Do Deep Learning Methods Really Perform Better in Molecular Conformation Generation?

Gengmo Zhou, Zhifeng Gao, Zhewei Wei, Hang Zheng, Guolin Ke

Keywords: Molecular Conformation Generation, Benchmark

TL;DR: We revisit the benchmark after simple traditional algorithms beat deep learning methods in conformation generation.

Abstract: 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.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2302.07061/code)