# FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning

arXiv: https://arxiv.org/abs/2209.15315 · HTML: https://mlsanity.com/paper/2209.15315/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 50 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [MetaICL: Learning to Learn In Context](https://mlsanity.com/paper/2110.15943/) (2021)
- [Memory Networks](https://mlsanity.com/paper/1410.3916/) (2014)
- [GNNGuard: Defending Graph Neural Networks against Adversarial Attacks](https://mlsanity.com/paper/2006.08149/) (2020)
- [Learning to Plan Chemical Syntheses](https://mlsanity.com/paper/1708.04202/) (2017)
- [Retrosynthesis Prediction with Conditional Graph Logic Network](https://mlsanity.com/paper/2001.01408/) (2020)
- [Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits](https://mlsanity.com/paper/2006.15426/) (2020)
- [Retrosynthetic reaction prediction using neural sequence-to-sequence models](https://mlsanity.com/paper/1706.01643/) (2017)
- [A Graph to Graphs Framework for Retrosynthesis Prediction](https://mlsanity.com/paper/2003.12725/) (2020)
- [Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search](https://mlsanity.com/paper/2006.15820/) (2020)
- [RetroXpert: Decompose Retrosynthesis Prediction like a Chemist](https://mlsanity.com/paper/2011.02893/) (2020)
- [Learning Graph Models for Retrosynthesis Prediction](https://mlsanity.com/paper/2006.07038/) (2020)
- [Energy-based View of Retrosynthesis](https://mlsanity.com/paper/2007.13437/) (2020)
- [RetroGraph: Retrosynthetic Planning with Graph Search](https://mlsanity.com/paper/2206.11477/) (2022)
- [Self-Improved Retrosynthetic Planning](https://mlsanity.com/paper/2106.04880/) (2021)
- [Learning to Make Generalizable and Diverse Predictions for Retrosynthesis](https://mlsanity.com/paper/1910.09688/) (2019)
- [Retrosynthetic Planning with Experience-Guided Monte Carlo Tree Search](https://mlsanity.com/paper/2112.06028/) (2021)
- [A generative model for molecule generation based on chemical reaction trees](https://mlsanity.com/paper/2106.03394/) (2021)

## Built on by (most cited first, 14 of 14)

- [Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search](https://mlsanity.com/paper/2407.06334/) (2024)
- [DirectMultiStep: Direct Route Generation for Multistep Retrosynthesis](https://mlsanity.com/paper/2405.13983/) (2024)
- [Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel](https://mlsanity.com/paper/2311.01276/) (2023)
- [Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models](https://mlsanity.com/paper/2405.17656/) (2024)
- [Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models](https://mlsanity.com/paper/2406.02066/) (2024)
- [Evaluating Molecule Synthesizability via Retrosynthetic Planning and Reaction Prediction](https://mlsanity.com/paper/2411.08306/) (2024)
- [A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges](https://mlsanity.com/paper/2306.15890/) (2023)
- [Retro-BLEU: Quantifying Chemical Plausibility of Retrosynthesis Routes through Reaction Template Sequence Analysis](https://mlsanity.com/paper/2311.06304/) (2023)
- [Procrustean Bed for AI-Driven Retrosynthesis: A Unified Framework for Reproducible Evaluation](https://mlsanity.com/paper/2512.07079/) (2025)
- [Globally Interpretable Graph Learning via Distribution Matching](https://mlsanity.com/paper/2306.10447/) (2023)
- [BioMedGPT-Mol: Multi-task Learning for Molecular Understanding and Generation](https://mlsanity.com/paper/2512.04629/) (2025)
- [Open-Source Molecular Processing Pipeline for Generating Molecules](https://mlsanity.com/paper/2408.06261/) (2024)
- [FragmentRetro: A Quadratic Retrosynthetic Method Based on Fragmentation Algorithms](https://mlsanity.com/paper/2509.15409/) (2025)
- [Reinforced Reasoning for End-to-End Retrosynthetic Planning](https://mlsanity.com/paper/2603.29723/) (2026)

## Abstract (arXiv)

Retrosynthetic planning aims to devise a complete multi-step synthetic route from starting materials to a target molecule. Current strategies use a decoupled approach of single-step retrosynthesis models and search algorithms, taking only the product as the input to predict the reactants for each planning step and ignoring valuable context information along the synthetic route. In this work, we propose a novel framework that utilizes context information for improved retrosynthetic planning. We view synthetic routes as reaction graphs and propose to incorporate context through three principled steps: encode molecules into embeddings, aggregate information over routes, and readout to predict reactants. Our approach is the first attempt to utilize in-context learning for retrosynthesis prediction in retrosynthetic planning. The entire framework can be efficiently optimized in an end-to-end fashion and produce more practical and accurate predictions. Comprehensive experiments demonstrate that by fusing in the context information over routes, our model significantly improves the performance of retrosynthetic planning over baselines that are not context-aware, especially for long synthetic routes. Code is available at https://github.com/SongtaoLiu0823/FusionRetro.