Molecular Representation Learning (MRL) has proven impactful in numerous
biochemical applications such as drug discovery and enzyme design. While Graph
Neural Networks (GNNs) are effective at learning molecular representations from
a 2D molecular graph or a single 3D structure, existing works often overlook
the flexible nature of molecules, which continuously interconvert across
conformations via chemical bond rotations and minor vibrational perturbations.
To better account for molecular flexibility, some recent works formulate MRL as
an ensemble learning problem, focusing on explicitly learning from a set of
conformer structures. However, most of these studies have limited datasets,
tasks, and models. In this work, we introduce the first MoleculAR Conformer
Ensemble Learning (MARCEL) benchmark to thoroughly evaluate the potential of
learning on conformer ensembles and suggest promising research directions.
MARCEL includes four datasets covering diverse molecule- and reaction-level
properties of chemically diverse molecules including organocatalysts and
transition-metal catalysts, extending beyond the scope of common GNN benchmarks
that are confined to drug-like molecules. In addition, we conduct a
comprehensive empirical study, which benchmarks representative 1D, 2D, and 3D
molecular representation learning models, along with two strategies that
explicitly incorporate conformer ensembles into 3D MRL models. Our findings
reveal that direct learning from an accessible conformer space can improve
performance on a variety of tasks and models.

分子表示学习具有在药物发现和酶设计等众多生物化学应用中的显著影响。我们提出了第一个全面评估从同构体集合学习的潜力的 MoleculAR Conformer Ensemble Learning (MARCEL) 基准，并建议有前景的研究方向。我们的研究结果表明，直接从可访问的同构体空间学习可以提高各种任务和模型的性能。

分子构象集的学习：数据集与基准

Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks

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

CoarsenConf: Equivariant Coarsening with Aggregated Attention for  Molecular Conformer Generation

Machine learning (ML) outperforms traditional approaches in many molecular
design tasks. ML models usually predict molecular properties from a 2D chemical
graph or a single 3D structure, but neither of these representations accounts
for the ensemble of 3D conformers that are accessible to a molecule. Property
prediction could be improved by using conformer ensembles as input, but there
is no large-scale dataset that contains graphs annotated with accurate
conformers and experimental data. Here we use advanced sampling and
semi-empirical density functional theory (DFT) to generate 37 million molecular
conformations for over 450,000 molecules. The Geometric Ensemble Of Molecules
(GEOM) dataset contains conformers for 133,000 species from QM9, and 317,000
species with experimental data related to biophysics, physiology, and physical
chemistry. Ensembles of 1,511 species with BACE-1 inhibition data are also
labeled with high-quality DFT free energies in an implicit water solvent, and
534 ensembles are further optimized with DFT. GEOM will assist in the
development of models that predict properties from conformer ensembles, and
generative models that sample 3D conformations.

使用密度泛函理论和高级采样方法生成超过 450,000 种分子的 3D 构象，这将有助于从构象集合中预测分子性质并进行生成模型的开发。