[3]
N. M. O’Boyle and A. Dalke, DeepSMILES: An adaptation of SMILES for use in machine-learning of chemical structures. doi:10.26434/chemrxiv.7097960.
[5]
E. J. Bjerrum and R. Threlfall, Molecular generation with recurrent neural networks (RNNs), arXiv preprint arXiv: 1705.04612, 2017.
[10]
M. J. Kusner, B. Paige, and J. M. Hernández-Lobato, Grammar variational autoencoder, in Proc. 34 th Int. Conf. Machine Learning, Sydney, Australia, 2017, pp. 1945–1954.
[11]
H. Dai, Y. Tian, B. Dai, S. Skiena, and L. Song, Syntax-directed variational autoencoder for molecule generation, in Proc. Int. Conf. Learning Representations, https://doi.org/10.48550/arXiv.1802.08786, 2018.
[12]
Q. Liu, M. Allamanis, M. Brockschmidt, and A. L. Gaunt, Constrained graph variational autoencoders for molecule design, in Proc. 32 nd Int. Conf. Neural Information Processing Systems, Montréal, Canada, 2018, pp. 7806–7815.
[13]
W. Jin, R. Barzilay, and T. Jaakkola, Junction tree variational autoencoder for molecular graph generation, in Proc. 35 th Int. Conf. Machine Learning, Stockholm, Sweden, 2018, pp. 2323–2332.
[14]
M. Simonovsky and N. Komodakis, GraphVAE: Towards generation of small graphs using variational autoencoders, in Proc. 27 th Int. Conf. Artificial Neural Networks, Rhodes, Greece, 2018, pp. 412–422.
[15]
G. L. Guimaraes, B. Sanchez-Lengeling, C. Outeiral, P. L. C. Farias, and A. Aspuru-Guzik, Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models, arXiv preprint arXiv: 1705.10843, 2018.
[18]
N. De Cao and T. Kipf, MolGAN: An implicit generative model for small molecular graphs, arXiv preprint arXiv: 1805.11973, 2022.
[19]
L. Dinh, D. Krueger, and Y. Bengio, NICE: Non-linear independent components estimation, arXiv preprint arXiv: 1410.8516, 2015.
[20]
L. Dinh, J. Sohl-Dickstein, and S. Bengio, Density estimation using real NVP, arXiv preprint arXiv: 1605.08803, 2017.
[21]
D. P. Kingma and P. Dhariwal, Glow: Generative flow with invertible 1x1 convolutions, arXiv preprint arXiv: 1807.03039, 2018.
[23]
C. Li, J. Yao, W. Wei, Z. Niu, X. Zeng, J. Li, and J. Wang, Geometry-based molecular generation with deep constrained variational autoencoder, IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2022.3147790.
[24]
C. Ma and X. Zhang, GF-VAE: A flow-based variational autoencoder for molecule generation, in Proc. 30 th ACM Int. Conf. Information & Knowledge Management, Virtual Event, Queensland, Australia, 2021, pp. 1181–1190.
[25]
S. Luo, J. Guan, J. Ma, and J. Peng, A 3D generative model for structure-based drug design, arXiv preprint arXiv: 2203.10446, 2022.
[27]
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, Attention is all you need, in Proc. 31 st Int. Conf. Neural Information Processing Systems, Long Beach, CA, USA, 2017. pp. 6000–6010.
[34]
T. Fu, C. Xiao, and J. Sun, CORE: Automatic molecule optimization using copy & refine strategy, Proc. AAAI Conf. Artif. Intell., vol. 34, no. 1, pp. 638–645, 2020.
[37]
D. Bahdanau, K. Cho, and Y. Bengio, Neural machine translation by jointly learning to align and translate, arXiv preprint arXiv: 1409.0473, 2016.
[44]
M. Swain, MolVS: Molecule validation and standardization, https://pypi.org/project/Molvs, 2018.