MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation

• URL: http://arxiv.org/abs/2302.09048v2
• Date: Mon, 5 Jun 2023 15:26:26 GMT
• Title: MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation
• Authors: Clement Vignac, Nagham Osman, Laura Toni, Pascal Frossard
• Abstract summary: This work introduces MiDi, a novel diffusion model for jointly generating molecular graphs and their corresponding 3D arrangement of atoms. Unlike existing methods that rely on predefined rules to determine molecular bonds based on the 3D conformation, MiDi offers an end-to-end differentiable approach that streamlines the molecule generation process.
• Score: 47.15291538945242
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work introduces MiDi, a novel diffusion model for jointly generating molecular graphs and their corresponding 3D arrangement of atoms. Unlike existing methods that rely on predefined rules to determine molecular bonds based on the 3D conformation, MiDi offers an end-to-end differentiable approach that streamlines the molecule generation process. Our experimental results demonstrate the effectiveness of this approach. On the challenging GEOM-DRUGS dataset, MiDi generates 92% of stable molecules, against 6% for the previous EDM model that uses interatomic distances for bond prediction, and 40% using EDM followed by an algorithm that directly optimize bond orders for validity. Our code is available at github.com/cvignac/MiDi.

Related papers

• 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)
• 3D molecule generation by denoising voxel grids [5.50581548670289]
  We propose a new score-based approach to generate 3D molecules represented as atomic densities on regular grids. We train a denoising neural network that learns to map from a smooth distribution of noisy molecules to the distribution of real molecules. Our experiments show that VoxMol captures the distribution of drug-like molecules better than state of the art, while being faster to generate samples.
  arXiv  Detail & Related papers  (2023-06-13T00:38:51Z)
• Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation [32.66694406638287]
  We propose a new joint 2D and 3D diffusion model (JODO) that generates molecules with atom types, formal charges, bond information, and 3D coordinates. Our model can also be extended for inverse molecular design targeting single or multiple quantum properties.
  arXiv  Detail & Related papers  (2023-05-21T04:49:53Z)
• Geometric Latent Diffusion Models for 3D Molecule Generation [172.15028281732737]
  Generative models, especially diffusion models (DMs), have achieved promising results for generating feature-rich geometries. We propose a novel and principled method for 3D molecule generation named Geometric Latent Diffusion Models (GeoLDM)
  arXiv  Detail & Related papers  (2023-05-02T01:07:22Z)
• MUDiff: Unified Diffusion for Complete Molecule Generation [104.7021929437504]
  We present a new model for generating a comprehensive representation of molecules, including atom features, 2D discrete molecule structures, and 3D continuous molecule coordinates. We propose a novel graph transformer architecture to denoise the diffusion process. Our model is a promising approach for designing stable and diverse molecules and can be applied to a wide range of tasks in molecular modeling.
  arXiv  Detail & Related papers  (2023-04-28T04:25:57Z)
• Geometry-Complete Diffusion for 3D Molecule Generation and Optimization [3.8366697175402225]
  We introduce the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation. GCDM outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings. We also show that GCDM's geometric features can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules.
  arXiv  Detail & Related papers  (2023-02-08T20:01:51Z)
• Equivariant Diffusion for Molecule Generation in 3D [74.289191525633]
  This work introduces a diffusion model for molecule computation generation in 3D that is equivariant to Euclidean transformations. Experimentally, the proposed method significantly outperforms previous 3D molecular generative methods regarding the quality of generated samples and efficiency at training time.
  arXiv  Detail & Related papers  (2022-03-31T12:52:25Z)
• Direct Molecular Conformation Generation [217.4815525740703]
  We propose a method that directly predicts the coordinates of atoms. Our method achieves state-of-the-art results on four public benchmarks.
  arXiv  Detail & Related papers  (2022-02-03T01:01:58Z)

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.