Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Gupta, Jyoti K; Adams, Dave J; Berry, Neil G (2016)
Publisher: Royal Society of Chemistry
Languages: English
Types: Article
The self-assembly of low molecular weight gelators to form gels has enormous potential for cell culturing, optoelectronics, sensing, and for the preparation of structured materials. There is an enormous “chemical space” of gelators. Even within one class, functionalised dipeptides, there are many structures based on both natural and unnatural amino acids that can be proposed and there is a need for methods that can successfully predict the gelation propensity of such molecules. We have successfully developed computational models, based on experimental data, which are robust and are able to identify in silico dipeptide structures that can form gels. A virtual computational screen of 2025 dipeptide candidates identified 9 dipeptides that were synthesised and tested. Every one of the 9 dipeptides synthesised and tested were correctly predicted for their gelation properties. This approach and set of tools enables the “dipeptide space” to be searched effectively and efficiently in order to deliver novel gelator molecules.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1 P. Terech and R. G. Weiss, Chem. Rev., 1997, 97, 3133-3160.
    • 2 R. G. Weiss, J. Am. Chem. Soc., 2014, 136, 7519-7530.
    • 3 N. Zweep and J. H. van Esch, in Functional Molecular Gels, The Royal Society of Chemistry, 2014, pp. 1-29.
    • 4 W. T. Truong, L. Lewis and P. Thordarson, in Functional Molecular Gels, The Royal Society of Chemistry, 2014, pp. 157-194.
    • 5 J. Puigmarti-Luis and D. B. Amabilino, in Functional Molecular Gels, The Royal Society of Chemistry, 2014, pp. 195-254.
    • 6 T. Kar and P. K. Das, in Functional Molecular Gels, The Royal Society of Chemistry, 2014, pp. 255-303.
    • 7 M. de Loos, B. L. Feringa and J. H. van Esch, Eur. J. Org. Chem., 2005, 3615-3631.
    • 8 D. M. Zurcher and A. J. McNeil, J. Org. Chem., 2015, 80, 2473- 2478.
    • 9 K. A. Houton, K. L. Morris, L. Chen, M. Schmidtmann, J. T. A. Jones, L. C. Serpell, G. O. Lloyd and D. J. Adams, Langmuir, 2012, 28, 9797-9806.
    • 10 M. L. Muro-Small, J. Chen and A. J. McNeil, Langmuir, 2011, 27, 13248-13253.
    • 11 D. J. Adams, K. Morris, L. Chen, L. C. Serpell, J. Bacsa and G. M. Day, So Matter, 2010, 6, 4144-4156.
    • 12 K. K. Diehn, H. Oh, R. Hashemipour, R. G. Weiss and S. R. Raghavan, So Matter, 2014, 10, 2632-2640.
    • 13 J. Bonnet, G. Suissa, M. Raynal and L. Bouteiller, So Matter, 2014, 10, 3154-3160.
    • 14 M. Raynal and L. Bouteiller, Chem. Commun., 2011, 47, 8271- 8273.
    • 15 Y. Lan, M. G. Corradini, R. G. Weiss, S. R. Raghavan and M. A. Rogers, Chem. Soc. Rev., 2015, 44, 6035-6058.
    • 16 T. K. Adalder and P. Dastidar, Cryst. Growth Des., 2014, 14, 2254-2262.
    • 17 K. N. King and A. J. McNeil, Chem. Commun., 2010, 46, 3511- 3513.
    • 18 W. J. M. FrederixPim, G. G. Scott, Y. M. Abul-Haija, D. Kalafatovic, C. G. Pappas, N. Javid, N. T. Hunt, R. V. Ulijn and T. Tuttle, Nat. Chem., 2014, 7, 30-37.
    • 19 S. Fleming and R. V. Ulijn, Chem. Soc. Rev., 2014, 43, 8150- 37 A. Schu¨ller, V. H¨ahnke and G. Schneider, QSAR Comb. Sci., 8177. 2007, 26, 407-410.
    • 20 E. K. Johnson, D. J. Adams and P. J. Cameron, J. Mater. 38 Y. Pocker and E. Green, J. Am. Chem. Soc., 1973, 95, 113-119. Chem., 2011, 21, 2024-2027. 39 D. J. Adams, M. F. Butler, W. J. Frith, M. Kirkland, L. Mullen
