An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
This addresses the challenge of generating accurate molecular structures for applications in chemistry and drug discovery, representing an incremental improvement over existing two-stage methods.
The paper tackles the problem of predicting molecular conformations from molecular graphs by proposing ConfVAE, an end-to-end framework that uses a conditional variational autoencoder and bilevel optimization to generate 3D structures, achieving state-of-the-art results on benchmark datasets.
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 https://github.com/MinkaiXu/ConfVAE-ICML21