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Updated: Feb 22, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Predicting novel metabolic pathways through subgraph mining.

Aravind Sankar¹, Sayan Ranu^1,2, Karthik Raman^2,3

¹Department of Computer Science and Engineering.

Bioinformatics (Oxford, England)

|September 30, 2017

Summary

This summary is machine-generated.

We developed a new subgraph mining method to predict biochemical transformations between molecules using only their structures. This approach rapidly predicts novel biosynthetic pathways and scales to large reaction databases.

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Area of Science:

Computational chemistry and cheminformatics
Systems biology and metabolic engineering

Background:

Predicting metabolic pathways is crucial for metabolic engineering.
Existing methods require detailed mechanistic understanding and accurate reactant mapping, which are often difficult to obtain.
There is a need for a rapid prediction method relying solely on molecular structures.

Purpose of the Study:

To develop a robust method for predicting biochemical transformations between molecules based on subgraph mining.
To enable rapid prediction of biosynthetic pathways, even for novel molecules not present in existing databases.
To provide a reliable ranking of potential pathways.

Main Methods:

Utilized subgraph edit distance to map reactants and products based on chemical structures.
Identified reaction centers and their neighborhoods to create a reaction signature.
Stored reaction signatures in a reaction rule network for efficient pathway prediction.

Main Results:

Successfully predicted biochemical transformations and pathways between previously unseen molecules.
Demonstrated pathway prediction for molecules absent from the KEGG database.
Developed a heuristic for directed search, recovering natural biosynthetic pathways and ranking alternatives.

Conclusions:

The subgraph mining approach provides a robust and rapid method for predicting biochemical reaction pathways.
The method effectively handles novel molecules and scales well to large reaction datasets.
The developed reaction rule network and heuristic search offer significant advancements in metabolic engineering and drug discovery.