Paper
An End-to-End Framework for Molecular Conformation Generation via\n Bilevel Programming
Predicting molecular conformations (or 3D structures) from molecular graphs\nis a fundamental problem in many applications. Most existing approaches are\nusually divided into two steps by first predicting the distances between atoms\nand then generating a 3D structure through optimizing a distance geometry\nproblem. However, the distances predicted with such two-stage approaches may\nnot be able to consistently preserve the geometry of local atomic\nneighborhoods, making the generated structures unsatisfying. In this paper, we\npropose an end-to-end solution for molecular conformation prediction called\nConfVAE based on the conditional variational autoencoder framework.\nSpecifically, the molecular graph is first encoded in a latent space, and then\nthe 3D structures are generated by solving a principled bilevel optimization\nprogram. Extensive experiments on several benchmark data sets prove the\neffectiveness of our proposed approach over existing state-of-the-art\napproaches. Code is available at https://github.com/MinkaiXu/ConfVAE-ICML21\n
Authors: Xu, Minkai · Wang, Wujie · Luo, Shitong · Shi, Chence · Bengio, Yoshua · Gomez-Bombarelli, Rafael · Tang, Jian