# ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction

arXiv: https://arxiv.org/abs/2310.13590 · HTML: https://mlsanity.com/paper/2310.13590/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023)
- [ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction](https://mlsanity.com/paper/2010.09885/) (2020)
- [An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA](https://mlsanity.com/paper/2109.05014/) (2021)
- [Topology Adaptive Graph Convolutional Networks](https://mlsanity.com/paper/1710.10370/) (2017)
- [Retrosynthesis Prediction with Conditional Graph Logic Network](https://mlsanity.com/paper/2001.01408/) (2020)
- [Molecular representation learning with language models and domain-relevant auxiliary tasks](https://mlsanity.com/paper/2011.13230/) (2020)
- [Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits](https://mlsanity.com/paper/2006.15426/) (2020)
- [Learning Graph Models for Retrosynthesis Prediction](https://mlsanity.com/paper/2006.07038/) (2020)
- [Chemical-Reaction-Aware Molecule Representation Learning](https://mlsanity.com/paper/2109.09888/) (2021)

## Built on by (most cited first, 24 of 24)

- [Large Language Models on Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2312.02783/) (2023)
- [A Survey of Graph Meets Large Language Model: Progress and Future Directions](https://mlsanity.com/paper/2311.12399/) (2023)
- [Towards 3D Molecule-Text Interpretation in Language Models](https://mlsanity.com/paper/2401.13923/) (2024)
- [Graph Machine Learning in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2404.14928/) (2024)
- [ProtT3: Protein-to-Text Generation for Text-based Protein Understanding](https://mlsanity.com/paper/2405.12564/) (2024)
- [MolTC: Towards Molecular Relational Modeling In Language Models](https://mlsanity.com/paper/2402.03781/) (2024)
- [EXGC: Bridging Efficiency and Explainability in Graph Condensation](https://mlsanity.com/paper/2402.05962/) (2024)
- [Benchmarking Retrieval-Augmented Generation for Chemistry](https://mlsanity.com/paper/2505.07671/) (2025)
- [ReactXT: Understanding Molecular "Reaction-ship" via Reaction-Contextualized Molecule-Text Pretraining](https://mlsanity.com/paper/2405.14225/) (2024)
- [Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey](https://mlsanity.com/paper/2403.01528/) (2024)
- [NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation](https://mlsanity.com/paper/2502.12638/) (2025)
- [Contextualized Sequence Likelihood: Enhanced Confidence Scores for Natural Language Generation](https://mlsanity.com/paper/2406.01806/) (2024)
- [ChemDFM-X: Towards Large Multimodal Model for Chemistry](https://mlsanity.com/paper/2409.13194/) (2024)
- [GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaboration](https://mlsanity.com/paper/2410.18032/) (2024)
- [Regression with Large Language Models for Materials and Molecular Property Prediction](https://mlsanity.com/paper/2409.06080/) (2024)
- [Advancing Molecular Graph-Text Pre-training via Fine-grained Alignment](https://mlsanity.com/paper/2409.14106/) (2024)
- [Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applications](https://mlsanity.com/paper/2510.21131/) (2025)
- [Bridging Text and Molecule: A Survey on Multimodal Frameworks for Molecule](https://mlsanity.com/paper/2403.13830/) (2024)
- [Quo Vadis ChatGPT? From Large Language Models to Large Knowledge Models](https://mlsanity.com/paper/2405.19561/) (2024)
- [Reforge: Low-Latency Distributed GNN Serving with Selective Embedding Recomputation](https://mlsanity.com/paper/2501.08547/) (2025)
- [ChemAmp: Amplified Chemistry Tools via Composable Agents](https://mlsanity.com/paper/2505.21569/) (2025)
- [SpecMol: A Spectroscopy-Grounded Foundation Model for Multi-Task Molecular Learning](https://mlsanity.com/paper/2509.21861/) (2025)
- [NOCL: Node-Oriented Conceptualization LLM for Graph Tasks without Message Passing](https://mlsanity.com/paper/2506.10014/) (2025)
- [Rethinking Message Passing as Retrieval for Text-Attributed Graph Learning](https://mlsanity.com/paper/2608.26732/) (2026)

## Abstract (arXiv)

Predicting chemical reactions, a fundamental challenge in chemistry, involves forecasting the resulting products from a given reaction process. Conventional techniques, notably those employing Graph Neural Networks (GNNs), are often limited by insufficient training data and their inability to utilize textual information, undermining their applicability in real-world applications. In this work, we propose ReLM, a novel framework that leverages the chemical knowledge encoded in language models (LMs) to assist GNNs, thereby enhancing the accuracy of real-world chemical reaction predictions. To further enhance the model's robustness and interpretability, we incorporate the confidence score strategy, enabling the LMs to self-assess the reliability of their predictions. Our experimental results demonstrate that ReLM improves the performance of state-of-the-art GNN-based methods across various chemical reaction datasets, especially in out-of-distribution settings. Codes are available at https://github.com/syr-cn/ReLM.