Path-Aware and Structure-Preserving Generation of Synthetically Accessible Molecules

Juhwan Noh, Dae-Woong Jeong, Kiyoung Kim, Sehui Han, Moontae Lee, Honglak Lee, Yousung Jung
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:16952-16968, 2022.

Abstract

Computational chemistry aims to autonomously design specific molecules with target functionality. Generative frameworks provide useful tools to learn continuous representations of molecules in a latent space. While modelers could optimize chemical properties, many generated molecules are not synthesizable. To design synthetically accessible molecules that preserve main structural motifs of target molecules, we propose a reaction-embedded and structure-conditioned variational autoencoder. As the latent space jointly encodes molecular structures and their reaction routes, our new sampling method that measures the path-informed structural similarity allows us to effectively generate structurally analogous synthesizable molecules. When targeting out-of-domain as well as in-domain seed structures, our model generates structurally and property-wisely similar molecules equipped with well-defined reaction paths. By focusing on the important region in chemical space, we also demonstrate that our model can design new molecules with even higher activity than the seed molecules.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-noh22a, title = {Path-Aware and Structure-Preserving Generation of Synthetically Accessible Molecules}, author = {Noh, Juhwan and Jeong, Dae-Woong and Kim, Kiyoung and Han, Sehui and Lee, Moontae and Lee, Honglak and Jung, Yousung}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {16952--16968}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/noh22a/noh22a.pdf}, url = {https://proceedings.mlr.press/v162/noh22a.html}, abstract = {Computational chemistry aims to autonomously design specific molecules with target functionality. Generative frameworks provide useful tools to learn continuous representations of molecules in a latent space. While modelers could optimize chemical properties, many generated molecules are not synthesizable. To design synthetically accessible molecules that preserve main structural motifs of target molecules, we propose a reaction-embedded and structure-conditioned variational autoencoder. As the latent space jointly encodes molecular structures and their reaction routes, our new sampling method that measures the path-informed structural similarity allows us to effectively generate structurally analogous synthesizable molecules. When targeting out-of-domain as well as in-domain seed structures, our model generates structurally and property-wisely similar molecules equipped with well-defined reaction paths. By focusing on the important region in chemical space, we also demonstrate that our model can design new molecules with even higher activity than the seed molecules.} }
Endnote
%0 Conference Paper %T Path-Aware and Structure-Preserving Generation of Synthetically Accessible Molecules %A Juhwan Noh %A Dae-Woong Jeong %A Kiyoung Kim %A Sehui Han %A Moontae Lee %A Honglak Lee %A Yousung Jung %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-noh22a %I PMLR %P 16952--16968 %U https://proceedings.mlr.press/v162/noh22a.html %V 162 %X Computational chemistry aims to autonomously design specific molecules with target functionality. Generative frameworks provide useful tools to learn continuous representations of molecules in a latent space. While modelers could optimize chemical properties, many generated molecules are not synthesizable. To design synthetically accessible molecules that preserve main structural motifs of target molecules, we propose a reaction-embedded and structure-conditioned variational autoencoder. As the latent space jointly encodes molecular structures and their reaction routes, our new sampling method that measures the path-informed structural similarity allows us to effectively generate structurally analogous synthesizable molecules. When targeting out-of-domain as well as in-domain seed structures, our model generates structurally and property-wisely similar molecules equipped with well-defined reaction paths. By focusing on the important region in chemical space, we also demonstrate that our model can design new molecules with even higher activity than the seed molecules.
APA
Noh, J., Jeong, D., Kim, K., Han, S., Lee, M., Lee, H. & Jung, Y.. (2022). Path-Aware and Structure-Preserving Generation of Synthetically Accessible Molecules. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:16952-16968 Available from https://proceedings.mlr.press/v162/noh22a.html.

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