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.
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Notes
- 1.
The prior is a standard normal in this paper.
- 2.
SAS = fragmentScore − complexityPenalty.
- 3.
Available at https://github.com/nicola-decao/MolGAN.
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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).
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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
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