# An Intermediate Level of Abstraction for Computational Systems Chemistry

## TL;DR

This paper introduces an intermediate abstraction level using graph grammars and integer hyperflows to analyze large chemical reaction networks, aiding prebiotic chemistry research.

## Contribution

It formalizes chemical reactions as graph grammars and extends this to integer hyperflows, enabling the identification of complex reaction patterns in large networks.

## Key findings

- Graph grammars provide a suitable abstraction for large reaction networks.
- Integer hyperflows facilitate detection of auto-catalysis and complex patterns.
- The approach supports optimization-based analysis of prebiotic chemical systems.

## Abstract

Computational techniques are required for narrowing down the vast space of possibilities to plausible prebiotic scenarios, since precise information on the molecular composition, the dominant reaction chemistry, and the conditions for that era are scarce. The exploration of large chemical reaction networks is a central aspect in this endeavour. While quantum chemical methods can accurately predict the structures and reactivities of small molecules, they are not efficient enough to cope with large-scale reaction systems. The formalization of chemical reactions as graph grammars provides a generative system, well grounded in category theory, at the right level of abstraction for the analysis of large and complex reaction networks. An extension of the basic formalism into the realm of integer hyperflows allows for the identification of complex reaction patterns, such as auto-catalysis, in large reaction networks using optimization techniques.