论文标题
具有低能潜在空间的分子构象异构体搜索
Molecular conformer search with low-energy latent space
论文作者
论文摘要
确定具有许多自由度的分子的量子机械精度的低能构象体具有挑战性。在这项工作中，我们使用分子二面角作为特征，并探索在潜在空间中进行分子构象异构体搜索的可能性，其生成模型名为变异自动编码器（VAE）。我们将VAE偏向低能量的分子构型，以生成更有信息的数据。这样，我们可以有效地为低能势能表面构建可靠的能源模型。构建能量模型后，我们提取局部最低构象并通过结构优化来完善它们。我们已经测试和基准测试了有机分子的低能潜伏空间（LOLS）结构搜索方法，并以$ 5-9 $搜索的尺寸进行了测试。我们的结果与以前的研究一致。
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.
