Predicting the stereoselectivity of chemical reactions by composite machine learning method

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Predicting the stereoselectivity of chemical reactions by composite machine learning method
  • Li, J. et al. Predicting the stereoselectivity of chemical transformations by machine learning. arXiv preprint arXiv:2110.05671 (2021).

  • Reid, J. P. & Sigman, M. S. Holistic prediction of enantioselectivity in asymmetric catalysis. Nature 571, 343–348 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Nugent, T. C. Chiral Amine Synthesis: Methods, Developments and Applications (Wiley, 2010).

    Book 

    Google Scholar 

  • Silverio, D. L. et al. Simple organic molecules as catalysts for enantioselective synthesis of amines and alcohols. Nature 494, 216–221 (2013).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Moon, S., Chatterjee, S., Seeberger, P. H. & Gilmore, K. Predicting glycosylation stereoselectivity using machine learning. Chem. Sci. 12, 2931–2939 (2021).

    Article 
    CAS 

    Google Scholar 

  • Yu, X. Prediction of enantioselectivity in thiol addition to imines catalyzed by chiral phosphoric acids. J. Phys. Org. Chem. 35, e4338 (2022).

    Article 
    CAS 

    Google Scholar 

  • Gao, B. et al. A machine learning model for predicting enantioselectivity in hypervalent iodine (iii) catalyzed asymmetric phenolic dearomatizations. CCS Chem. 1–14 (2024).

  • Hoque, A. & Sunoj, R. B. Deep learning for enantioselectivity predictions in catalytic asymmetric \(\beta \)-c-h bond activation reactions. Digital Discov. 1, 926–940 (2022).

    Article 
    CAS 

    Google Scholar 

  • Hong, Y., Welch, C. J., Piras, P. & Tang, H. Enhanced structure-based prediction of chiral stationary phases for chromatographic enantioseparation from 3D molecular conformations. Analytical Chem. (2024).

  • Ferraz-Caetano, J., Teixeira, F. & Cordeiro, M. N. D. Explainable supervised machine learning model to predict solvation gibbs energy. J. Chem. Inf. Model. 64, 2250–2262 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Ward, L. et al. Graph-based approaches for predicting solvation energy in multiple solvents: open datasets and machine learning models. J. Phys. Chem. A 125, 5990–5998 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Low, K., Coote, M. L. & Izgorodina, E. I. Explainable solvation free energy prediction combining graph neural networks with chemical intuition. J. Chem. Inf. Model. 62, 5457–5470 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Lim, H. & Jung, Y. MLSolvA: Solvation free energy prediction from pairwise atomistic interactions by machine learning. J. Cheminform. 13, 56 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Pathak, Y., Mehta, S. & Priyakumar, U. D. Learning atomic interactions through solvation free energy prediction using graph neural networks. J. Chem. Inf. Model. 61, 689–698 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Solomons, T. G. & Fryhle, C. B. Organic Chemistry (Wiley, 2008).

    Google Scholar 

  • Terada, M., Machioka, K. & Sorimachi, K. High substrate/catalyst organocatalysis by a chiral brønsted acid for an enantioselective aza-ene-type reaction. Angew. Chem. Int. Ed. 45, 2254–2257 (2006).

    Article 
    CAS 

    Google Scholar 

  • Chen, M.-W. et al. Organocatalytic asymmetric reduction of fluorinated alkynyl ketimines. J. Org. Chem. 83, 8688–8694 (2018).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Zahrt, A. F. et al. Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning. Science 363, eaau5631 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Dudley, R. The Shapiro–Wilk test for normality (2023).

  • Stevens, J. P. Intermediate Statistics: A Modern Approach (Routledge, 2013).

    Book 

    Google Scholar 

  • Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B Stat Methodol. 58, 267–288 (1996).

    Article 
    MathSciNet 

    Google Scholar 

  • Loh, W.-Y. Classification and regression trees. Wiley Interdiscipl. Rev. Data Mining Knowl. Discov. 1, 14–23 (2011).

    Article 

    Google Scholar 

  • Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article 

    Google Scholar 

  • Drucker, H. Improving regressors using boosting techniques. In Icml, vol. 97, 107–115 (Citeseer, 1997).

  • Smola, A. J. & Schölkopf, B. A tutorial on support vector regression. Stat. Comput. 14, 199–222 (2004).

    Article 
    MathSciNet 

    Google Scholar 

  • Schapire, R. E. The strength of weak learnability. Mach. Learn. 5, 197–227 (1990).

    Article 

    Google Scholar 

  • Tsiambaos, G. & Sabatakakis, N. Considerations on strength of intact sedimentary rocks. Eng. Geol. 72, 261–273 (2004).

    Article 

    Google Scholar 

  • Xu, Q.-S. & Liang, Y.-Z. Monte Carlo cross validation. Chemom. Intell. Lab. Syst. 56, 1–11 (2001).

    Article 
    CAS 

    Google Scholar 

  • Frazier, P. I. A tutorial on Bayesian optimization. arXiv preprint arXiv:1807.02811 (2018).

  • Kaneko, H. Cross-validated permutation feature importance considering correlation between features. Anal. Sci. Adv. 3, 278–287 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • scikitlearn. sklearn.svm.svc. https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html.

  • Zimmerman, D. W. Correcting two-sample “z” and “t” tests for correlation: An alternative to one-sample tests on difference scores. Psicologica Int. J. Methodol. Exp. Psychol. 33, 391–418 (2012).

  • Hogg, R. V., Tanis, E. A. & Zimmerman, D. L. Probability and Statistical Inference, vol. 993 (Macmillan, 1977).

  • Walker, M. A. Libretexts. https://chem.libretexts.org.

  • Shi, H., Yang, N., Yang, X. & Tang, H. Clarifying relationship between pm2.5 concentrations and spatiotemporal predictors using multi-way partial dependence plots. Remote Sens. 15, 358 (2023).

    Article 
    ADS 

    Google Scholar 

  • Buchanan, R., Whiting, R. & Damert, W. When is simple good enough: a comparison of the Gompertz, Baranyi, and three-phase linear models for fitting bacterial growth curves. Food Microbiol. 14, 313–326 (1997).

    Article 

    Google Scholar 

  • McLachlan, G. J. & Basford, K. E. Mixture Models: Inference and Applications to Clustering, vol. 38 (M. Dekker, 1988).

  • Dempster, A. P., Laird, N. M. & Rubin, D. B. Maximum likelihood from incomplete data via the em algorithm. J. Roy. Stat. Soc. Ser. B (Methodol.) 39, 1–22 (1977).

    Article 
    MathSciNet 

    Google Scholar 

  • Neath, A. A. & Cavanaugh, J. E. The Bayesian information criterion: Background, derivation, and applications. Wiley Interdiscipl. Rev. Comput. Stat. 4, 199–203 (2012).

    Article 

    Google Scholar 

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