论文标题
3DLINKER：E（3）分子接头设计的差异自动编码器
3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
论文作者
论文摘要
深度学习在设计具有理想药物特性的新型化学化合物方面取得了巨大的成功。在这项工作中，我们专注于一种新型的药物设计问题 - 生成一个小的“接头”，以物理上附着两个独立的分子及其独特的功能。主要的计算挑战包括：1）连接器的产生是在两个给定分子上的条件，与以前的作品中从头开始产生完整分子相比； 2）链接器在很大程度上取决于要连接的两个分子的锚定原子，这是未知的； 3）需要考虑三个分子的3D结构和方向，以避免原子冲突，对于e（3）组，必须对其进行均衡。为了解决这些问题，我们提出了一个有条件的生成模型，称为3DLINKER，该模型能够预测基于E（3）Equivariant图形变异自动编码器的锚定原子并共同生成链接图及其3D结构。据我们所知，没有以前的模型可以实现这一任务。我们将模型与从其他分子设计任务修改的多种条件生成模型进行比较，发现我们的模型在恢复分子图方面具有明显更高的速率，更重要的是，准确地预测了所有原子的3D坐标。
Deep learning has achieved tremendous success in designing novel chemical compounds with desirable pharmaceutical properties. In this work, we focus on a new type of drug design problem -- generating a small "linker" to physically attach two independent molecules with their distinct functions. The main computational challenges include: 1) the generation of linkers is conditional on the two given molecules, in contrast to generating full molecules from scratch in previous works; 2) linkers heavily depend on the anchor atoms of the two molecules to be connected, which are not known beforehand; 3) 3D structures and orientations of the molecules need to be considered to avoid atom clashes, for which equivariance to E(3) group are necessary. To address these problems, we propose a conditional generative model, named 3DLinker, which is able to predict anchor atoms and jointly generate linker graphs and their 3D structures based on an E(3) equivariant graph variational autoencoder. So far as we know, there are no previous models that could achieve this task. We compare our model with multiple conditional generative models modified from other molecular design tasks and find that our model has a significantly higher rate in recovering molecular graphs, and more importantly, accurately predicting the 3D coordinates of all the atoms.
