Conditional Constrained Graph Variational Autoencoders for Molecule Design

• URL: http://arxiv.org/abs/2009.00725v1
• Date: Tue, 1 Sep 2020 21:58:07 GMT
• Title: Conditional Constrained Graph Variational Autoencoders for Molecule Design
• Authors: Davide Rigoni, Nicol\`o Navarin and Alessandro Sperduti
• Abstract summary: 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.
• Score: 70.59828655929194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, deep generative models for graphs have been used to generate new molecules. These models have produced good results, leading to several proposals in the literature. However, these models may have troubles learning some of the complex laws governing the chemical world. In this work, we explore the usage of the histogram of atom valences to drive the generation of molecules in such models. We present Conditional Constrained Graph Variational Autoencoder (CCGVAE), a model that implements this key-idea in a state-of-the-art model, and shows improved results on several evaluation metrics on two commonly adopted datasets for molecule generation.

Related papers

• IFH: a Diffusion Framework for Flexible Design of Graph Generative Models [53.219279193440734]
  Graph generative models can be classified into two prominent families: one-shot models, which generate a graph in one go, and sequential models, which generate a graph by successive additions of nodes and edges. This paper proposes a graph generative model, called Insert-Fill-Halt (IFH), that supports the specification of a sequentiality degree.
  arXiv  Detail & Related papers  (2024-08-23T16:24:40Z)
• Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation [0.6800113478497425]
  We return to the simplest representation of molecules, and investigate overlooked limitations of classical generative approaches. We propose a hybrid model in the form of a novel regularizer that leverages the strengths of both to improve validity, conditional generation, and style transfer of molecular sequences.
  arXiv  Detail & Related papers  (2024-08-19T11:50:23Z)
• Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space [46.11163798008912]
  We introduce a new framework for molecular graph generation with 3D molecular generative models. Our framework maps molecular graphs to Euclidean point clouds via synthetic conformer coordinates and learns the inverse map using an E(n)-Equivariant Graph Neural Network (EGNN) The induced point cloud-structured latent space is well-suited to apply existing 3D molecular generative models.
  arXiv  Detail & Related papers  (2024-06-15T05:29:07Z)
• RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Design [70.59828655929194]
  Deep Graph Variational Autoencoders are among the most powerful machine learning tools with which it is possible to address this problem. We propose RGCVAE, an efficient and effective Graph Variational Autoencoder based on: (i) an encoding network exploiting a new powerful Graph Isomorphism Network; (ii) a novel probabilistic decoding component.
  arXiv  Detail & Related papers  (2023-05-19T14:23:48Z)
• MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation [4.517805235253331]
  MolHF is a new hierarchical flow-based model that generates molecular graphs in a coarse-to-fine manner. MolHF is the first flow-based model that can be applied to model larger molecules (polymer) with more than 100 heavy atoms.
  arXiv  Detail & Related papers  (2023-05-15T08:59:35Z)
• GraphPiece: Efficiently Generating High-Quality Molecular Graph with Substructures [7.021635649909492]
  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.
  arXiv  Detail & Related papers  (2021-06-29T05:26:18Z)
• Learning Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
  We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
  arXiv  Detail & Related papers  (2021-02-20T03:17:58Z)
• Learning Latent Space Energy-Based Prior Model for Molecule Generation [59.875533935578375]
  We learn latent space energy-based prior model with SMILES representation for molecule modeling. Our method is able to generate molecules with validity and uniqueness competitive with state-of-the-art models.
  arXiv  Detail & Related papers  (2020-10-19T09:34:20Z)
• Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder [0.0]
  We develop a molecular generative model based on variational inference and graph theory in the small data regime. The model's performance is evaluated by generating molecules with desired target properties.
  arXiv  Detail & Related papers  (2020-09-29T09:05:27Z)
• Learning Graph Models for Retrosynthesis Prediction [90.15523831087269]
  Retrosynthesis prediction is a fundamental problem in organic synthesis. This paper introduces a graph-based approach that capitalizes on the idea that the graph topology of precursor molecules is largely unaltered during a chemical reaction. Our model achieves a top-1 accuracy of $53.7%$, outperforming previous template-free and semi-template-based methods.
  arXiv  Detail & Related papers  (2020-06-12T09:40:42Z)

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.