EquiBoost: An Equivariant Boosting Approach to Molecular Conformation Generation

TL;DR

EquiBoost is a novel boosting framework using equivariant graph transformers that improves molecular conformation generation by balancing accuracy and efficiency without relying on diffusion models.

Contribution

It introduces a boosting approach with equivariant graph transformers for molecular conformation generation, outperforming diffusion-based methods in quality and diversity.

Findings

Achieves better Average Minimum RMSD (AMR) on GEOM datasets.

Balances accuracy and efficiency effectively.

Rejuvenates boosting as a robust alternative to diffusion models.

Abstract

Molecular conformation generation plays key roles in computational drug design. Recently developed deep learning methods, particularly diffusion models have reached competitive performance over traditional cheminformatical approaches. However, these methods are often time-consuming or require extra support from traditional methods. We propose EquiBoost, a boosting model that stacks several equivariant graph transformers as weak learners, to iteratively refine 3D conformations of molecules. Without relying on diffusion techniques, EquiBoost balances accuracy and efficiency more effectively than diffusion-based methods. Notably, compared to the previous state-of-the-art diffusion method, EquiBoost improves generation quality and preserves diversity, achieving considerably better precision of Average Minimum RMSD (AMR) on the GEOM datasets. This work rejuvenates boosting and sheds light on its potential to be a robust alternative to diffusion models in certain scenarios.