adv. Neural information. Treat. System. 12 (1999).

  • Pardoe, D. & Stone, P. Boosting for Regression Transfer. In ICML (2010).

  • Wu, Q., Burges, CJ, Svore, KM, and Gao, J. Adaptation of stimulation to information-seeking measures. Inf. Retr. 13254–270 (2010).

    Google Scholar article

  • Ying, C., Qi-Guang, M., Jia-Chen, L. & Lin, G. Advance and Prospects of the AdaBoost Algorithm. Acta Autom. Peach. 39745–758 (2013).

    Google Scholar

  • Benesty, J., Chen, J., Huang, Y. & Cohen, I. Pearson correlation coefficient. In Noise reduction in speech processing 1–4 (Springer, 2009).

  • Jiang, R., Tang, W., Wu, X. & Fu, W. A random forest approach for detecting epistatic interactions in case-control studies. BMC Bioinform. ten1–12 (2009).

    Google Scholar article

  • Zhang, J. et al. Rapid evaluation of Tan sheep texture parameters using hyperspectral imaging with optimization algorithms. food control 135108815 (2022).

    Google Scholar article

  • Geurts, P., Ernst, D. & Wehenkel, L. Extremely random trees. Mach. Learn. 633–42 (2006).

    Google Scholar article

  • Dutta, S., Mukherjee, U. & Bandyopadhyay, SK Impact of pharmacy on Covid-19 vaccination progress using a machine learning approach. J.Pharm. Res. Int. 33202-217 (2021).

    Google Scholar article

  • Song, Y.-Y. & Ying, L. Decision Tree Methods: Applications for Classification and Prediction. Shanghai Arch. Psychiatry 27130 (2015).

    PubMed PubMed Central Google Scholar

  • Friedman, JH Greedy Function Approximation: A Gradient Boosting Machine. Ann. Statistical 291189-1232 (2001).

    MathSciNet ArticleGoogle Scholar

  • Truong, V.-H., Vu, Q.-V., Thai, H.-T. & Ha, M.-H. A robust method for steel truss safety assessment using a gradient tree reinforcement algorithm. Adv. Eng. Software 147102825 (2020).

    Google Scholar article

  • Xu, Q. et al. PDC-GBS: Prediction of effective drug combinations using a stochastic gradient boosting algorithm. J. Theor. Biol. 4171–7 (2017).

    ADS CAS Article Google Scholar

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