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RxnCluster: A Web-Based Tool for Exploring Reaction Clusters Leading to Target Molecules by Digitalizing Typical
Shaozhen Ding1, Yu Tian1, Dongliang Liu1
1Department of Chemical and Biomolecular Engineering, School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China.
ACS Synthetic Biology
|July 29, 2025
Summary
This study introduces RxnCluster, a novel platform for digitalizing biosynthetic patterns. It identifies multi-step reaction pathways for molecule biosynthesis, aiding in discovering new synthetic strategies.
Area of Science:
- Biotechnology
- Computational Chemistry
- Synthetic Biology
Background:
- Biosynthetic pathways are sequential reactions crucial for molecule production.
- Current in silico methods often overlook multi-step synthetic strategies.
- Molecular structures can be divided into substructures, suggesting similar biosynthetic approaches.
Purpose of the Study:
- To develop a user-friendly platform for digitalizing typical biosynthetic patterns.
- To enable the identification of multi-step reaction pathways for molecule biosynthesis.
- To facilitate pathway mining for novel biosynthetic strategies.
Main Methods:
- Constructed a platform named RxnCluster based on gene cluster concepts.
- Digitalized typical biosynthetic patterns into reaction rule clusters.
- Developed a database containing 14,378 biosynthetic patterns and 37,317 reaction combinations.
Main Results:
- RxnCluster successfully identifies reaction clusters with 1 to 4 steps.
- Identified pathways align with experimental findings from wet laboratories.
- The platform also reveals novel, previously unreported reaction clusters.
Conclusions:
- RxnCluster represents a novel approach to pathway mining for molecule biosynthesis.
- The platform facilitates the discovery of diverse and efficient synthetic strategies.
- This tool has the potential to accelerate the identification of new biosynthetic pathways.
