An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming

Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gomez-Bombarelli, Jian Tang
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:11537-11547, 2021.

Abstract

Predicting molecular conformations (or 3D structures) from molecular graphs is a fundamental problem in many applications. Most existing approaches are usually divided into two steps by first predicting the distances between atoms and then generating a 3D structure through optimizing a distance geometry problem. However, the distances predicted with such two-stage approaches may not be able to consistently preserve the geometry of local atomic neighborhoods, making the generated structures unsatisfying. In this paper, we propose an end-to-end solution for molecular conformation prediction called ConfVAE based on the conditional variational autoencoder framework. Specifically, the molecular graph is first encoded in a latent space, and then the 3D structures are generated by solving a principled bilevel optimization program. Extensive experiments on several benchmark data sets prove the effectiveness of our proposed approach over existing state-of-the-art approaches. Code is available at \url{https://github.com/MinkaiXu/ConfVAE-ICML21}.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-xu21f, title = {An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming}, author = {Xu, Minkai and Wang, Wujie and Luo, Shitong and Shi, Chence and Bengio, Yoshua and Gomez-Bombarelli, Rafael and Tang, Jian}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {11537--11547}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/xu21f/xu21f.pdf}, url = {https://proceedings.mlr.press/v139/xu21f.html}, abstract = {Predicting molecular conformations (or 3D structures) from molecular graphs is a fundamental problem in many applications. Most existing approaches are usually divided into two steps by first predicting the distances between atoms and then generating a 3D structure through optimizing a distance geometry problem. However, the distances predicted with such two-stage approaches may not be able to consistently preserve the geometry of local atomic neighborhoods, making the generated structures unsatisfying. In this paper, we propose an end-to-end solution for molecular conformation prediction called ConfVAE based on the conditional variational autoencoder framework. Specifically, the molecular graph is first encoded in a latent space, and then the 3D structures are generated by solving a principled bilevel optimization program. Extensive experiments on several benchmark data sets prove the effectiveness of our proposed approach over existing state-of-the-art approaches. Code is available at \url{https://github.com/MinkaiXu/ConfVAE-ICML21}.} }
Endnote
%0 Conference Paper %T An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming %A Minkai Xu %A Wujie Wang %A Shitong Luo %A Chence Shi %A Yoshua Bengio %A Rafael Gomez-Bombarelli %A Jian Tang %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-xu21f %I PMLR %P 11537--11547 %U https://proceedings.mlr.press/v139/xu21f.html %V 139 %X Predicting molecular conformations (or 3D structures) from molecular graphs is a fundamental problem in many applications. Most existing approaches are usually divided into two steps by first predicting the distances between atoms and then generating a 3D structure through optimizing a distance geometry problem. However, the distances predicted with such two-stage approaches may not be able to consistently preserve the geometry of local atomic neighborhoods, making the generated structures unsatisfying. In this paper, we propose an end-to-end solution for molecular conformation prediction called ConfVAE based on the conditional variational autoencoder framework. Specifically, the molecular graph is first encoded in a latent space, and then the 3D structures are generated by solving a principled bilevel optimization program. Extensive experiments on several benchmark data sets prove the effectiveness of our proposed approach over existing state-of-the-art approaches. Code is available at \url{https://github.com/MinkaiXu/ConfVAE-ICML21}.
APA
Xu, M., Wang, W., Luo, S., Shi, C., Bengio, Y., Gomez-Bombarelli, R. & Tang, J.. (2021). An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:11537-11547 Available from https://proceedings.mlr.press/v139/xu21f.html.

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