GeoMol：分子三维构象系综的扭转几何生成
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
摘要：
从分子图预测分子的三维构象系综在化学信息学和药物发现领域中具有关键作用。现有的生成模型具有若干缺点，包括缺乏对重要的分子几何元素（例如扭转角）的建模、易于误差累积的单独的优化阶段、以及需要基于近似经典力场或计算昂贵的方法（诸如在每个几何结构处具有近似量子力学计算的元自洽法）进行结构微调。我们提出了GeoMol-一种端到端的，非自回归和SE（3）不变的机器学习方法来生成低能分子3D构象的分布。利用消息传递神经网络（MPNN）的能力来捕获局部和全局图形信息，我们预测局部原子3D结构和扭转角，避免几何自由度的不必要的过度参数化（例如，每个非末端键一个角度）。这样的局部预测对于训练损失计算以及对于完整的确定性构象组装（在测试时）都是足够的。我们设计了一个非对抗性的最佳运输的损失函数，以促进不同的构象生成。GeoMol的性能明显优于流行的开源、商业或最先进的机器学习（ML）模型，同时实现了显著的速度提升。我们期望这种可微的3D结构生成器能够显著影响分子建模和相关应用。
英文摘要：
Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements (e.g. torsion angles), separate optimization stages prone to error accumulation, and the need for structure fine-tuning based on approximate classical force-fields or computationally expensive methods such as metadynamics with approximate quantum mechanics calculations at each geometry. We propose GeoMol--an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate distributions of low-energy molecular 3D conformers. Leveraging the power of message passing neural networks (MPNNs) to capture local and global graph information, we predict local atomic 3D structures and torsion angles, avoiding unnecessary over-parameterization of the geometric degrees of freedom (e.g. one angle per non-terminal bond). Such local predictions suffice both for the training loss computation, as well as for the full deterministic conformer assembly (at test time). We devise a non-adversarial optimal transport based loss function to promote diverse conformer generation. GeoMol predominantly outperforms popular open-source, commercial, or state-of-the-art machine learning (ML) models, while achieving significant speed-ups. We expect such differentiable 3D structure generators to significantly impact molecular modeling and related applications.
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