Citation: Han-Bin Liu, Xiaoyu Cheng, Zhou Guo, Juan Yang, Fuwen Tan, Donghui Lan, Jian-Ping Tan, Bing Yi, Weixin Zhai, Qing-Hui Guo. CrownBind-IA: A machine learning model predicting binding constants between crown ethers and alkali metal ions[J]. Chinese Chemical Letters, ;2025, 36(12): 111149. doi: 10.1016/j.cclet.2025.111149 shu

CrownBind-IA: A machine learning model predicting binding constants between crown ethers and alkali metal ions

Han-Bin Liu ^{a, *,} ,
Xiaoyu Cheng ^{b, c} ,
Zhou Guo ^{b, c} ,
Juan Yang ^a ,
Fuwen Tan ^a ,
Donghui Lan ^a ,
Jian-Ping Tan ^a ,
Bing Yi ^{a, *,} ,
Weixin Zhai ^{b, c, *,} ,
Qing-Hui Guo ^{d, *,}

a.
Hunan Province Key Laboratory of Environmental Catalysis and Waste Recycling, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan 411104, China
b.
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
c.
Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
d.
MOE Key Laboratory of Bioorganic Phosphorous and Chemical Biology, Department of Chemistry, Tsinghua University, Beijing 100084, China

hanbin.liu@hnie.edu.cn

bingyi2004@126.com

zhaiweixin@cau.edu.cn

guoqh@tsinghua.edu.cn

Received Date: 8 January 2025
Revised Date: 12 March 2025
Accepted Date: 26 March 2025
Available Online: 26 March 2025

Figures(4)