    • 21 D. B. Boyd, in Reviews in Computational Chemistry, John and P. Sanderson, So Matter, 2009, 5, 1856-1862. Wiley & Sons, Inc., 2007, pp. 355-371. 40 J. Raeburn, A. Zamith Cardoso and D. J. Adams, Chem. Soc.
    • 22 A. R. Katritzky, M. Kuanar, S. Slavov, C. D. Hall, M. Karelson, Rev., 2013, 42, 5143-5156. I. Kahn and D. A. Dobchev, Chem. Rev., 2010, 110, 5714- 41 A. Karatzoglou, A. Smola, K. Hornik and A. Zeileis, Journal of 5789. Statistical Soware, 2004, 11, 20.
    • 23 R. Todeschini and V. Consonni, Handbook of Molecular 42 L. Breiman, Machine Learning, 2001, vol. 45, pp. 5-32. Descriptors, WILEY-VCH, 2008. 43 W. N. Venables and B. D. Ripley, Modern Applied Statistics
    • 24 J. B. O. Mitchell, Wiley Interdiscip. Rev.: Comput. Mol. Sci., with S., Springer, 4th edn, 2002. 2014, 4, 468-481. 44 B. H. Mevik, R. Wehrens and L. Hovde, Partial Least Squares
    • 25 A. Tropsha, Mol. Inf., 2010, 29, 476-488. and Principal Component Regression. R package version 2.5-0.,
    • 26 L. Chen, S. Revel, K. Morris, L. C. Serpell and D. J. Adams, http://CRAN.R-project.org/package¼plshttp://CRAN.R-project. Langmuir, 2010, 26, 13466-13471. org/package¼pls, Accessed 3/10/2015.
    • 27 L. Chen, T. O. McDonald and D. J. Adams, RSC Adv., 2013, 3, 45 C. Weihs, U. Ligges, K. Luebke and N. Raabe, Data Analysis 8714-8720. and Decision Support, Springer Verlag, Berlin, 2005.
    • 28 D. J. Adams, L. M. Mullen, M. Berta, L. Chen and W. J. Frith, 46 M. Kuhn, S. Weston, N. Coulter and M. Culp, C5.0: C5.0 So Matter, 2010, 6, 1971-1980. Decision Trees and Rule-Based Models. R package version
    • 29 S. Awhida, E. R. Draper, T. O. McDonald and D. J. Adams, J. 0.1.0, http://CRAN.R-project.org/package¼C50, Accessed 3/ Colloid Interface Sci., 2015, 455, 24-31. 10/2015.
    • 30 E. R. Draper, T. O. McDonald and D. J. Adams, Chem. 47 M. Feher, Drug Discovery Today, 2006, 11, 421-428. Commun., 2015, 51, 12827-12830. 48 M. Kuhn and K. Johnson, Applied Predictive Modelling,
    • 31 http://www.cambridgeso.com/soware/overview.aspx, Acces- Springer, New York, 2013. sed 3/10/2015. 49 P. Czodrowski, J. Comput.-Aided Mol. Des., 2014, 28, 1049-
    • 32 http://accelrys.com/products/collaborative-science/biovia- 1055. pipeline-pilot/. 50 X. Robin, N. Turck, A. Hainard, N. Tiberti, F. Lisacek,
    • 33 M. Kuhn, J. Wing, S. Weston, A. Williams, C. Keefer, J.-C. Sanchez and M. Mueller, BMC Bioinf., 2011, 12, 1-8. A. Engelhardt, T. Cooper, Z. Mayer, B. Kenkel, R. C. Team, 51 C. Ru¨cker, G. Ru¨cker and M. Meringer, J. Chem. Inf. Model., M. Benesty, R. Lescarbeau, A. Ziem and L. Scrucca, caret: 2007, 47, 2345-2357. Classication and Regression Training. R package version 6.0- 52 V. Jayawarna, M. Ali, T. A. Jowitt, A. F. Miller, A. Saiani, 52, http://CRAN.R-project.org/package¼caretAccessed 3/10/ J. E. Gough and R. V. Ulijn, Adv. Mater., 2006, 18, 611-614. 2015. 53 Z. M. Yang, G. L. Liang, M. L. Ma, Y. Gao and B. Xu, J. Mater.
    • 34 R. C. Team, R Foundation for Statistical Computing, 2015. Chem., 2007, 17, 850-854.
    • 35 A. Golbraikh, E. Muratov, D. Fourches and A. Tropsha, J. 54 A. D. Martin, J. P. Wojciechowski, M. M. Bhadbhade and Chem. Inf. Model., 2014, 54, 1-4. P. Thordarson, Langmuir, 2016, 32, 2245-2250.
    • 36 D. J. Hand and C. Anagnostopoulos, Pattern Recognit. Lett., 55 Y. Huang, Z. Qiu, Y. Xu, J. Shi, H. Lin and Y. Zhang, Org. 2014, 40, 41-46. Biomol. Chem., 2011, 9, 2149-2155.
  • No related research data.
  • No similar publications.

Share - Bookmark

Funded by projects

Cite this article