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.

翻译：分子构象生成在计算药物设计中扮演着关键角色。近期发展的深度学习方法，特别是扩散模型，其性能已超越传统化学信息学方法，达到具有竞争力的水平。然而，这些方法通常耗时较长，或需要传统方法的额外支持。我们提出EquiBoost，一种提升模型，它将多个等变图Transformer作为弱学习器进行堆叠，以迭代优化分子的三维构象。在不依赖扩散技术的情况下，EquiBoost比基于扩散的方法更有效地平衡了准确性与效率。值得注意的是，与先前最先进的扩散方法相比，EquiBoost提高了生成质量并保持了多样性，在GEOM数据集上实现了显著更优的平均最小均方根偏差（AMR）精度。这项工作为提升方法注入了新的活力，并揭示了其在某些场景下作为扩散模型稳健替代方案的潜力。