Molecular conformer generation (MCG) is an important task in cheminformatics and drug discovery. The ability to efficiently generate low-energy 3D structures can avoid expensive quantum mechanical simulations, leading to accelerated screenings and enhanced structural exploration. Several generative models have been developed for MCG, but many struggle to consistently produce high-quality conformers. To address these issues, we introduce CoarsenConf, which coarse-grains molecular graphs based on torsional angles and integrates them into an SE(3)-equivariant hierarchical variational autoencoder. Through equivariant coarse-graining, we aggregate the fine-grained atomic coordinates of subgraphs connected via rotatable bonds, creating a variable-length coarse-grained latent representation. Our model uses a novel aggregated attention mechanism to restore fine-grained coordinates from the coarse-grained latent representation, enabling efficient autoregressive generation of large molecules. Furthermore, our work expands current conformer generation benchmarks and introduces new metrics to better evaluate the quality and viability of generated conformers. We demonstrate that CoarsenConf generates more accurate conformer ensembles compared to prior generative models and traditional cheminformatics methods.

本文介绍了CoarsenConf，它是一种基于分子图的粗粒化策略，可以用于高效地生成低能3D结构，并使用聚合注意机制恢复精细的坐标，从而实现大分子的自回归生成，此外，我们引入了新的指标以更好地评估生成构象体的质量和可行性，并证明CoarsenConf相比之前的生成模型和传统化学信息学方法可以生成更准确的构象体集合。

CoarsenConf: 分子构象生成的均变性聚合注意力粗化