论文标题
子结构 - 原子互相注意分子表示学习
Substructure-Atom Cross Attention for Molecular Representation Learning
论文作者
论文摘要
设计用于分子表示的神经网络结构对于AI驱动的药物发现和分子设计至关重要。在这项工作中，我们提出了一个用于分子表示学习的新框架。我们的贡献是三重的：（a）证明将子结构纳入分子的节点特征的有用性，（b）设计由变压器和图形神经网络组成的两个分支网络，以使网络与不对称的关注融合，并且（c）不需要启发式特征和来自分子的计算表现量的信息。使用从Chembl和PubChem数据库中收集的180万个分子，我们将网络预先介绍，以最少的监督学习分子的一般表示。实验结果表明，我们审计的网络在11个下游任务上实现了分子性能预测的竞争性能。
Designing a neural network architecture for molecular representation is crucial for AI-driven drug discovery and molecule design. In this work, we propose a new framework for molecular representation learning. Our contribution is threefold: (a) demonstrating the usefulness of incorporating substructures to node-wise features from molecules, (b) designing two branch networks consisting of a transformer and a graph neural network so that the networks fused with asymmetric attention, and (c) not requiring heuristic features and computationally-expensive information from molecules. Using 1.8 million molecules collected from ChEMBL and PubChem database, we pretrain our network to learn a general representation of molecules with minimal supervision. The experimental results show that our pretrained network achieves competitive performance on 11 downstream tasks for molecular property prediction.
