Finding Pathways in Reaction Networks guided by Energy Barriers using Integer Linear Programming

Abstract

Analyzing synthesis pathways for target molecules in a chemical reaction network annotated with information on the kinetics of individual reactions is an area of active study. This work presents a computational methodology for searching for pathways in reaction networks which is based on integer linear programming and the modeling of reaction networks by directed hypergraphs. Often multiple pathways fit the given search criteria. To rank them, we develop an objective function based on physical arguments maximizing the probability of the pathway. We furthermore develop an automated pipeline to estimate the energy barriers of individual reactions in reaction networks. Combined, the methodology facilitates flexible and kinetically informed pathway investigations on large reaction networks by computational means, even for networks coming without kinetic annotation, such as those created via generative approaches for expanding molecular spaces. To demonstrate the methodology, we apply it on a chemical reaction network generated from 2-hydroxyethanenitrile, water, and ammonia, where we search for pathways to glycine and 2-hydroxyethanoic acid using the input molecules as precursors.

20 pages, 17 figures, 5 tables

References in corpus (5)

• Towards the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space
• Automated Exploration of Reaction Network and Mechanism via Meta-dynamics Nanoreactor
• Universal Slow Dynamics of Chemical Reaction Networks
• Finding Thermodynamically Favorable Pathways in Chemical Reaction Networks Using Flows in Hypergraphs and Mixed-Integer Linear Programming
• Cayley Graphs of Semigroups Applied to Atom Tracking in Chemistry