Abstract
Deep generative models for graphs are promising for being able to sidestep expensive search procedures in the huge space of chemical compounds. However, incorporating complex and non-differentiable property metrics into a generative model remains a challenge. In this work, we formulate a differentiable objective to regularize a variational autoencoder model that we design for graphs. Experiments demonstrate that the regularization performs excellently when used for generating molecules since it can not only improve the performance of objectives optimization task but also generate molecules with high quality in terms of validity and novelty.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The prior is a standard normal in this paper.
- 2.
SAS = fragmentScore − complexityPenalty.
- 3.
Available at https://github.com/nicola-decao/MolGAN.
References
Dai, H., Tian, Y., Dai, B., Skiena, S., Song, L.: Syntax-directed variational autoencoder for structured data. In: 6th International Conference on Learning Representations, Conference Track Proceedings, ICLR 2018, Vancouver, BC, Canada, 30 April–3 May 2018. OpenReview.net (2018). https://openreview.net/forum?id=SyqShMZRb
De Cao, N., Kipf, T.: MolGAN: an implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973 (2018)
Duvenaud, D.K., et al.: Convolutional networks on graphs for learning molecular fingerprints. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, Montreal, Quebec, Canada, 7–12 December 2015, pp. 2224–2232 (2015). http://papers.nips.cc/paper/5954-convolutional-networks-on-graphs-for-learning-molecular-fingerprints
Ertl, P., Schuffenhauer, A.: Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminformatics 1, 8 (2009). https://doi.org/10.1186/1758-2946-1-8
Ghose, A.K., Crippen, G.M.: Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. modeling dispersive and hydrophobic interactions. J. Chem. Inf. Comput. Sci. 27(1), 21–35 (1987)
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: Precup and Teh [17], pp. 1263–1272. http://proceedings.mlr.press/v70/
Gómez-Bombarelli, R., et al.: Automatic chemical design using a data-driven continuous representation of molecules. CoRR abs/1610.02415 (2016). http://arxiv.org/abs/1610.02415
Guimaraes, G.L., Sanchez-Lengeling, B., Farias, P.L.C., Aspuru-Guzik, A.: Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models. CoRR abs/1705.10843 (2017). http://arxiv.org/abs/1705.10843
Irwin, J.J., Sterling, T., Mysinger, M.M., Bolstad, E.S., Coleman, R.G.: ZINC: a free tool to discover chemistry for biology. J. Chem. Inf. Model. 52(7), 1757–1768 (2012)
Jin, W., Barzilay, R., Jaakkola, T.: Junction tree variational autoencoder for molecular graph generation. arXiv preprint arXiv:1802.04364 (2018)
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, Conference Track Proceedings, ICLR 2014, Banff, AB, Canada, 14–16 April 2014 (2014). http://arxiv.org/abs/1312.6114
Kusner, M.J., Paige, B., Hernández-Lobato, J.M.: Grammar variational autoencoder. In: Precup and Teh [17], pp. 1945–1954. http://proceedings.mlr.press/v70/kusner17a.html
Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.S.: Gated graph sequence neural networks. In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, Conference Track Proceedings, ICLR 2016, San Juan, Puerto Rico, 2–4 May 2016 (2016). https://iclr.cc/archive/www/doku.php%3Fid=iclr2016:accepted-main.html
Liu, Q., Allamanis, M., Brockschmidt, M., Gaunt, A.: Constrained graph variational autoencoders for molecule design. In: Advances in Neural Information Processing Systems, pp. 7795–7804 (2018)
Ma, T., Chen, J., Xiao, C.: Constrained generation of semantically valid graphs via regularizing variational autoencoders. In: Bengio, S., Wallach, H.M., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, Montréal, Canada, 3–8 December 2018, pp. 7113–7124 (2018). http://papers.nips.cc/paper/7942-constrained-generation-of-semantically-valid-graphs-via-regularizing-variational-autoencoders
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Fürnkranz, J., Joachims, T. (eds.) Proceedings of the 27th International Conference on Machine Learning (ICML 2010), Haifa, Israel, 21–24 June 2010, pp. 807–814. Omnipress (2010). https://icml.cc/Conferences/2010/papers/432.pdf
Precup, D., Teh, Y.W. (eds.): Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6–11 August 2017, Proceedings of Machine Learning Research, vol. 70. PMLR (2017). http://proceedings.mlr.press/v70/
Ramakrishnan, R., Dral, P.O., Rupp, M., Von Lilienfeld, O.A.: Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 1, 140022 (2014)
Ruddigkeit, L., Van Deursen, R., Blum, L.C., Reymond, J.L.: Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J. Chem. Inf. Model. 52(11), 2864–2875 (2012)
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Simonovsky, M., Komodakis, N.: GraphVAE: towards generation of small graphs using variational autoencoders. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11139, pp. 412–422. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01418-6_41
Weininger, D.: Smiles, a chemical language and information system 1 introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28(1), 31–36 (1988). https://doi.org/10.1021/ci00057a005
Wildman, S.A., Crippen, G.M.: Prediction of physicochemical parameters by atomic contributions. J. Chem. Inf. Comput. Sci. 39(5), 868–873 (1999). https://doi.org/10.1021/ci990307l
Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant 61876003. It is a research achievement of Key Laboratory of Science, Techonology and Standard in Press Industry (Key Laboratory of Intelligent Press Media Technology).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, X., Lyu, X., Zhang, H., Hu, K., Tang, Z. (2019). Regularizing Variational Autoencoders for Molecular Graph Generation. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-36802-9_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36801-2
Online ISBN: 978-3-030-36802-9
eBook Packages: Computer ScienceComputer Science (R0)