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Article

3D Conformational Generative Models for Biological Structures Using Graph Information-Embedded Relative Coordinates

by
Mingyuan Xu
1,*,†,
Weifeng Huang
2,†,
Min Xu
3,
Jinping Lei
2,* and
Hongming Chen
1,*
1
Guangzhou Laboratory, Guangzhou International Bio Island, Guangzhou 510005, China
2
School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou 510006, China
3
XtalPi, International Biomedical Innovation Park II 3F, 2, Shenzhen 518000, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Molecules 2023, 28(1), 321; https://doi.org/10.3390/molecules28010321
Submission received: 9 November 2022 / Revised: 13 December 2022 / Accepted: 24 December 2022 / Published: 31 December 2022
(This article belongs to the Special Issue Molecular Simulation in Modern Chemical Physics)
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Abstract

Developing molecular generative models for directly generating 3D conformation has recently become a hot research area. Here, an autoencoder based generative model was proposed for molecular conformation generation. A unique feature of our method is that the graph information embedded relative coordinate (GIE-RC), satisfying translation and rotation invariance, was proposed as a novel way for encoding molecular three-dimensional structure. Compared with commonly used Cartesian coordinate and internal coordinate, GIE-RC is less sensitive on errors when decoding latent variables to 3D coordinates. By using this method, a complex 3D generation task can be turned into a graph node feature generation problem. Examples were shown that the GIE-RC based autoencoder model can be used for both ligand and peptide conformation generation. Additionally, this model was used as an efficient conformation sampling method to augment conformation data needed in the construction of neural network-based force field.
Keywords: conformation sampling; generative model conformation sampling; generative model
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MDPI and ACS Style

Xu, M.; Huang, W.; Xu, M.; Lei, J.; Chen, H. 3D Conformational Generative Models for Biological Structures Using Graph Information-Embedded Relative Coordinates. Molecules 2023, 28, 321. https://doi.org/10.3390/molecules28010321

AMA Style

Xu M, Huang W, Xu M, Lei J, Chen H. 3D Conformational Generative Models for Biological Structures Using Graph Information-Embedded Relative Coordinates. Molecules. 2023; 28(1):321. https://doi.org/10.3390/molecules28010321

Chicago/Turabian Style

Xu, Mingyuan, Weifeng Huang, Min Xu, Jinping Lei, and Hongming Chen. 2023. "3D Conformational Generative Models for Biological Structures Using Graph Information-Embedded Relative Coordinates" Molecules 28, no. 1: 321. https://doi.org/10.3390/molecules28010321

APA Style

Xu, M., Huang, W., Xu, M., Lei, J., & Chen, H. (2023). 3D Conformational Generative Models for Biological Structures Using Graph Information-Embedded Relative Coordinates. Molecules, 28(1), 321. https://doi.org/10.3390/molecules28010321

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