论文标题
半等级的连续归一化流量，用于目标感知分子的产生
Semi-Equivariant Continuous Normalizing Flows for Target-Aware Molecule Generation
论文作者
论文摘要
我们提出了一种用于学习分子的条件生成模型的算法。具体而言，给定一个希望与之结合的受体分子，条件模型会产生可能与之结合的候选配体分子。分布应该是对配体和受体上$ \ textit {interly} $的刚体变换的不变性；它也应该是配体原子或受体原子排列的不变。我们的学习算法基于连续的归一流流。我们在流量上建立了半均衡条件，以确保有条件分布的上述不变条件。我们提出了一种实现这一流程的图形神经网络体系结构，尽管配体和受体之间的大小存在很大差异，旨在有效地学习。我们在CrossDocked2020数据集上评估了我们的方法，从而在与竞争方法的结合亲和力方面取得了重大改善。
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.
