Geometric Latent Diffusion Models for 3D Molecule Generation
- URL: http://arxiv.org/abs/2305.01140v1
- Date: Tue, 2 May 2023 01:07:22 GMT
- Title: Geometric Latent Diffusion Models for 3D Molecule Generation
- Authors: Minkai Xu, Alexander Powers, Ron Dror, Stefano Ermon, Jure Leskovec
- Abstract summary: 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)
- Score: 172.15028281732737
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Generative models, especially diffusion models (DMs), have achieved promising
results for generating feature-rich geometries and advancing foundational
science problems such as molecule design. Inspired by the recent huge success
of Stable (latent) Diffusion models, we propose a novel and principled method
for 3D molecule generation named Geometric Latent Diffusion Models (GeoLDM).
GeoLDM is the first latent DM model for the molecular geometry domain, composed
of autoencoders encoding structures into continuous latent codes and DMs
operating in the latent space. Our key innovation is that for modeling the 3D
molecular geometries, we capture its critical roto-translational equivariance
constraints by building a point-structured latent space with both invariant
scalars and equivariant tensors. Extensive experiments demonstrate that GeoLDM
can consistently achieve better performance on multiple molecule generation
benchmarks, with up to 7\% improvement for the valid percentage of large
biomolecules. Results also demonstrate GeoLDM's higher capacity for
controllable generation thanks to the latent modeling. Code is provided at
\url{https://github.com/MinkaiXu/GeoLDM}.
Related papers
- NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation [72.22099363325145]
We propose a foundation model -- NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation.
NExT-Mol uses an extensively pretrained molecule LM for 1D molecule generation, and subsequently predicts the generated molecule's 3D conformers.
We enhance NExT-Mol's performance by scaling up the LM's model size, refining the diffusion neural architecture, and applying 1D to 3D transfer learning.
arXiv Detail & Related papers (2025-02-18T08:40:13Z) - LMDM:Latent Molecular Diffusion Model For 3D Molecule Generation [6.720821935934759]
We propose a latent molecular diffusion model that can make generated 3D molecules rich in diversity and maintain rich geometric features.
The model captures the information of the forces and local constraints between atoms so that the generated molecules can maintain Euclidean transformation.
In the experiment, the quality of the samples we generated and the convergence speed of the model have been significantly improved.
arXiv Detail & Related papers (2024-12-05T15:25:18Z) - Geometric Trajectory Diffusion Models [58.853975433383326]
Generative models have shown great promise in generating 3D geometric systems.
Existing approaches only operate on static structures, neglecting the fact that physical systems are always dynamic in nature.
We propose geometric trajectory diffusion models (GeoTDM), the first diffusion model for modeling the temporal distribution of 3D geometric trajectories.
arXiv Detail & Related papers (2024-10-16T20:36:41Z) - Geometry Informed Tokenization of Molecules for Language Model Generation [85.80491667588923]
We consider molecule generation in 3D space using language models (LMs)
Although tokenization of molecular graphs exists, that for 3D geometries is largely unexplored.
We propose the Geo2Seq, which converts molecular geometries into $SE(3)$-invariant 1D discrete sequences.
arXiv Detail & Related papers (2024-08-19T16:09:59Z) - 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) - 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) - MDM: Molecular Diffusion Model for 3D Molecule Generation [19.386468094571725]
Existing diffusion-based 3D molecule generation methods could suffer from unsatisfactory performances.
Interatomic relations are not in molecules' 3D point cloud representations.
Proposed model significantly outperforms existing methods for both unconditional and conditional generation tasks.
arXiv Detail & Related papers (2022-09-13T03:40:18Z) - GeoMol: Torsional Geometric Generation of Molecular 3D Conformer
Ensembles [60.12186997181117]
Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery.
Existing generative models have several drawbacks including lack of modeling important molecular geometry elements.
We propose GeoMol, an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate 3D conformers.
arXiv Detail & Related papers (2021-06-08T14:17:59Z)
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.