Algorithmic Complexity and Reprogrammability of Chemical Structure Networks

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.

A Note on Distance-Based Entropy of Dendrimers

“…The findings presented in this section show that the correlation between ecc and degree-ecc-entropy measures of three dendrimers is approximately R 2 ≈ 0.999, see This means that the ecc and degree-ecc-entropy measures might be interesting for further investigations in predicting the physico-chemical properties of molecules [12,14,[18][19][20][21][22][23][24]. The reasons for choosing these molecular descriptors is that the dendrimers are hyper-branched molecules the degree and eccentricity of vertices of which are important.…”

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

“…An important inquiry in understanding collective effects is the influence of mixture complexity on the property complexity. While compositional complexity refers to the diversity of components in a mixtureits behavior being typically dependent on the compositional details, mixture-property complexity refers to the number of independent influential physical variables and their multiscale interactions . While Curie’s principle, emphasizing similar symmetries in causes (structure) and effects (property), has found application in the context of neural networks for isolated molecules, a notable gap in understanding remains regarding how symmetry breaking in spatially heterogeneous mixtures (Figure ) may increase the complexity of their properties .…”

Symmetry and Algorithmic Complexity of Polyominoes and Polyhedral Graphs

“…In [21,22] we explore connections related to structure networks of chemical compounds with toxicological applications. Here, in the current paper, we provide a unified and robust platform for estimating the algorithmic complexity of a graph on the basis of algorithmic information theory that is applicable to both abstract objects-e.g.…”