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.

该论文提出了一种基于条件变分自编码器框架的端到端的分子构象预测解决方案，称为 ConfVAE，通过解决一个有原则的双层优化程序来生成 3D 结构，并在几个基准数据集上进行广泛的实验证明了该方法的有效性。