Algorithmic Complexity and Reprogrammability of Chemical Structure Networks

TL;DR

This paper investigates the algorithmic complexity and reprogrammability of chemical structure networks to understand their transformations and classify chemical compounds without relying on atomic details.

Contribution

It introduces a computational interventional calculus based on algorithmic probability to profile chemical networks and distinguish chemical classes through their structural reprogrammability.

Findings

Reprogrammability correlates with thermodynamic and chemical properties.

Methods can classify chemical structures without atomic or molecular data.

Numerical results suggest links between structural complexity and chemical function.

Abstract

Here we address the challenge of profiling causal properties and tracking the transformation of chemical compounds from an algorithmic perspective. We explore the potential of applying a computational interventional calculus based on the principles of algorithmic probability to chemical structure networks. We profile the sensitivity of the elements and covalent bonds in a chemical structure network algorithmically, asking whether reprogrammability affords information about thermodynamic and chemical processes involved in the transformation of different compound classes. We arrive at numerical results suggesting a correspondence between some physical, structural and functional properties. Our methods are capable of separating chemical classes that reflect functional and natural differences without considering any information about atomic and molecular properties. We conclude that these methods, with their links to chemoinformatics via algorithmic, probability hold promise for future research.