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Generation of 3D Tumor Spheroids for Drug Evaluation Studies

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Published on: February 24, 2023

Geometric deep learning methods and applications in 3D structure-based drug design.

Qifeng Bai¹, Tingyang Xu², Junzhou Huang³

¹School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, Gansu, PR China.

Drug Discovery Today

|May 17, 2024

Summary

This summary is machine-generated.

Geometric deep learning advances 3D structure-based drug design (SBDD) by enabling neural networks to learn complex molecular data. This review covers key methods and applications for innovative drug discovery.

Keywords:

3D structure-based drug design deep learning generative models geometric deep learning

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Area of Science:

Computational chemistry and cheminformatics
Artificial intelligence in drug discovery

Background:

3D structure-based drug design (SBDD) is crucial for discovering novel therapeutics.
Accurate modeling of complex 3D molecular data presents a significant challenge in SBDD.
Geometric deep learning offers a powerful approach to address these modeling challenges.

Purpose of the Study:

To review geometric deep learning methods applicable to 3D SBDD.
To highlight advanced applications of these methods in drug discovery.
To provide insights for researchers in the drug discovery community.

Main Methods:

Utilizing neural network models to learn from non-Euclidean data, including 3D molecular graphs.
Exploring equivariant graph neural networks (EGNNs) for molecular representation.
Summarizing six generative model approaches: diffusion models, flow-based models, GANs, VAEs, autoregressive models, and energy-based models.

Main Results:

Geometric deep learning methods effectively handle complex 3D molecular data.
These methods enable accurate model training for 3D SBDD.
A range of generative models are applicable for molecular design.

Conclusions:

Geometric deep learning is a transformative approach for 3D SBDD.
The reviewed methods and applications offer significant potential for accelerating innovative drug discovery.
This review serves as a valuable resource for the drug discovery field.