RT Journal Article
SR Electronic
T1 READRetro: Natural Product Biosynthesis Planning with Retrieval-Augmented Dual-View Retrosynthesis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.03.21.533616
DO 10.1101/2023.03.21.533616
A1 Seul Lee
A1 Taein Kim
A1 Min-Soo Choi
A1 Yejin Kwak
A1 Jeongbin Park
A1 Sung Ju Hwang
A1 Sang-Gyu Kim
YR 2023
UL http://biorxiv.org/content/early/2023/03/23/2023.03.21.533616.abstract
AB 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 StatementThe authors have declared no competing interest.