论文标题
从图形卷积网络上从输入属性产生分子
Molecule Generation from Input-Attributions over Graph Convolutional Networks
论文作者
论文摘要
众所周知，在时间和经济努力方面，药物设计通常是一个昂贵的过程。虽然良好的定量结构 - 活性关系模型（QSAR）可以帮助预测分子特性而无需合成它们，但仍需要提出要测试的新分子。这主要是由于缺乏工具来确定哪些修饰更有前途，或者分子的哪些方面对最终活动/属性更具影响力。在这里，我们提出了一个自动过程，该过程涉及图形卷积网络模型和输入 - 贡献方法，以生成新分子。我们还探讨了过度优化和适用性的问题，将它们视为实际使用此类自动工具的两个重要方面。
It is well known that Drug Design is often a costly process both in terms of time and economic effort. While good Quantitative Structure-Activity Relationship models (QSAR) can help predicting molecular properties without the need to synthesize them, it is still required to come up with new molecules to be tested. This is mostly done in lack of tools to determine which modifications are more promising or which aspects of a molecule are more influential for the final activity/property. Here we present an automatic process which involves Graph Convolutional Network models and input-attribution methods to generate new molecules. We also explore the problems of over-optimization and applicability, recognizing them as two important aspects in the practical use of such automatic tools.
