# Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small Models

arXiv: https://arxiv.org/abs/2408.10124 · HTML: https://mlsanity.com/paper/2408.10124/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 33 references that are in the map.
- First posted on arXiv 19 Aug 2024.

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

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Directional Message Passing for Molecular Graphs](https://mlsanity.com/paper/2003.03123/) (2020)
- [Pre-training Molecular Graph Representation with 3D Geometry](https://mlsanity.com/paper/2110.07728/) (2021)
- [Large Language Models on Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2312.02783/) (2023)
- [Pre-training via Denoising for Molecular Property Prediction](https://mlsanity.com/paper/2206.00133/) (2022)
- [Molecular Contrastive Learning with Chemical Element Knowledge Graph](https://mlsanity.com/paper/2112.00544/) (2021)
- [Can Large Language Models Empower Molecular Property Prediction?](https://mlsanity.com/paper/2307.07443/) (2023)
- [Heterogeneous Deep Graph Infomax](https://mlsanity.com/paper/1911.08538/) (2019)
- [Chemical-Reaction-Aware Molecule Representation Learning](https://mlsanity.com/paper/2109.09888/) (2021)
- [KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction](https://mlsanity.com/paper/2206.03364/) (2022)
- [Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs](https://mlsanity.com/paper/2311.14324/) (2023)
- [Automated 3D Pre-Training for Molecular Property Prediction](https://mlsanity.com/paper/2306.07812/) (2023)
- [Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection](https://mlsanity.com/paper/2101.11206/) (2021)
- [Deep Molecular Representation Learning via Fusing Physical and Chemical Information](https://mlsanity.com/paper/2112.04624/) (2021)
- [GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction](https://mlsanity.com/paper/2310.03030/) (2023)
- [Label Contrastive Coding based Graph Neural Network for Graph Classification](https://mlsanity.com/paper/2101.05486/) (2021)
- [MolTailor: Tailoring Chemical Molecular Representation to Specific Tasks via Text Prompts](https://mlsanity.com/paper/2401.11403/) (2024)

## Built on by (most cited first, 2 of 2)

- [DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining](https://mlsanity.com/paper/2502.08949/) (2025)
- [NanoNet: Parameter-Efficient Learning with Label-Scarce Supervision for Lightweight Text Mining Model](https://mlsanity.com/paper/2602.06093/) (2026)

## Abstract (arXiv)

Molecular property prediction is a crucial foundation for drug discovery. In recent years, pre-trained deep learning models have been widely applied to this task. Some approaches that incorporate prior biological domain knowledge into the pre-training framework have achieved impressive results. However, these methods heavily rely on biochemical experts, and retrieving and summarizing vast amounts of domain knowledge literature is both time-consuming and expensive. Large Language Models (LLMs) have demonstrated remarkable performance in understanding and efficiently providing general knowledge. Nevertheless, they occasionally exhibit hallucinations and lack precision in generating domain-specific knowledge. Conversely, Domain-specific Small Models (DSMs) possess rich domain knowledge and can accurately calculate molecular domain-related metrics. However, due to their limited model size and singular functionality, they lack the breadth of knowledge necessary for comprehensive representation learning. To leverage the advantages of both approaches in molecular property prediction, we propose a novel Molecular Graph representation learning framework that integrates Large language models and Domain-specific small models (MolGraph-LarDo). Technically, we design a two-stage prompt strategy where DSMs are introduced to calibrate the knowledge provided by LLMs, enhancing the accuracy of domain-specific information and thus enabling LLMs to generate more precise textual descriptions for molecular samples. Subsequently, we employ a multi-modal alignment method to coordinate various modalities, including molecular graphs and their corresponding descriptive texts, to guide the pre-training of molecular representations. Extensive experiments demonstrate the effectiveness of the proposed method.