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A Web Tool for Generating High Quality Machine-readable Biological Pathways

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Published on: February 8, 2017

Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities.

Idil Ismail¹, Raphael Chantreau Majerus¹, Scott Habershon¹

¹Department of Chemistry, University of Warwick, CoventryCV4 7AL, United Kingdom.

The Journal of Physical Chemistry. A

|October 3, 2022

Summary

Graph-based methods automate chemical reaction discovery by modeling molecular structures and reactions. Recent advances integrate computational chemistry for exploring complex reaction networks, acting as virtual reaction vessels.

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Area of Science:

Computational Chemistry
Chemical Informatics

Background:

Graph-based descriptors (e.g., adjacency matrices) offer a compact representation of molecular structures.
These descriptors facilitate the cataloging of chemical reactions, including bond-making and -breaking events.

Purpose of the Study:

To outline the evolution of graph-based methodologies for automated chemical reaction network generation.
To highlight recent advancements integrating computational chemistry with graph-based reaction discovery.

Main Methods:

Review of graph-based reaction discovery schemes.
Integration of semiempirical and ab initio electronic structure calculations.
Application of minimum-energy path refinements and transition state searches.

Main Results:

Demonstration of graph-based schemes as "virtual reaction vessels" for mechanistic studies.
Examples from homogeneous catalysis and interstellar chemistry showcase the utility of these methods.
Identification of ongoing challenges in chemical accuracy and computational speed.

Conclusions:

Graph-based methods are advancing the automation of chemical reaction network modeling.
Further development is needed to address computational limitations and the vastness of chemical reaction space.