GREYC's Chemistry dataset
ensicaen greyc LCMT


This page contains several archive or links towards various chemical databases of molecules. Each database concerns a specific problem (either classification or Regression).

Dataset # dataset mean size mean degree min size max size Stereoisomerism Problem type
PAH 94 20.7 2.4 10 28 No Classif.
MAO 68 18.4 2.1 11 27 No Classif.
PTC 416 14.4 2.1 2 64 No Classif.
AIDS 2000 15.7 2.1 2 95 No Classif.
Alkane 150 8.9 1.8 1 10 No Regression
Acyclic 185 8.2 1.8 3 11 No Regression
Chiral Acyclic 35 21.29 1.98 14 32 Yes Regression
Vitamin D 69 76.91 2.05 68 88 Yes Regression
ACE 32 52 2.04 52 52 Yes Classification
Steroid 64 75.11 2.08 57 94 Yes Regression
Description of the different chemical datasets provided by GREYC and LCMT laboratories.

Achiral Molecules

Chiral Molecules

References

  1. Bernstein, S., Kauzmann, W. J., & Wallis, E. S. (1941). The relationship between optical rotatory power and constitution of the sterols. The Journal of Organic Chemistry, 6(2), 319-330.
  2. Castillo-Garit, J. A., Marrero-Ponce, Y., Torrens, F., & Rotondo, R. (2007). Atom-based stochastic and non-stochastic 3D-chiral bilinear indices and their applications to central chirality codification . Journal of Molecular Graphics and Modelling, 26(1), 32-47.
  3. Cherqaoui, D., Villemin, D., 1994. Use of a neural network to determine the boiling point of alkanes. J. Chem. Soc. Faraday Trans. 90, 97 102.
  4. Cherqaoui, D., Villemin, D., Mesbah, A., Cense, J.M., Kvasnicka, V., 1994a. Use of a neural network to determine the normal boiling points of acyclic ethers, peroxides, acetals and their sulfur analogues. J. Chem. Soc. Faraday Trans. 90, 2015 2019.
  5. Gaüzère, B., et al. Two new graphs kernels in chemoinformatics. Pattern Recognition Lett. (2012), http://dx.doi.org/10.1016/j.patrec.2012.03.020.
  6. Grenier, Pierre-Anthony, Brun, Luc & Villemin, Didier. Treelet Kernel Incorporating Chiral Information. In 9th IAPR-TC15 International Workshop on Graph-based Representations in Pattern Recognition , Vienne, Autriche , May 2013
  7. Mahé, P., Vert, J.-P., 2008. Graph kernels based on tree patterns for molecules. Machine Learn. 75 (1), 3 35.
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  9. dd Riesen, K., Neuhaus, M., Bunke, H., 2007. Graph embedding in vector spaces by means of prototype selection. In: Escolano, F., Vento, M. (Eds.), 6th IAPR-TC15 Internat. Workshop GbRPR 2007. IAPR TC15. Springer-Verlag, pp. 383 393.
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  11. Vishwanathan, S., Borgwardt, K.M., Kondor, I.R., Schraudolph, N.N., 2010. Graph kernels. J. Machine Learn. Res. 11, 1201 1242.
  12. J. Brown, T. Urata, T. Tamura, M. A. Arai, T. Kawa- bata, and T. Akutsu. Compound analysis via graph ker- nels incorporating chirality. Journal of Bioinformatics and Computational Biology , 8(1) :63–81, 2010
  13. Grenier, Pierre-Anthony, Brun, Luc & Villemin, Didier. Incorporating Molecule's Stereisomerism within the Machine Learning Framework. In Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2014, Joensuu, Finland, August 20-22, 2014. Proceedings , pages 12--21 2014 .
  14. Grenier, Pierre-Anthony, Brun, Luc & Villemin, Didier. A Graph Kernel incorporating molecule's stereisomerism information. In Proceedings of ICPR 2014 , pages - , Stockholm, Suede , Aug 2014 .
  15. Grenier, Pierre-Anthony, Brun, Luc & Villemin, Didier. From bags to graphs of stereo subgraphs in order to predict molecule’s properties In Proceedings of GbR2015, to be published by LNCS.