Home Book editor Development of a new robust method to improve the solubility of oxaprozin as a nonsteroidal anti-inflammatory drug based on machine learning

Development of a new robust method to improve the solubility of oxaprozin as a nonsteroidal anti-inflammatory drug based on machine learning

0
  • Khanna, I. Drug discovery in the pharmaceutical industry: productivity challenges and trends. Drug discovery. Today 171088-1102 (2012).

    Google Scholar article

  • Sarkis, M., Bernardi, A., Shah, N. & Papathanasiou, MM Emerging challenges and opportunities in pharmaceutical manufacturing and distribution. Process 9457 (2021).

    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

  • Birmingham, B. & Buvanendran, A. 40 – Nonsteroidal anti-inflammatories, acetaminophen and COX-2 inhibitors. In Practical Pain Management (Fifth Edition) (eds Benzon, HT et al.) 553-568.e555 (Mosby, 2014).

    Google Scholar Chapter

  • Dallegri, F., Bertolotto, M. & Ottonello, L. A review of the emerging profile of the anti-inflammatory oxaprozin. Expert advice. Pharmacologist. 6777–785 (2005).

    CAS Google Scholar Article

  • Todd, PA & Brogden, RN Oxaprozin: a preliminary review of its pharmacodynamic and pharmacokinetic properties and therapeutic efficacy. Drugs 32291–312 (1986).

    CAS Google Scholar Article

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

    CAS PubMed Google Scholar

  • Hojjati, M., Yamini, Y., Khajeh, M., and Vatanara, A. Solubility of some statins in supercritical carbon dioxide and representing solute solubility data with several density-based correlations. J. Superscript. Fluids 41187–194 (2007).

    CAS Google Scholar Article

  • Foster, N. et al. Processing of pharmaceutical compounds using dense gas technology. Eng. ind. Chem. Res. 426476–6493 (2003).

    CAS Google Scholar Article

  • Güçlü-Üstündağ, Ö. & Temelli, F. Solubility behavior of ternary systems of lipids, cosolvents, and supercritical carbon dioxide and processing aspects. J. Superscript. Fluids 361–15 (2005).

    Google Scholar article

  • Paul, D. et al. Artificial intelligence in drug discovery and development. Drug discovery. Today 2680 (2021).

    CAS Google Scholar Article

  • Yang, J., Du, Q., Ma, R. & Khan, A. Artificial intelligence simulation of water treatment using a novel bimodal micromesoporous nanocomposite. J.Mol. Liquid. 340117296 (2021).

    CAS Google Scholar Article

  • El Naqa, I. & Murphy, MJ What is Machine Learning?. In: Machine Learning in Radiation Oncology. 3–11 (Springer, 2015).

  • Wang, H., Lei, Z., Zhang, X., Zhou, B., and Peng, J. Basics of machine learning. deep learning. 98-164 (2016).

  • Dietterich, TG Ensemble methods in machine learning. In: International Workshop on Multiple Classification Systems. 1–15 (Springer, 2000).

  • Zhou, Z.-H. Ensemble Methods: Fundamentals and Algorithms, Chapman and Hall/CRC, 2019.

  • Freund, Y. & Schapire, RE A generalization of e-learning decision theory and an application to boosting. J. Compute. System Science. 55119–139 (1997).

    MathSciNet ArticleGoogle Scholar

  • Mathuria, M. Decision tree analysis on the j48 algorithm for data mining. Int. J. Adv. Res. Calculation. Science. Software Engineering. 31114-1119 (2013).

    Google Scholar

  • Sakar, A. & Mammone, RJ Growth and pruning of neural tree networks. IEEE Trans. Calculation. 42291-299 (1993).

    Google Scholar article

  • Frau, L., Susto, GA, Barbariol, T. & Feltresi, E. Uncertainty estimation for machine learning models in multiphase flow applications. Computer science. 858 (2021).

    Google Scholar article

  • Mosavi, A. et al. mapping the water erosion sensitivity of soils using machine learning models. Water 121995 (2020).

    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

  • Quinonero-Candela, J. & Rasmussen, CE A unifying view of sparse approximate Gaussian process regression. J.Mach. Learn. Res. 61939-1959 (2005).

    MathSciNet MATHGoogle Scholar

  • Jiang, Y., Jia, J., Li, Y., Kou, Y., and Sun, S. Prediction of two-phase gas-liquid throttling flow using Gaussian process regression. Flow Meas. Instrument. 81102044 (2021).

    Google Scholar article

  • Quinlan, JR Learning Decision Tree Classifiers. MCA calculation. Surv. (SCUR) 2871–72 (1996).

    Google Scholar article

  • Xu, M., Watanachaturaporn, P., Varshney, PK & Arora, MK Decision Tree Regression for Flexible Classification of Remote Sensing Data. Remote Sensing Approx. 97322–336 (2005).

    Article on Google Scholar Ads

  • Kushwah, JS et al. Comparative study of regressor and classifier with decision tree using modern tools. Mater. Today Proc. 563571–3576 (2021).

    Google Scholar article

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

  • Segal, MR & Bloch, DA A comparison of estimated proportional hazards models and regression trees. Med Statistics 8539–550 (1989).

    CAS Google Scholar Article

  • Schapire, RE The challenging approach to machine learning: an overview. Nonlinear Estimation and Classification 149–171 (2003).

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

    Google Scholar article

  • Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning. Springer Series in Statistics (Springer, 2001).

  • Bishop, CM and Nasrabadi, NM Pattern recognition and machine learning Flight. 4. (New York: Springer, 2006).

  • Hastie, T., Rosset, S., Zhu, J. & Zou, H. Adaboost multi-classes, statistics and sound. Interface 2349–360 (2009).

    MathSciNet MATHGoogle Scholar

  • Berk, RA An introduction to ensemble methods for data analysis. Social. Methods Res. 34263–295 (2006).

    MathSciNet ArticleGoogle Scholar

  • Ouyang, Z., Ravier, P. & Jabloun, M. STL decomposition of time series can benefit predictions made by statistical methods, but not by machine learning methods. Eng. proc. 5(1), 42 (2021).

    Google Scholar

  • De Myttenaere, A., Golden, B., Le Grand, B., and Rossi, F. Mean absolute error in percent for regression models. Neuroinformatics 19238–48 (2016).

    Google Scholar article

  • Paula, M., Marilaine, C., Nuno, FJ, and Wallace, C. Prediction of long-term wind speed in wind farms in northeast Brazil: a comparative analysis using learning models automatique. IEEE Lat. A m. Trans. 182011-2018 (2020).

    Google Scholar article

  • Botchkarev, A. Performance evaluation of regression machine learning models using multiple error metrics in Azure Machine Learning Studio. Available at SSRN 3177507. (2018).

  • Knez, Z., Skerget, M., Sencar-Bozic, P. & Rizner, A. Solubility of nifedipine and nitrendipine in supercritical CO2. J. Chem. Eng. Data 40216–220 (1995).

    CAS Google Scholar Article