Fetching the paper…
Reading the bibliography…
From AlexNet to Inception, autoencoders to diffusion models, the development of novel and powerful deep learning models and learning algorithms has proceeded at breakneck speeds.
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
1988
Earlier work this paper cites.
H. Edelsbrunner, J. Harer et al. , “Persistent homology-a survey,” Contemporary mathematics , vol. 453, pp. 257–282, 2008
2008
Earlier work this paper cites.
T. L. Griffiths, N. Chater, C. Kemp, A. Perfors, and J. B. Tenenbaum, “Probabilistic models of cognition: Exploring representations and inductive biases,” Trends in cognitive sciences , vol. 14, no. 8, pp. 357–364, 2010
2010
Earlier work this paper cites.
P. Ramachandran and G. Varoquaux, “Mayavi: 3d visualization of scientific data,” Computing in Science & Engineering , vol. 13, no. 2, pp. 40–51, 2011
2011
Earlier work this paper cites.
E. E. Bolton, J. Chen, S. Kim, L. Han, S. He, W. Shi, V. Simonyan, Y. Sun, P. A. Thiessen, J. Wang et al. , “Pubchem3d: a new resource for scientists,” Journal of cheminformatics , vol. 3, no. 1, pp. 1–15, 2011
2011
Earlier work this paper cites.
V. De Silva, D. Morozov, and M. Vejdemo-Johansson, “Dualities in persistent (co) homology,” Inverse Problems , vol. 27, no. 12, p. 124003, 2011
2011
Earlier work this paper cites.
M. Rupp, A. Tkatchenko, K.-R. Müller, and O. A. Von Lilienfeld, “Fast and accurate modeling of molecular atomization energies with machine learning,” Physical review letters , vol. 108, no. 5, p. 058301, 2012
2012
Earlier work this paper cites.
G. Montavon, K. Hansen, S. Fazli, M. Rupp, F. Biegler, A. Ziehe, A. Tkatchenko, A. Lilienfeld, and K.-R. Müller, “Learning invariant representations of molecules for atomization energy prediction,” Advances in neural information processing systems , vol. 25, 2012
2012
Earlier work this paper cites.
G. Landrum et al. , “Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling,” 2013
2013
Earlier work this paper cites.
H. Schöpf and P. Supancic, “On bürmann’s theorem and its application to problems of linear and nonlinear heat transfer and diffusion,” The Mathematica Journal , vol. 16, no. 11, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/
2015
Earlier work this paper cites.
S. K. Lam, A. Pitrou, and S. Seibert, “Numba: A llvm-based python jit compiler,” in Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC , 2015, pp. 1–6
2015
Earlier work this paper cites.
N. Maho, “The PubChemQC project: A large chemical database from the first principle calculations,” in AIP conference proceedings , vol. 1702, no. 1. AIP Publishing LLC, 2015, p. 090058
2015
Earlier work this paper cites.
S. Kim, P. A. Thiessen, E. E. Bolton, J. Chen, G. Fu, A. Gindulyte, L. Han, J. He, S. He, B. A. Shoemaker et al. , “PubChem substance and compound databases,” Nucleic acids research , vol. 44, no. D1, pp. D1202–D1213, 2016
2016
Cited alongside, same era.
2017
Cited alongside, same era.
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in International conference on machine learning . PMLR, 2017, pp. 1263–1272
2017
Cited alongside, same era.
K. Schütt, P.-J. Kindermans, H. E. Sauceda Felix, S. Chmiela, A. Tkatchenko, and K.-R. Müller, “Schnet: A continuous-filter convolutional neural network for modeling quantum interactions,” Advances in neural information processing systems , vol. 30, 2017
2017
M. Krenn, F. Häse, A. Nigam, P. Friederich, and A. Aspuru-Guzik, “Self-referencing embedded strings (selfies): A 100% robust molecular string representation,” Machine Learning: Science and Technology , vol. 1, no. 4, p. 045024, 2020
2020
Later among the works it cites.
F. Zhuang, Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, H. Xiong, and Q. He, “A comprehensive survey on transfer learning,” Proceedings of the IEEE , vol. 109, no. 1, pp. 43–76, 2020
2020
Later among the works it cites.
B. Kim, S. Lee, and J. Kim, “Inverse design of porous materials using artificial neural networks,” Science advances , vol. 6, no. 1, p. eaax9324, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
M. Nakata and T. Shimazaki, “PubChemQC project: a large-scale first-principles electronic structure database for data-driven chemistry,” Journal of chemical information and modeling , vol. 57, no. 6, pp. 1300–1308, 2017
2017
Cited alongside, same era.
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.
W. Jin, R. Barzilay, and T. Jaakkola, “Junction tree variational autoencoder for molecular graph generation,” in International conference on machine learning . PMLR, 2018, pp. 2323–2332
2018
Cited alongside, same era.
2018
Cited alongside, same era.
T. Xie and J. C. Grossman, “Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties,” Physical review letters , vol. 120, no. 14, p. 145301, 2018
2018
Cited alongside, same era.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4401–4410
2019
Cited alongside, same era.
2019
Cited alongside, same era.
C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del Río, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant, “Array programming with NumPy,” Nature , vol. 585, no. 7825, pp. 357–362, Sep. 2020. [Online]. Available: https://doi.org/10.1038/s41586-020-2649-2
2020
Later among the works it cites.
2020
Later among the works it cites.
D. Polykovskiy, A. Zhebrak, B. Sanchez-Lengeling, S. Golovanov, O. Tatanov, S. Belyaev, R. Kurbanov, A. Artamonov, V. Aladinskiy, M. Veselov et al. , “Molecular sets (moses): a benchmarking platform for molecular generation models,” Frontiers in pharmacology , vol. 11, p. 1931, 2020
2020
Later among the works it cites.
S. Wang, J. Witek, G. A. Landrum, and S. Riniker, “Improving conformer generation for small rings and macrocycles based on distance geometry and experimental torsional-angle preferences,” Journal of chemical information and modeling , vol. 60, no. 4, pp. 2044–2058, 2020
2020
Later among the works it cites.
M. Nakata, T. Shimazaki, M. Hashimoto, and T. Maeda, “Pubchemqc pm6: data sets of 221 million molecules with optimized molecular geometries and electronic properties,” Journal of Chemical Information and Modeling , vol. 60, no. 12, pp. 5891–5899, 2020
2020
Later among the works it cites.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International Conference on Machine Learning . PMLR, 2021, pp. 8821–8831
2021
Later among the works it cites.
2021
Later among the works it cites.
Z. Yao, B. Sánchez-Lengeling, N. S. Bobbitt, B. J. Bucior, S. G. H. Kumar, S. P. Collins, T. Burns, T. K. Woo, O. K. Farha, R. Q. Snurr et al. , “Inverse design of nanoporous crystalline reticular materials with deep generative models,” Nature Machine Intelligence , vol. 3, no. 1, pp. 76–86, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.