论文标题
基于3D形相似性的分子表示学习方法
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning
论文作者
论文摘要
分子形状和几何形状决定了关键的生物物理识别过程，但是许多图形神经网络无视3D信息以用于分子性质预测。在这里，我们提出了用于图神经网络的新的对比学习程序，分子对比度从形状相似性（Molclass）中隐含地学习了三维表示。 Molclass不是直接编码或靶向三维姿势，而是匹配基于高斯叠加的相似性目标，以学习有意义的分子形状表示。我们演示了该框架自然如何捕获二维表示无法并为脚手架跳跃提供的感应框架的三维的关键方面。
Molecular shape and geometry dictate key biophysical recognition processes, yet many graph neural networks disregard 3D information for molecular property prediction. Here, we propose a new contrastive-learning procedure for graph neural networks, Molecular Contrastive Learning from Shape Similarity (MolCLaSS), that implicitly learns a three-dimensional representation. Rather than directly encoding or targeting three-dimensional poses, MolCLaSS matches a similarity objective based on Gaussian overlays to learn a meaningful representation of molecular shape. We demonstrate how this framework naturally captures key aspects of three-dimensionality that two-dimensional representations cannot and provides an inductive framework for scaffold hopping.
