# N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules

arXiv: https://arxiv.org/abs/1806.09206 · HTML: https://mlsanity.com/paper/1806.09206/

## Key facts

- Cited by 93 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2018.

## Built on by (most cited first, 30 of 93)

- [Pre-training Molecular Graph Representation with 3D Geometry](https://mlsanity.com/paper/2110.07728/) (2021)
- [Large-Scale Chemical Language Representations Capture Molecular Structure and Properties](https://mlsanity.com/paper/2106.09553/) (2021)
- [ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction](https://mlsanity.com/paper/2106.06130/) (2021)
- [Spherical Message Passing for 3D Graph Networks](https://mlsanity.com/paper/2102.05013/) (2021)
- [Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing](https://mlsanity.com/paper/2212.10789/) (2022)
- [Pre-training via Denoising for Molecular Property Prediction](https://mlsanity.com/paper/2206.00133/) (2022)
- [GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning](https://mlsanity.com/paper/2306.13089/) (2023)
- [Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching](https://mlsanity.com/paper/2206.13602/) (2022)
- [MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design](https://mlsanity.com/paper/2203.14500/) (2022)
- [Molecular Contrastive Learning with Chemical Element Knowledge Graph](https://mlsanity.com/paper/2112.00544/) (2021)
- [Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast](https://mlsanity.com/paper/2202.09346/) (2022)
- [Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction](https://mlsanity.com/paper/2205.01893/) (2022)
- [A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining](https://mlsanity.com/paper/2305.18407/) (2023)
- [KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction](https://mlsanity.com/paper/2206.03364/) (2022)
- [ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback](https://mlsanity.com/paper/2305.18090/) (2023)
- [Artificial Intelligence in Drug Discovery: Applications and Techniques](https://mlsanity.com/paper/2106.05386/) (2021)
- [Dual-view Molecule Pre-training](https://mlsanity.com/paper/2106.10234/) (2021)
- [Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Model](https://mlsanity.com/paper/2211.10590/) (2022)
- [A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications](https://mlsanity.com/paper/2202.07893/) (2022)
- [UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation](https://mlsanity.com/paper/2408.00863/) (2024)
- [Evaluating Self-Supervised Learning for Molecular Graph Embeddings](https://mlsanity.com/paper/2206.08005/) (2022)
- [Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials](https://mlsanity.com/paper/2306.09375/) (2023)
- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [Learning Attributed Graph Representations with Communicative Message Passing Transformer](https://mlsanity.com/paper/2107.08773/) (2021)
- [Molecular geometric deep learning](https://mlsanity.com/paper/2306.15065/) (2023)
- [Structured Multi-task Learning for Molecular Property Prediction](https://mlsanity.com/paper/2203.04695/) (2022)
- [CoSP: Co-supervised pretraining of pocket and ligand](https://mlsanity.com/paper/2206.12241/) (2022)
- [From Generalist to Specialist: A Survey of Large Language Models for Chemistry](https://mlsanity.com/paper/2412.19994/) (2024)
- [Attentive Walk-Aggregating Graph Neural Networks](https://mlsanity.com/paper/2110.02667/) (2021)
- [Attention-wise masked graph contrastive learning for predicting molecular property](https://mlsanity.com/paper/2206.08262/) (2022)

## Abstract (arXiv)

Machine learning techniques have recently been adopted in various applications in medicine, biology, chemistry, and material engineering. An important task is to predict the properties of molecules, which serves as the main subroutine in many downstream applications such as virtual screening and drug design. Despite the increasing interest, the key challenge is to construct proper representations of molecules for learning algorithms. This paper introduces the N-gram graph, a simple unsupervised representation for molecules. The method first embeds the vertices in the molecule graph. It then constructs a compact representation for the graph by assembling the vertex embeddings in short walks in the graph, which we show is equivalent to a simple graph neural network that needs no training. The representations can thus be efficiently computed and then used with supervised learning methods for prediction. Experiments on 60 tasks from 10 benchmark datasets demonstrate its advantages over both popular graph neural networks and traditional representation methods. This is complemented by theoretical analysis showing its strong representation and prediction power.