Fetching the paper…
Reading the bibliography…
Graph neural networks have recently become a standard method for analysing chemical compounds.
M. J. Kusner, B. Paige, and J. M. Hernández-Lobato, “Grammar variational autoencoder,” in International Conference on Machine Learning . PMLR, 2017, pp. 1945–1954
1954
Earlier work this paper cites.
D. Weininger, “Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules,” Journal of Chemical Information and Computer Sciences , vol. 28, no. 1, pp. 31–36, 1988. [Online]. Available: https://pubs.acs.org/doi/abs/10.1021/ci00057a005
1988
Earlier work this paper cites.
G. W. Bemis and M. A. Murcko, “The properties of known drugs. 1. molecular frameworks,” Journal of medicinal chemistry , vol. 39, no. 15, pp. 2887–2893, 1996
1996
Earlier work this paper cites.
M. Ester, H.-P. Kriegel, J. Sander, X. Xu et al. , “A density-based algorithm for discovering clusters in large spatial databases with noise.” in Kdd , vol. 96, no. 34, 1996, pp. 226–231
1996
Earlier work this paper cites.
H. Kubinyi, “Qsar and 3d qsar in drug design part 1: methodology,” Drug discovery today , vol. 2, no. 11, pp. 457–467, 1997
1997
Earlier work this paper cites.
J. S. Delaney, “Esol: estimating aqueous solubility directly from molecular structure,” Journal of chemical information and computer sciences , vol. 44, no. 3, pp. 1000–1005, 2004
2004
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” Journal of machine learning research , vol. 9, no. 11, 2008
2008
Earlier work this paper cites.
D. Rogers and M. Hahn, “Extended-connectivity fingerprints,” Journal of chemical information and modeling , vol. 50, no. 5, pp. 742–754, 2010
2010
Earlier work this paper cites.
L. Ruddigkeit, R. Van Deursen, L. C. Blum, and J.-L. Reymond, “Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17,” Journal of chemical information and modeling , vol. 52, no. 11, pp. 2864–2875, 2012
2012
Earlier work this paper cites.
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, “Convolutional networks on graphs for learning molecular fingerprints,” in Advances in neural information processing systems , 2015, pp. 2224–2232
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
A. L. Perryman, T. P. Stratton, S. Ekins, and J. S. Freundlich, “Predicting mouse liver microsomal stability with “pruned” machine learning models and public data,” Pharmaceutical research , vol. 33, no. 2, pp. 433–449, 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
S. Kearnes, K. McCloskey, M. Berndl, V. Pande, and P. Riley, “Molecular graph convolutions: moving beyond fingerprints,” J. Comput. Aid. Mol. Des. , vol. 30, no. 8, pp. 595–608, 2016
2016
Cited alongside, same era.
C. W. Coley, R. Barzilay, W. H. Green, T. S. Jaakkola, and K. F. Jensen, “Convolutional embedding of attributed molecular graphs for physical property prediction,” Journal of chemical information and modeling , vol. 57, no. 8, pp. 1757–1772, 2017
2017
Cited alongside, same era.
S. Podlewska and R. Kafel, “Metstabon—online platform for metabolic stability predictions,” International journal of molecular sciences , vol. 19, no. 4, p. 1040, 2018
2018
Later among the works it cites.
K. Yang, K. Swanson, W. Jin, C. Coley, P. Eiden, H. Gao, A. Guzman-Perez, T. Hopper, B. Kelley, M. Mathea et al. , “Analyzing learned molecular representations for property prediction,” Journal of chemical information and modeling , vol. 59, no. 8, pp. 3370–3388, 2019
2019
Later among the works it cites.
O. Laufkötter, N. Sturm, J. Bajorath, H. Chen, and O. Engkvist, “Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability,” Journal of cheminformatics , vol. 11, no. 1, pp. 1–14, 2019
2019
Later among the works it cites.
K. Liu, X. Sun, L. Jia, J. Ma, H. Xing, J. Wu, H. Gao, Y. Sun, F. Boulnois, and J. Fan, “Chemi-net: a molecular graph convolutional network for accurate drug property prediction,” International journal of molecular sciences , vol. 20, no. 14, p. 3389, 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
M. Olivecrona, T. Blaschke, O. Engkvist, and H. Chen, “Molecular de-novo design through deep reinforcement learning,” Journal of cheminformatics , vol. 9, no. 1, pp. 1–14, 2017
2017
Cited alongside, same era.
K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller, “Schnet–a deep learning architecture for molecules and materials,” The Journal of Chemical Physics , vol. 148, no. 24, p. 241722, 2018
2018
Cited alongside, same era.
M. Popova, O. Isayev, and A. Tropsha, “Deep reinforcement learning for de novo drug design,” Science advances , vol. 4, no. 7, p. eaap7885, 2018
2018
Cited alongside, same era.
R. Gómez-Bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams, and A. Aspuru-Guzik, “Automatic chemical design using a data-driven continuous representation of molecules,” ACS central science , vol. 4, no. 2, pp. 268–276, 2018
2018
Cited alongside, same era.
2019
Later among the works it cites.
X. Li, X. Yan, Q. Gu, H. Zhou, D. Wu, and J. Xu, “Deepchemstable: Chemical stability prediction with an attention-based graph convolution network,” Journal of chemical information and modeling , vol. 59, no. 3, pp. 1044–1049, 2019
2019
Later among the works it cites.
2020
Closest in time.
2020
Closest in time.
T. Danel, P. Spurek, J. Tabor, M. Śmieja, Ł. Struski, A. Słowik, and Ł. Maziarka, “Spatial graph convolutional networks,” in International Conference on Neural Information Processing . Springer, 2020, pp. 668–675
2020
Closest in time.
Y. Rong, Y. Bian, T. Xu, W. Xie, Y. Wei, W. Huang, and J. Huang, “Self-supervised graph transformer on large-scale molecular data,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
Closest in time.
Y. Song, S. Zheng, Z. Niu, Z.-H. Fu, Y. Lu, and Y. Yang, “Communicative representation learning on attributed molecular graphs,” in IJCAI , 2020
2020
Closest in time.