# Modelling Chemical Reasoning to Predict Reactions

arXiv: https://arxiv.org/abs/1608.07117 · HTML: https://mlsanity.com/paper/1608.07117/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2016.

## Built on by (most cited first, 8 of 8)

- [RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction](https://mlsanity.com/paper/2112.11225/) (2021)
- [SemiRetro: Semi-template framework boosts deep retrosynthesis prediction](https://mlsanity.com/paper/2202.08205/) (2022)
- [Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction](https://mlsanity.com/paper/2306.06119/) (2023)
- [Challenging reaction prediction models to generalize to novel chemistry](https://mlsanity.com/paper/2501.06669/) (2025)
- [Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities](https://mlsanity.com/paper/2502.17456/) (2025)
- [Deep Inverse Reinforcement Learning for Structural Evolution of Small Molecules](https://mlsanity.com/paper/2008.11804/) (2020)
- [ReactionTeam: Teaming Experts for Divergent Thinking Beyond Typical Reaction Patterns](https://mlsanity.com/paper/2310.04674/) (2023)
- [A Community-driven vision for a new Knowledge Resource for AI](https://mlsanity.com/paper/2506.16596/) (2025)

## Abstract (arXiv)

The ability to reason beyond established knowledge allows Organic Chemists to solve synthetic problems and to invent novel transformations. Here, we propose a model which mimics chemical reasoning and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180,000 randomly selected binary reactions. We show that our data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-) discovering novel transformations (even including transition-metal catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph, and because each single reaction prediction is typically achieved in a sub-second time frame, our model can be used as a high-throughput generator of reaction hypotheses for reaction discovery.