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An Equivariant Generative Framework for Molecular Graph-Structure Co-Design

Zaixi Zhang, Qi Liu, Chee-Kong Lee, Chang-Yu Hsieh, Enhong Chen
doi: https://doi.org/10.1101/2023.04.13.536803
Zaixi Zhang
1Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China, Hefei, Anhui 230026, China
2State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, 230088, China
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Qi Liu
1Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China, Hefei, Anhui 230026, China
2State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, 230088, China
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  • For correspondence: qiliuql@ustc.edu.cn
Chee-Kong Lee
3Tencent America, Palo Alto, CA 94306, United States
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Chang-Yu Hsieh
4Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
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Enhong Chen
1Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China, Hefei, Anhui 230026, China
2State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, 230088, China
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ABSTRACT

Designing molecules with desirable physiochemical properties and functionalities is a long-standing challenge in chemistry, material science, and drug discovery. Recently, machine learning-based generative models have emerged as promising approaches for de novo molecule design. However, further refinement of methodology is highly desired as most existing methods lack unified modeling of 2D topology and 3D geometry information and fail to effectively learn the structure-property relationship for molecule design. Here we present MolCode, a roto-translation equivariant generative framework for Molecular graph-structure Co-design. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Extensive experimental results show that MolCode outperforms previous methods on a series of challenging tasks including de novo molecule design, targeted molecule discovery, and structure-based drug design. Particularly, MolCode not only consistently generates valid (99.95% Validity) and diverse (98.75% Uniqueness) molecular graphs/structures with desirable properties, but also generate drug-like molecules with high affinity to target proteins (61.8% high affinity ratio), which demonstrates MolCode’s potential applications in material design and drug discovery. Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provides new insights into machine learning-based molecule representation and generation.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted April 17, 2023.
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An Equivariant Generative Framework for Molecular Graph-Structure Co-Design
Zaixi Zhang, Qi Liu, Chee-Kong Lee, Chang-Yu Hsieh, Enhong Chen
bioRxiv 2023.04.13.536803; doi: https://doi.org/10.1101/2023.04.13.536803
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An Equivariant Generative Framework for Molecular Graph-Structure Co-Design
Zaixi Zhang, Qi Liu, Chee-Kong Lee, Chang-Yu Hsieh, Enhong Chen
bioRxiv 2023.04.13.536803; doi: https://doi.org/10.1101/2023.04.13.536803

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