# Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions

arXiv: https://arxiv.org/abs/2007.04275 · HTML: https://mlsanity.com/paper/2007.04275/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [Predictive Chemistry Augmented with Text Retrieval](https://mlsanity.com/paper/2312.04881/) (2023)

## Abstract (arXiv)

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.