3M-Diffusion: Latent Multi-Modal Diffusion for Text-Guided Generation of
Molecular Graphs
- URL: http://arxiv.org/abs/2403.07179v1
- Date: Mon, 11 Mar 2024 21:44:54 GMT
- Title: 3M-Diffusion: Latent Multi-Modal Diffusion for Text-Guided Generation of
Molecular Graphs
- Authors: Huaisheng Zhu, Teng Xiao, Vasant G Honavar
- Abstract summary: We propose 3M-Diffusion, a novel multi-modal molecular graph generation method.
We show that 3M-Diffusion can generate high-quality, novel and diverse molecular graphs that semantically match the textual description provided.
- Score: 20.84977867473101
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Generating molecules with desired properties is a critical task with broad
applications in drug discovery and materials design. Inspired by recent
advances in large language models, there is a growing interest in using natural
language descriptions of molecules to generate molecules with the desired
properties. Most existing methods focus on generating molecules that precisely
match the text description. However, practical applications call for methods
that generate diverse, and ideally novel, molecules with the desired
properties. We propose 3M-Diffusion, a novel multi-modal molecular graph
generation method, to address this challenge. 3M-Diffusion first encodes
molecular graphs into a graph latent space aligned with text descriptions. It
then reconstructs the molecular structure and atomic attributes based on the
given text descriptions using the molecule decoder. It then learns a
probabilistic mapping from the text space to the latent molecular graph space
using a diffusion model. The results of our extensive experiments on several
datasets demonstrate that 3M-Diffusion can generate high-quality, novel and
diverse molecular graphs that semantically match the textual description
provided.
Related papers
- LDMol: Text-Conditioned Molecule Diffusion Model Leveraging Chemically Informative Latent Space [55.5427001668863]
We present a novel latent diffusion model dubbed LDMol, which enables a natural text-conditioned molecule generation.
Specifically, LDMol is composed of three building blocks: a molecule encoder that produces a chemically informative feature space, a natural language-conditioned latent diffusion model using a Diffusion Transformer (DiT), and an autoregressive decoder for molecule regressive.
arXiv Detail & Related papers (2024-05-28T04:59:13Z) - Data-Efficient Molecular Generation with Hierarchical Textual Inversion [48.816943690420224]
We introduce Hierarchical textual Inversion for Molecular generation (HI-Mol), a novel data-efficient molecular generation method.
HI-Mol is inspired by the importance of hierarchical information, e.g., both coarse- and fine-grained features, in understanding the molecule distribution.
Compared to the conventional textual inversion method in the image domain using a single-level token embedding, our multi-level token embeddings allow the model to effectively learn the underlying low-shot molecule distribution.
arXiv Detail & Related papers (2024-05-05T08:35:23Z) - 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) - 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) - A Molecular Multimodal Foundation Model Associating Molecule Graphs with
Natural Language [63.60376252491507]
We propose a molecular multimodal foundation model which is pretrained from molecular graphs and their semantically related textual data.
We believe that our model would have a broad impact on AI-empowered fields across disciplines such as biology, chemistry, materials, environment, and medicine.
arXiv Detail & Related papers (2022-09-12T00:56:57Z) - Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning [68.8204255655161]
We introduce a novel framework for scalable 3D design that uses a hierarchical agent to build molecules.
In a variety of experiments, we show that our agent, guided only by energy considerations, can efficiently learn to produce molecules with over 100 atoms.
arXiv Detail & Related papers (2022-02-01T18:54:24Z) - Learning a Continuous Representation of 3D Molecular Structures with
Deep Generative Models [0.0]
Generative models are an entirely different approach that learn to represent and optimize molecules in a continuous latent space.
We describe deep generative models of three dimensional molecular structures using atomic density grids.
We are also able to sample diverse sets of molecules based on a given input compound to increase the probability of creating valid, drug-like molecules.
arXiv Detail & Related papers (2020-10-17T01:15:47Z) - A Deep Generative Model for Fragment-Based Molecule Generation [21.258861822241272]
We develop a language model for small molecular substructures called fragments.
In other words, we generate molecules fragment by fragment, instead of atom by atom.
We show experimentally that our model largely outperforms other language model-based competitors.
arXiv Detail & Related papers (2020-02-28T15:55:11Z)
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.