# MechRetro is a chemical-mechanism-driven graph learning framework for interpretable retrosynthesis prediction and pathway planning

arXiv: https://arxiv.org/abs/2210.02630 · HTML: https://mlsanity.com/paper/2210.02630/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Learning to Plan Chemical Syntheses](https://mlsanity.com/paper/1708.04202/) (2017)
- [Retrosynthetic reaction prediction using neural sequence-to-sequence models](https://mlsanity.com/paper/1706.01643/) (2017)
- [Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction](https://mlsanity.com/paper/2110.09681/) (2021)
- [Retroformer: Pushing the Limits of Interpretable End-to-end Retrosynthesis Transformer](https://mlsanity.com/paper/2201.12475/) (2022)

## Built on by (most cited first, 9 of 9)

- [Re-evaluating Retrosynthesis Algorithms with Syntheseus](https://mlsanity.com/paper/2310.19796/) (2023)
- [AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Search](https://mlsanity.com/paper/2509.20988/) (2025)
- [DirectMultiStep: Direct Route Generation for Multistep Retrosynthesis](https://mlsanity.com/paper/2405.13983/) (2024)
- [Enhancing Chemical Reaction and Retrosynthesis Prediction with Large Language Model and Dual-task Learning](https://mlsanity.com/paper/2505.02639/) (2025)
- [Chemist-aligned retrosynthesis by ensembling diverse inductive bias models](https://mlsanity.com/paper/2412.05269/) (2024)
- [RLSynC: Offline-Online Reinforcement Learning for Synthon Completion](https://mlsanity.com/paper/2309.02671/) (2023)
- [Retrosynthesis Planning via Worst-path Policy Optimisation in Tree-structured MDPs](https://mlsanity.com/paper/2509.10504/) (2025)
- [RxnNano:Training Compact LLMs for Chemical Reaction and Retrosynthesis Prediction via Hierarchical Curriculum Learning](https://mlsanity.com/paper/2603.02215/) (2026)
- [ConRetroBert: EMA Stabilized Dual Encoders for Template-Based Single-Step Retrosynthesis](https://mlsanity.com/paper/2605.12736/) (2026)

## Abstract (arXiv)

Leveraging artificial intelligence for automatic retrosynthesis speeds up organic pathway planning in digital laboratories. However, existing deep learning approaches are unexplainable, like "black box" with few insights, notably limiting their applications in real retrosynthesis scenarios. Here, we propose MechRetro, a chemical-mechanism-driven graph learning framework for interpretable retrosynthetic prediction and pathway planning, which learns several retrosynthetic actions to simulate a reverse reaction via elaborate self-adaptive joint learning. By integrating chemical knowledge as prior information, we design a novel Graph Transformer architecture to adaptively learn discriminative and chemically meaningful molecule representations, highlighting the strong capacity in molecule feature representation learning. We demonstrate that MechRetro outperforms the state-of-the-art approaches for retrosynthetic prediction with a large margin on large-scale benchmark datasets. Extending MechRetro to the multi-step retrosynthesis analysis, we identify efficient synthetic routes via an interpretable reasoning mechanism, leading to a better understanding in the realm of knowledgeable synthetic chemists. We also showcase that MechRetro discovers a novel pathway for protokylol, along with energy scores for uncertainty assessment, broadening the applicability for practical scenarios. Overall, we expect MechRetro to provide meaningful insights for high-throughput automated organic synthesis in drug discovery.