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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

Wed Jul 21 07:20 PM -- 07:25 PM (PDT) @ None

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}.

Author Information

Minkai Xu (University of Montreal)
Wujie Wang (Massachusetts Institute of Technology)
Shitong Luo (Peking University)
Chence Shi (University of Montreal)
Yoshua Bengio (Mila - Quebec AI Institute)
Rafael Gomez-Bombarelli (MIT)
Jian Tang (HEC Montreal & MILA)

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