论文标题
通过四面体手性的分子的消息传递网络
Message Passing Networks for Molecules with Tetrahedral Chirality
论文作者
论文摘要
具有相同图形连通性的分子，如果它们表现出立体化学，则可以表现出不同的物理和生物学特性 - 空间结构特征。然而，旨在从分子结构学习结构 - 特质关系的现代神经体系结构将分子视为图形结构数据，因此对立体化学是不变的。在这里，我们开发了两个自定义聚合函数，以传递神经网络，以学习具有四面体手性的分子的特性，这是立体化学的一种常见形式。我们评估合成数据的性能以及与药物发现有关的新蛋白质配体数据集。结果表明，对基线总和聚合器的改进适度，突出了进一步建筑开发的机会。
Molecules with identical graph connectivity can exhibit different physical and biological properties if they exhibit stereochemistry-a spatial structural characteristic. However, modern neural architectures designed for learning structure-property relationships from molecular structures treat molecules as graph-structured data and therefore are invariant to stereochemistry. Here, we develop two custom aggregation functions for message passing neural networks to learn properties of molecules with tetrahedral chirality, one common form of stereochemistry. We evaluate performance on synthetic data as well as a newly-proposed protein-ligand docking dataset with relevance to drug discovery. Results show modest improvements over a baseline sum aggregator, highlighting opportunities for further architecture development.
