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Related Experiment Video

Updated: Aug 9, 2025

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

Published on: February 23, 2018

Geometry-Complete Diffusion for 3D Molecule Generation and Optimization.

Alex Morehead¹, Jianlin Cheng¹

¹Electrical Engineering & Computer Science, NextGen Precision Health, University of Missouri, Columbia, 65211, Missouri, USA.

|February 17, 2023

Summary

This summary is machine-generated.

The Geometry-Complete Diffusion Model (GCDM) generates valid, stable 3D molecules by learning geometric properties, outperforming prior methods. This advances 3D molecule generation and optimization for drug design.

Keywords:

3D molecules Diffusion generative modeling Geometric deep learning

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Last Updated: Aug 9, 2025

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

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Published on: February 23, 2018

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Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

Area of Science:

Computational chemistry
Machine learning
Drug discovery

Background:

Deep learning models using graph neural networks (GNNs) are used for 3D molecule generation.
Existing methods struggle to learn crucial geometric properties, limiting the generation of valid, large 3D molecules.

Purpose of the Study:

To introduce a novel 3D molecule generation model that addresses limitations of current methods.
To improve the generation of valid and energetically stable large 3D molecules.

Main Methods:

Developed the Geometry-Complete Diffusion Model (GCDM).
Utilized equivariant GNNs within a diffusion framework to learn geometric properties.
Evaluated GCDM on QM9 and GEOM-Drugs datasets.

Main Results:

GCDM significantly outperforms existing 3D molecular diffusion models.
GCDM successfully generates valid and energetically stable large molecules.
Extensions of GCDM demonstrate versatility in designing molecules for protein pockets and optimizing existing molecules.

Conclusions:

GCDM represents a significant advancement in 3D molecule generation.
The model's ability to learn geometric properties is key to its success.
GCDM offers new possibilities for molecular design and optimization in drug discovery.