mlsanityData to 7 Oct 2026

Molecular Hypergraph Grammar with its Application to Molecular Optimization

arXiv 1809.02745 ↗First posted 8 Sep 2018cs.LG stat.MLcited by 56

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Abstract (arXiv)

Molecular optimization aims to discover novel molecules with desirable properties. Two fundamental challenges are: (i) it is not trivial to generate valid molecules in a controllable way due to hard chemical constraints such as the valency conditions, and (ii) it is often costly to evaluate a property of a novel molecule, and therefore, the number of property evaluations is limited. These challenges are to some extent alleviated by a combination of a variational autoencoder (VAE) and Bayesian optimization (BO). VAE converts a molecule into/from its latent continuous vector, and BO optimizes a latent continuous vector (and its corresponding molecule) within a limited number of property evaluations. While the most recent work, for the first time, achieved 100% validity, its architecture is rather complex due to auxiliary neural networks other than VAE, making it difficult to train. This paper presents a molecular hypergraph grammar variational autoencoder (MHG-VAE), which uses a single VAE to achieve 100% validity. Our idea is to develop a graph grammar encoding the hard chemical constraints, called molecular hypergraph grammar (MHG), which guides VAE to always generate valid molecules. We also present an algorithm to construct MHG from a set of molecules.

Key facts. Cited by 56 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Sep 2018.

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Citing papers per year: 2021: 12; 2022: 11; 2023: 16; 2024: 9; 2025: 5; 2026: 3.

56 papers.

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  1. 2022Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pocketscited by 182
  2. 2021GraphDF: A Discrete Flow Model for Molecular Graph Generationcited by 177
  3. 2022Local Latent Space Bayesian Optimization over Structured Inputscited by 78
  4. 2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
  5. 2023InstructZero: Efficient Instruction Optimization for Black-Box Large Language Modelscited by 68
  6. 2023Grammar Prompting for Domain-Specific Language Generation with Large Language Modelscited by 58
  7. 2021Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spacescited by 45
  8. 2021Edge Representation Learning with Hypergraphscited by 37
  9. 2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
  10. 2022Data-Efficient Graph Grammar Learning for Molecular Generationcited by 26
  11. 2023Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddingscited by 24
  12. 2024Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structurescited by 20
38 more
  1. 2022NVDiff: Graph Generation through the Diffusion of Node Vectorscited by 18
  2. 2023Advancing Bayesian Optimization via Learning Correlated Latent Spacecited by 18
  3. 2021Bayesian Optimization over Permutation Spacescited by 17
  4. 2021C5T5: Controllable Generation of Organic Molecules with Transformerscited by 15
  5. 2022Molecule Generation for Drug Design: a Graph Learning Perspectivecited by 13
  6. 2022Modular Flows: Differential Molecular Generationcited by 11
  7. 2024Inversion-based Latent Bayesian Optimizationcited by 11
  8. 2023Deep reinforcement learning for process design: Review and perspectivecited by 9
  9. 2021Polygrammar: Grammar for Digital Polymer Representation and Generationcited by 8
  10. 2022Conditional β-VAE for De Novo Molecular Generationcited by 8
  11. 2023Molecule Design by Latent Prompt Transformercited by 8
  12. 2024HYGENE: A Diffusion-based Hypergraph Generation Methodcited by 8
  13. 2023MAGNet: Motif-Agnostic Generation of Molecules from Shapescited by 7
  14. 2023Molecular Hypergraph Neural Networkscited by 7
  15. 2024Representing Molecules as Random Walks Over Interpretable Grammarscited by 7
  16. 2025Recent Advances in Hypergraph Neural Networkscited by 7
  17. 2021Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Designcited by 6
  18. 2022Graph Machine Learning for Design of High-Octane Fuelscited by 6
  19. 2023The power of motifs as inductive bias for learning molecular distributionscited by 6
  20. 2021Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discoverycited by 5
  21. 2023Latent Space Bayesian Optimization with Latent Data Augmentation for Enhanced Explorationcited by 5
  22. 2021Graph Energy-based Model for Substructure Preserving Molecular Designcited by 4
  23. 2023MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Networkcited by 4
  24. 2025How to Make Large Language Models Generate 100% Valid Molecules?cited by 4
  25. 2024Data-Efficient Molecular Generation with Hierarchical Textual Inversioncited by 3
  26. 2025LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discoverycited by 3
  27. 2023Kernel-Elastic Autoencoder for Molecular Designcited by 2
  28. 2024SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learningcited by 2
  29. 2021Operator Autoencoders: Learning Physical Operations on Encoded Molecular Graphscited by 1
  30. 2021An In-depth Summary of Recent Artificial Intelligence Applications in Drug Designcited by 1
  31. 2022Biases in In Silico Evaluation of Molecular Optimization Methods and Bias-Reduced Evaluation Methodologycited by 1
  32. 2023Inverse Protein Folding Using Deep Bayesian Optimizationcited by 1
  33. 2023Improving Molecular Properties Prediction Through Latent Space Fusioncited by 1
  34. 2024GeoPos: A Minimal Positional Encoding for Enhanced Fine-Grained Details in Image Synthesis Using Convolutional Neural Networkscited by 1
  35. 2024FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproductioncited by 1
  36. 2022Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networkscited by 0
  37. 2023Dynamic Vertex Replacement Grammarscited by 0
  38. 2023Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Predictioncited by 0

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