# ReactXT: Understanding Molecular "Reaction-ship" via Reaction-Contextualized Molecule-Text Pretraining

arXiv: https://arxiv.org/abs/2405.14225 · HTML: https://mlsanity.com/paper/2405.14225/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 60 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 22 of 22)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (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)
- [The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"](https://mlsanity.com/paper/2309.12288/) (2023)
- [ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction](https://mlsanity.com/paper/2010.09885/) (2020)
- [Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing](https://mlsanity.com/paper/2212.10789/) (2022)
- [A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language](https://mlsanity.com/paper/2209.05481/) (2022)
- [MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter](https://mlsanity.com/paper/2310.12798/) (2023)
- [Retrosynthesis Prediction with Conditional Graph Logic Network](https://mlsanity.com/paper/2001.01408/) (2020)
- [Unifying Molecular and Textual Representations via Multi-task Language Modelling](https://mlsanity.com/paper/2301.12586/) (2023)
- [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)
- [RetroXpert: Decompose Retrosynthesis Prediction like a Chemist](https://mlsanity.com/paper/2011.02893/) (2020)
- [Towards 3D Molecule-Text Interpretation in Language Models](https://mlsanity.com/paper/2401.13923/) (2024)
- [Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction](https://mlsanity.com/paper/2203.11444/) (2022)
- [Regression Transformer: Concurrent sequence regression and generation for molecular language modeling](https://mlsanity.com/paper/2202.01338/) (2022)
- [Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules](https://mlsanity.com/paper/2310.14753/) (2023)
- [ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction](https://mlsanity.com/paper/2310.13590/) (2023)
- [MolTC: Towards Molecular Relational Modeling In Language Models](https://mlsanity.com/paper/2402.03781/) (2024)

## Built on by (most cited first, 19 of 19)

- [NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation](https://mlsanity.com/paper/2502.12638/) (2025)
- [PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes](https://mlsanity.com/paper/2406.13193/) (2024)
- [From Generalist to Specialist: A Survey of Large Language Models for Chemistry](https://mlsanity.com/paper/2412.19994/) (2024)
- [mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Molecules](https://mlsanity.com/paper/2505.12565/) (2025)
- [Language-Enhanced Representation Learning for Single-Cell Transcriptomics](https://mlsanity.com/paper/2503.09427/) (2025)
- [Text-guided Diffusion Model for 3D Molecule Generation](https://mlsanity.com/paper/2410.03803/) (2024)
- [AgentDrug: Utilizing Large Language Models in An Agentic Workflow for Zero-Shot Molecular Editing](https://mlsanity.com/paper/2410.13147/) (2024)
- [Omni-Mol: Multitask Molecular Model for Any-to-any Modalities](https://mlsanity.com/paper/2502.01074/) (2025)
- [ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data](https://mlsanity.com/paper/2506.23520/) (2025)
- [Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesis](https://mlsanity.com/paper/2508.10967/) (2025)
- [Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs](https://mlsanity.com/paper/2510.01527/) (2025)
- [PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs](https://mlsanity.com/paper/2507.05101/) (2025)
- [EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme Backbone Generation](https://mlsanity.com/paper/2510.25132/) (2025)
- [3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding](https://mlsanity.com/paper/2510.16780/) (2025)
- [Learning 3D Anisotropic Noise Distributions Improves Molecular Force Field Modeling](https://mlsanity.com/paper/2510.22123/) (2025)
- [A Scientific Reasoning Model for Organic Synthesis Procedure Generation](https://mlsanity.com/paper/2512.13668/) (2025)
- [LabBuilder: Protocol-Grounded 3D Layout Generation for Interactable and Safe Laboratory](https://mlsanity.com/paper/2605.02288/) (2026)
- [Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale](https://mlsanity.com/paper/2605.07022/) (2026)
- [SafeMol: Dual-Modality Safety Alignment for Molecular Multimodal Models](https://mlsanity.com/paper/2609.33640/) (2026)

## Abstract (arXiv)

Molecule-text modeling, which aims to facilitate molecule-relevant tasks with a textual interface and textual knowledge, is an emerging research direction. Beyond single molecules, studying reaction-text modeling holds promise for helping the synthesis of new materials and drugs. However, previous works mostly neglect reaction-text modeling: they primarily focus on modeling individual molecule-text pairs or learning chemical reactions without texts in context. Additionally, one key task of reaction-text modeling -- experimental procedure prediction -- is less explored due to the absence of an open-source dataset. The task is to predict step-by-step actions of conducting chemical experiments and is crucial to automating chemical synthesis. To resolve the challenges above, we propose a new pretraining method, ReactXT, for reaction-text modeling, and a new dataset, OpenExp, for experimental procedure prediction. Specifically, ReactXT features three types of input contexts to incrementally pretrain LMs. Each of the three input contexts corresponds to a pretraining task to improve the text-based understanding of either reactions or single molecules. ReactXT demonstrates consistent improvements in experimental procedure prediction and molecule captioning and offers competitive results in retrosynthesis. Our code is available at https://github.com/syr-cn/ReactXT.