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READRetro: Natural Product Biosynthesis Planning with Retrieval-Augmented Dual-View Retrosynthesis

Seul Lee, Taein Kim, Min-Soo Choi, Yejin Kwak, Jeongbin Park, Sung Ju Hwang, Sang-Gyu Kim
doi: https://doi.org/10.1101/2023.03.21.533616
Seul Lee
1Kim Jaechul Graduate School of AI, KAIST, Daejeon, 34141, Republic of Korea
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Taein Kim
2Department of Biological Sciences, KAIST, Daejeon, 34141, Republic of Korea
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Min-Soo Choi
2Department of Biological Sciences, KAIST, Daejeon, 34141, Republic of Korea
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Yejin Kwak
3Department of BioMedical Convergence Engineering, Pusan National University, Republic of Korea
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Jeongbin Park
3Department of BioMedical Convergence Engineering, Pusan National University, Republic of Korea
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Sung Ju Hwang
1Kim Jaechul Graduate School of AI, KAIST, Daejeon, 34141, Republic of Korea
4School of Computing, KAIST, Daejeon, 34141, Republic of Korea
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  • For correspondence: sjhwang82@kaist.ac.kr sgkim1@kaist.ac.kr
Sang-Gyu Kim
2Department of Biological Sciences, KAIST, Daejeon, 34141, Republic of Korea
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  • For correspondence: sjhwang82@kaist.ac.kr sgkim1@kaist.ac.kr
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Abstract

Elucidating the biosynthetic pathways of natural products has been a major focus of biochemistry and pharmacy. However, predicting the whole pathways from target molecules to metabolic building blocks remains a challenge. Here we propose READRetro as a practical bio-retrosynthesis tool for planning the biosynthetic pathways of natural products. READRetro effectively resolves the tradeoff between generalizability and memorability in bio-retrosynthesis by implementing two separate modules; each module is responsible for either generalizability or memorability. Specifically, READRetro utilizes a rule-based retriever for memorability and an ensemble of two dual-representation-based deep learning models for generalizability. Through extensive experiments, READRetro was demonstrated to outperform existing models by a large margin in terms of both generalizability and memorability. READRetro was also capable of predicting the known pathways of complex plant secondary metabolites such as monoterpene indole alkaloids, demonstrating its applicability in the real-world bio-retrosynthesis planning of natural products. A website (https://readretro.net) and open-source code have been provided for READRetro, a practical tool with state-of-the-art performance for natural product biosynthesis research.

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-NC-ND 4.0 International license.
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Posted March 23, 2023.
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READRetro: Natural Product Biosynthesis Planning with Retrieval-Augmented Dual-View Retrosynthesis
Seul Lee, Taein Kim, Min-Soo Choi, Yejin Kwak, Jeongbin Park, Sung Ju Hwang, Sang-Gyu Kim
bioRxiv 2023.03.21.533616; doi: https://doi.org/10.1101/2023.03.21.533616
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READRetro: Natural Product Biosynthesis Planning with Retrieval-Augmented Dual-View Retrosynthesis
Seul Lee, Taein Kim, Min-Soo Choi, Yejin Kwak, Jeongbin Park, Sung Ju Hwang, Sang-Gyu Kim
bioRxiv 2023.03.21.533616; doi: https://doi.org/10.1101/2023.03.21.533616

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