# Graph Transformation Policy Network for Chemical Reaction Prediction

arXiv: https://arxiv.org/abs/1812.09441 · HTML: https://mlsanity.com/paper/1812.09441/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2018.

## Built on by (most cited first, 30 of 56)

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- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
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- [BARTSmiles: Generative Masked Language Models for Molecular Representations](https://mlsanity.com/paper/2211.16349/) (2022)
- [Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction](https://mlsanity.com/paper/2106.07801/) (2021)
- [Information Gain Propagation: a new way to Graph Active Learning with Soft Labels](https://mlsanity.com/paper/2203.01093/) (2022)
- [On Structural Explanation of Bias in Graph Neural Networks](https://mlsanity.com/paper/2206.12104/) (2022)
- [Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges](https://mlsanity.com/paper/2212.05532/) (2022)
- [Electron flow matching for generative reaction mechanism prediction obeying conservation laws](https://mlsanity.com/paper/2502.12979/) (2025)
- [A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning](https://mlsanity.com/paper/2210.05448/) (2022)
- [FairGAT: Fairness-aware Graph Attention Networks](https://mlsanity.com/paper/2303.14591/) (2023)
- [Graph Neural Networks in Modern AI-aided Drug Discovery](https://mlsanity.com/paper/2506.06915/) (2025)
- [Deep Learning Methods for Small Molecule Drug Discovery: A Survey](https://mlsanity.com/paper/2303.00313/) (2023)
- [Topological Regularization for Graph Neural Networks Augmentation](https://mlsanity.com/paper/2104.02478/) (2021)
- [Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification](https://mlsanity.com/paper/2104.13718/) (2021)

## Abstract (arXiv)

We address a fundamental problem in chemistry known as chemical reaction product prediction. Our main insight is that the input reactant and reagent molecules can be jointly represented as a graph, and the process of generating product molecules from reactant molecules can be formulated as a sequence of graph transformations. To this end, we propose Graph Transformation Policy Network (GTPN) -- a novel generic method that combines the strengths of graph neural networks and reinforcement learning to learn the reactions directly from data with minimal chemical knowledge. Compared to previous methods, GTPN has some appealing properties such as: end-to-end learning, and making no assumption about the length or the order of graph transformations. In order to guide model search through the complex discrete space of sets of bond changes effectively, we extend the standard policy gradient loss by adding useful constraints. Evaluation results show that GTPN improves the top-1 accuracy over the current state-of-the-art method by about 3% on the large USPTO dataset. Our model's performances and prediction errors are also analyzed carefully in the paper.