标题： EquiBoost：一种用于分子构象生成的等变提升方法 显示英文标题

标题： EquiBoost: An Equivariant Boosting Approach to Molecular Conformation Generation

摘要： 分子构象生成在计算药物设计中起着关键作用。最近开发的深度学习方法，特别是扩散模型，在与传统化学信息学方法相比方面达到了具有竞争力的性能。然而，这些方法通常耗时较长或需要传统方法的额外支持。我们提出了EquiBoost，一种增强模型，它将多个等变图变压器作为弱学习器进行堆叠，以迭代地优化分子的3D构象。无需依赖扩散技术，EquiBoost在准确性和效率之间取得了更好的平衡，优于基于扩散的方法。值得注意的是，与之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好的平均最小RMSD（AMR）精度。这项工作使增强方法焕发新生，并为其在某些场景下成为扩散模型的稳健替代方案提供了启示。 显示英文摘要

摘要： 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.