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.

翻译：在设计具有理想制药特性的新化学化合物方面,深层学习取得了巨大成功。在这项工作中,我们侧重于一种新的药物设计问题 -- -- 产生一个小型的“熟客”,以实际附加两个独立分子,并具有其独特的功能。主要的计算挑战包括:(1) 产生连接器的条件是两个给定分子,而不是以前作品中从零产生完整的分子;(2) 连接器严重依赖两个分子连接的锚原子,而这些分子事先并不知道;(3) 需要考虑分子的3D结构和方向,以避免原子冲突,为此需要将模型与从其他分子设计任务到E(3)组等同起来。为了解决这些问题,我们提出了一个称为 3DLinker的有条件的基因化模型,它能够预测原子的锚定,并联合生成连接器图及其基于E(3) 等离子图形自动电离层结构的3D结构。据我们所知,以前没有能够实现这项任务的模型。我们将我们的模型与其他从其他分子设计任务到E(3)类设计任务的多个有条件的基因化模型结构和方向加以比较。我们建议了一个有条件的基因化模型的模型,在模型中有一个更精确的精确的坐标,在模型中可以追溯地恢复。