We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning.
- Pub Date:
- July 2020
- Computer Science - Machine Learning;
- Statistics - Machine Learning
- 23 pages, 10 tables, 13 figures, to appear in the ICML 2020 Workshop on Graph Representation Learning and Beyond (GRLB)