# Computational approaches in chemical space exploration for carbon fixation pathways

## TL;DR

This paper introduces a computational method to explore chemical pathways for carbon fixation, aiding in sustainability efforts.

## Contribution

A novel graph-based computational approach integrated with ILP optimization for exploring and designing carbon fixation pathways.

## Key findings

- The approach identifies autocatalytic cycles and novel enzymatic cascades in carbon fixation pathways.
- Optimization criteria reveal new varieties and recombinations of natural autocatalytic pathways.
- The framework has potential applications in carbon capture and sustainable chemical production.

## Abstract

Chemical space exploration is an important part of chemistry and biology, enabling the discovery and optimization of metabolic pathways, advancing synthetic metabolic functions, and understanding biochemical network evolution. We use a graph-based computational approach implemented in the cheminformatics software MØD, integrated with Integer Linear Programming (ILP) optimization, to systematically search chemical spaces. This approach allows for flexible and targeted queries, including identification of autocatalytic cycles, thermodynamic considerations, and discovery of novel enzymatic cascades. Specifically, we explore the chemical space of natural and artificial carbon fixation pathways defined from relevant enzyme reactions. By applying different optimization criteria, we identify new varieties and recombinations of natural autocatalytic pathways, and compare the properties of the pathways. This work highlights the versatility of graph-based cheminformatics for the purpose of chemical space exploration and artificial pathway design. Potential applications of this framework extend to carbon capture technologies, improved agricultural yields, and value-added chemical production, advancing efforts to address global sustainability challenges.