Identifying low-energy conformers with quantum mechanical accuracy for molecules with many degrees of freedom is challenging. In this work, we use the molecular dihedral angles as features and explore the possibility of performing molecular conformer search in a latent space with a generative model named variational auto-encoder (VAE). We bias the VAE towards low-energy molecular configurations to generate more informative data. In this way, we can effectively build a reliable energy model for the low-energy potential energy surface. After the energy model has been built, we extract local-minimum conformations and refine them with structure optimization. We have tested and benchmarked our low-energy latent-space (LOLS) structure search method on organic molecules with 5–9 searching dimensions. Our results agree with previous studies.

中文翻译：

---

低能量潜在空间的分子构象搜索


对于具有多个自由度的分子，以量子力学精度识别低能构象异构体具有挑战性。在这项工作中，我们使用分子二面角作为特征，并探索使用称为变分自动编码器（VAE）的生成模型在潜在空间中执行分子构象异构体搜索的可能性。我们将 VAE 偏向于低能分子构型，以生成更多信息数据。这样，我们就可以有效地建立可靠的低能势能面能量模型。建立能量模型后，我们提取局部最小构象并通过结构优化对其进行细化。我们已经对具有 5-9 个搜索维度的有机分子测试了我们的低能潜在空间 (LOLS) 结构搜索方法并对其进行了基准测试。我们的结果与之前的研究一致。