图遍历
树遍历
计算机科学
图形
算法
深度优先搜索
理论计算机科学
生成模型
生成语法
人工智能
搜索算法
作者
Rocío Mercado,Esben Jannik Bjerrum,Ola Engkvist
标识
DOI:10.33774/chemrxiv-2021-5c5l1-v2
摘要
Here we explore the impact of different graph traversal algorithms on molecular graph generation. We do this by training a graph-based deep molecular generative model to build structures using a node order determined via either a breadth- or depth-first search algorithm. What we observe is that using a breadth-first traversal leads to better coverage of training data features compared to a depth-first traversal. We have quantified these differences using a variety of metrics on a dataset of natural products. These metrics include: percent validity, molecular coverage, and molecular shape. We also observe that using either a breadth- or depth-first traversal it is possible to over-train the generative models, at which point the results with the graph traversal algorithm are identical
科研通智能强力驱动
Strongly Powered by AbleSci AI