  Molecular conformation generation (MCG) is a fundamental and important
problem in drug discovery. Many traditional methods have been developed to
solve the MCG problem, such as systematic searching, model-building, random
searching, distance geometry, molecular dynamics, Monte Carlo methods, etc.
However, they have some limitations depending on the molecular structures.
Recently, there are plenty of deep learning based MCG methods, which claim they
largely outperform the traditional methods. However, to our surprise, we design
a simple and cheap algorithm (parameter-free) based on the traditional methods
and find it is comparable to or even outperforms deep learning based MCG
methods in the widely used GEOM-QM9 and GEOM-Drugs benchmarks. In particular,
our design algorithm is simply the clustering of the RDKIT-generated
conformations. We hope our findings can help the community to revise the deep
learning methods for MCG. The code of the proposed algorithm could be found at
https://gist.github.com/ZhouGengmo/5b565f51adafcd911c0bc115b2ef027c.
