论文标题
半监督GCN用于学习分子结构活性关系
Semi-Supervised GCN for learning Molecular Structure-Activity Relationships
论文作者
论文摘要
自从引入药物化学中的人工智能以来，已经出现了分析分子性质变化如何通过单个原子或化学基团调节分子性质变化的必要性。在本文中，我们建议使用半监督的学习来训练图形神经网络，以归因结构 - 托管关系。作为初步案例研究，我们将方法应用于溶解度和分子酸度，同时检查其与已知的实验化学数据相比的一致性。作为最终目标，我们的方法可以代表一种有价值的工具来处理诸如活动悬崖，铅优化和北野药物设计等问题。
Since the introduction of artificial intelligence in medicinal chemistry, the necessity has emerged to analyse how molecular property variation is modulated by either single atoms or chemical groups. In this paper, we propose to train graph-to-graph neural network using semi-supervised learning for attributing structure-property relationships. As initial case studies we apply the method to solubility and molecular acidity while checking its consistency in comparison with known experimental chemical data. As final goal, our approach could represent a valuable tool to deal with problems such as activity cliffs, lead optimization and de-novo drug design.
