用提升法生成分子构象，速度快且精度更高
EquiBoost: An Equivariant Boosting Approach to Molecular Conformation Generation
- 用等变图变换器堆叠成弱学习器，逐步优化分子三维结构
- 在GEOM数据集上平均最小RMSD精度显著优于当前最佳扩散模型
- 适合追求高效与高精度的药物设计场景
分子构象生成在计算药物设计中起关键作用。近年来，深度学习方法，尤其是扩散模型，已达到与传统化学信息学方法相当的性能，但通常耗时较长或需依赖传统方法支持。我们提出EquiBoost，一种基于提升法的模型，通过堆叠多个等变图变压器作为弱学习器，迭代优化分子的3D构象。该方法不依赖扩散技术，在准确率与效率之间实现更优平衡，显著优于现有扩散基方法。特别地，在GEOM数据集上，EquiBoost在平均最小RMSD（AMR）精度上表现更佳，同时保持构象多样性。本工作重振了提升法的应用，揭示其在特定场景下可成为扩散模型的可靠替代方案。
原文摘要 · Abstract (English)
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.
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