GraphPiece: Efficiently Generating High-Quality Molecular Graph with Substructures

• URL: http://arxiv.org/abs/2106.15098v1
• Date: Tue, 29 Jun 2021 05:26:18 GMT
• Title: GraphPiece: Efficiently Generating High-Quality Molecular Graph with Substructures
• Authors: Xiangzhe Kong, Zhixing Tan, Yang Liu
• Abstract summary: We propose a method to automatically discover common substructures, which we call em graph pieces, from given molecular graphs. Based on graph pieces, we leverage a variational autoencoder to generate molecules in two phases: piece-level graph generation followed by bond completion.
• Score: 7.021635649909492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Molecular graph generation is a fundamental but challenging task in various applications such as drug discovery and material science, which requires generating valid molecules with desired properties. Auto-regressive models, which usually construct graphs following sequential actions of adding nodes and edges at the atom-level, have made rapid progress in recent years. However, these atom-level models ignore high-frequency subgraphs that not only capture the regularities of atomic combination in molecules but also are often related to desired chemical properties. In this paper, we propose a method to automatically discover such common substructures, which we call {\em graph pieces}, from given molecular graphs. Based on graph pieces, we leverage a variational autoencoder to generate molecules in two phases: piece-level graph generation followed by bond completion. Experiments show that our graph piece variational autoencoder achieves better performance over state-of-the-art baselines on property optimization and constrained property optimization tasks with higher computational efficiency.

Related papers

• GraphXForm: Graph transformer for computer-aided molecular design with application to extraction [73.1842164721868]
  We present GraphXForm, a decoder-only graph transformer architecture, which is pretrained on existing compounds and then fine-tuned. We evaluate it on two solvent design tasks for liquid-liquid extraction, showing that it outperforms four state-of-the-art molecular design techniques.
  arXiv  Detail & Related papers  (2024-11-03T19:45:15Z)
• MolGrapher: Graph-based Visual Recognition of Chemical Structures [50.13749978547401]
  We introduce MolGrapher to recognize chemical structures visually. We treat all candidate atoms and bonds as nodes and put them in a graph. We classify atom and bond nodes in the graph with a Graph Neural Network.
  arXiv  Detail & Related papers  (2023-08-23T16:16:11Z)
• Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations [55.42602325017405]
  We propose a novel method called GODE, which takes into account the two-level structure of individual molecules. By pre-training two graph neural networks (GNNs) on different graph structures, combined with contrastive learning, GODE fuses molecular structures with their corresponding knowledge graph substructures. When fine-tuned across 11 chemical property tasks, our model outperforms existing benchmarks, registering an average ROC-AUC uplift of 13.8% for classification tasks and an average RMSE/MAE enhancement of 35.1% for regression tasks.
  arXiv  Detail & Related papers  (2023-06-02T15:49:45Z)
• Graph Generation with Diffusion Mixture [57.78958552860948]
  Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures. We propose a generative framework that models the topology of graphs by explicitly learning the final graph structures of the diffusion process.
  arXiv  Detail & Related papers  (2023-02-07T17:07:46Z)
• Graph neural networks for the prediction of molecular structure-property relationships [59.11160990637615]
  Graph neural networks (GNNs) are a novel machine learning method that directly work on the molecular graph. GNNs allow to learn properties in an end-to-end fashion, thereby avoiding the need for informative descriptors. We describe the fundamentals of GNNs and demonstrate the application of GNNs via two examples for molecular property prediction.
  arXiv  Detail & Related papers  (2022-07-25T11:30:44Z)
• FunQG: Molecular Representation Learning Via Quotient Graphs [0.0]
  We propose a novel molecular graph coarsening framework named FunQG. FunQG uses Functional groups as influential building blocks of a molecule to determine its properties. We show that the resulting informative graphs are much smaller than the molecular graphs and thus are good candidates for training GNNs.
  arXiv  Detail & Related papers  (2022-07-18T13:36:20Z)
• Molecular Graph Generation via Geometric Scattering [7.796917261490019]
  Graph neural networks (GNNs) have been used extensively for addressing problems in drug design and discovery. We propose a representation-first approach to molecular graph generation. We show that our architecture learns meaningful representations of drug datasets and provides a platform for goal-directed drug synthesis.
  arXiv  Detail & Related papers  (2021-10-12T18:00:23Z)
• Conditional Constrained Graph Variational Autoencoders for Molecule Design [70.59828655929194]
  We present Conditional Constrained Graph Variational Autoencoder (CCGVAE), a model that implements this key-idea in a state-of-the-art model. We show improved results on several evaluation metrics on two commonly adopted datasets for molecule generation.
  arXiv  Detail & Related papers  (2020-09-01T21:58:07Z)
• MoFlow: An Invertible Flow Model for Generating Molecular Graphs [19.829612234339578]
  MoFlow is a flow-based graph generative model to learn invertible mappings between molecular graphs and latent representations. Our model has merits including exact and tractable likelihood training, efficient one-pass embedding and generation, chemical validity guarantees, 100% reconstruction of training data, and good generalization ability.
  arXiv  Detail & Related papers  (2020-06-17T20:14:19Z)
• Hierarchical Generation of Molecular Graphs using Structural Motifs [38.637412590671865]
  We propose a new hierarchical graph encoder-decoder that employs significantly larger and more flexible graph motifs as basic building blocks. Our encoder produces a multi-resolution representation for each molecule in a fine-to-coarse fashion, from atoms to connected motifs. We evaluate our model on multiple molecule generation tasks, including polymers, and show that our model significantly outperforms previous state-of-the-art baselines.
  arXiv  Detail & Related papers  (2020-02-08T21:21:04Z)
• GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation [45.360695120154]
  We propose a flow-based autoregressive model for graph generation called GraphAF. GraphAF combines the advantages of both autoregressive and flow-based approaches and enjoys: (1) high model flexibility for data density estimation; (2) efficient parallel computation for training; (3) an iterative sampling process, which allows leveraging chemical domain knowledge for valency checking.
  arXiv  Detail & Related papers  (2020-01-26T01:12:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.