Machine learning methodologies have been extensively leveraged across diverse domains of chemical research, yielding remarkable outcomes, and exhibit substantial potential for impactful future applications within the field of supramolecular chemistry. The recognition of alkali metal ions by crown ethers is one of the most classic and widely applied host-guest interactions in supramolecular chemistry. Due to the numerous factors affecting the host-guest interaction, it remains a great challenge to achieve fast and accurate prediction of the binding constants between crown ethers and alkali metal ions. Herein, we report a highly accurate machine learning model that can effectively predict the binding constants between crown ethers and alkali metal ions, i.e., CrownBind-IA, with a low RMSE of 0.68 logK units. Moreover, this model proves robust extrapolative capabilities by accurately predicting out-of-sample data. The establishment of CrownBind-IA demonstrates the promising application potentials of data-driven machine learning methods in supramolecular chemistry, and it will substantially reduce the time and expense of experimental trials and characterizations, promote the exploration of the mechanism of host-guest interactions, as well as the rational design of novel functional supramolecular host molecules.
- Machine learning,
- Host-guest chemistry,
- Molecular recognition,
- Binding constants
1. [1]
  Z. Wu, B. Ramsundar, E.N. Feinberg, et al., Chem. Sci. 9 (2018) 513–530.
2. [2]
  Q. Yang, Y. Li, J.D. Yang, et al., Angew. Chem. Int. Ed. 59 (2020) 19282–19291. doi: 10.1002/anie.202008528
3. [3]
  L.M. Sigmund, S.S. Shree, A. Albers, et al., Angew. Chem. Int. Ed. 63 (2024) e202401084.
4. [4]
  A.F. Zahrt, J.J. Henle, B.T. Rose, et al., Science 363 (2019) eaau5631.
5. [5]
  L.C. Xu, S.Q. Zhang, X. Li, et al., Angew. Chem. Int. Ed. 60 (2021) 22804–22811. doi: 10.1002/anie.202106880
6. [6]
  S.W. Li, L.C. Xu, C. Zhang, et al., Nat. Commun. 14 (2023) 3569–3580.
7. [7]
  Z.J. Zhang, S.W. Li, J.C.A. Oliveira, et al., Nat. Commun. 14 (2023) 3149–4157.
8. [8]
  X. Hong, Q. Yang, K. Liao, et al., Sci. China Chem. 67 (2024) 2461–2496. doi: 10.1007/s11426-024-2072-4
9. [9]
  D.J. Cram, Angew. Chem. Int. Ed. 27 (1988) 1009–1020.
10. [10]
  C.J. Pedersen, Angew. Chem. Int. Ed. 27 (1988) 1021–1027.
11. [11]
  J.M. Lehn, Angew. Chem. Int. Ed. 27 (1988) 89–112.
12. [12]
  D.X. Wang, Q.Y. Zheng, Q.Q. Wang, et al., Angew. Chem. Int. Ed. 47 (2008) 7485–7488. doi: 10.1002/anie.200801705
13. [13]
  D.E. Koshland, Angew. Chem. Int. Ed. 33 (2003) 2375–2378.
14. [14]
  I. Goldberg, J. Am. Chem. Soc. 102 (1980) 4106–4113. doi: 10.1021/ja00532a021
15. [15]
  G.W. Gokel, L.J. Barbour, R. Ferdani, J. Hu, Acc. Chem. Res. 35 (2002) 878–886.
16. [16]
  J.M. Parrilla-Gutiérrez, J.M. Granda, J.F. Ayme, et al., Nat. Comput. Sci. 4 (2024) 200–209. doi: 10.1038/s43588-024-00602-x
17. [17]
  C.J. Pedersen, J. Am. Chem. Soc. 89 (1967) 7017–7036. doi: 10.1021/ja01002a035
18. [18]
  C.J. Pedersen, J. Am. Chem. Soc. 89 (1967) 2495–2496. doi: 10.1021/ja00986a052
19. [19]
  J.S. Bradshaw, R.M. Izatt, Acc. Chem. Res. 30 (1997) 338–345.
20. [20]
  S. Matile, A.V. Jentzsch, J. Montenegro, A. Fin, Chem. Soc. Rev. 40 (2011) 2453–2474. doi: 10.1039/c0cs00209g
21. [21]
  C.A. Schalley, K. Biezai, F. Vögtle, Acc. Chem. Res. 34 (2001) 465–476.
22. [22]
  F.M. Raymo, J.F. Stoddart, Chem. Rev. 99 (1999) 1643–1663.
23. [23]
  J.P. Sauvage, Acc. Chem. Res. 31 (1998) 611–619.
24. [24]
  D. Philp, J.F. Stoddart, Angew. Chem. Int. Ed. 35 (2003) 1154–1196.
25. [25]
  J.D. Badjić, V. Balzani, A. Credi, S. Silvi, J.F. Stoddart, Science 303 (2004) 1845–1849.
26. [26]
  S. Erbas-Cakmak, D.A. Leigh, C.T. McTernan, A.L. Nussbaumer, Chem. Rev. 115 (2015) 10081–10206. doi: 10.1021/acs.chemrev.5b00146
27. [27]
  R.M. Izatt, J.S. Bradshaw, S.A. Nielsen, et al., Chem. Rev. 85 (1985) 271–339. doi: 10.1021/cr00068a003
28. [28]
  R.M. Izatt, K. Pawlak, J.S. Bradshaw, R.L. Bruening, Chem. Rev. 91 (1991) 1721–2085. doi: 10.1021/cr00008a003
29. [29]
  D. Weininger, J. Chem. Inf. Comput. Sci. 28 (1988) 31–36. doi: 10.1021/ci00057a005
30. [30]
  D. Weininger, A. Weininger, J.L. Weininger, J. Chem. Inf. Comput. Sci. 29 (1989) 97–101. doi: 10.1021/ci00062a008
31. [31]
  D. Weininger, J. Chem. Inf. Comput. Sci. 30 (1990) 237–243. doi: 10.1021/ci00067a005
32. [32]
  RDKit, http://www.rdkit.org.
33. [33]
  D. Rogers, M. Hahn, J. Chem. Inf. Model. 50 (2010) 742–754. doi: 10.1021/ci100050t
34. [34]
  L. Himanen, M.O.J. Jäger, E.V. Morooka, et al., Comput. Phys. Commun. 247 (2020) 106949 -104960.
35. [35]
  Y.J. Lee, T.H. Ho, C.C. Lai, S.H. Chiu, Biomol. Chem. 14 (2016) 1153–1160.
36. [36]
  X.X. Zhang, A.V. Bordunov, J.S. Bradshaw, et al., J. Am. Chem. Soc. 117 (1995) 11507–11511. doi: 10.1021/ja00151a014
37. [37]
  A.V. Bordunov, J.S. Bradshaw, X.X. Zhang, et al., Inorg. Chem. 35 (1996) 7229–7240.
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