We propose an algorithm for learning a conditional generative model of a molecule given a target. Specifically, given a receptor molecule that one wishes to bind to, the conditional model generates candidate ligand molecules that may bind to it. The distribution should be invariant to rigid body transformations that act $\textit{jointly}$ on the ligand and the receptor; it should also be invariant to permutations of either the ligand or receptor atoms. Our learning algorithm is based on a continuous normalizing flow. We establish semi-equivariance conditions on the flow which guarantee the aforementioned invariance conditions on the conditional distribution. We propose a graph neural network architecture which implements this flow, and which is designed to learn effectively despite the vast differences in size between the ligand and receptor. We evaluate our method on the CrossDocked2020 dataset, attaining a significant improvement in binding affinity over competing methods.

翻译：我们建议一种算法, 用于学习某个分子的有条件基因模型。 具体地说, 假设一个受体分子, 该受体分子希望与之捆绑, 该受体分子会产生候选的离子分子和可能与之绑在一起的分子。 该分布法应该对硬体变形不起作用, 在离子体和受体上作用 $\ textit{ 共同 } 和受体之间作用; 它也应该不易改变离子体或受体原子的变形。 我们的学习算法是以连续的正常流为基础的。 我们对流动设置了半等值条件, 保证了上述在有条件分布上的变异性条件。 我们提出了一个用于控制这种流动的图形神经网络结构, 并且设计它来有效学习, 尽管离子体和受体之间在大小上存在巨大的差异。 我们评价了我们关于CrossDocked20数据集的方法, 从而在对相竞方法的结合上取得了显著的改进。