Metabolism is central to all processes of life and the metabolome – large-scale measurement of the quantities of small molecular entities in cells and tissues – gives a readout of cellular functioning at a point in time. Harnessing metabolomic information together with transcriptomic information about gene expression allows for multi-level insights into genetic dysregulation and its cellular effects. I will describe a multi-omics approach based on genome-scale modelling that is able to integrate the two levels and provide insights into the systems-level deregulation of cellular function due to ageing by transforming the cellular reaction space into a constraint-based linear optimisation problem. Metabolic models such as these and their interpretation depends on publicly available data about small molecular metabolites. Chemical ontologies provide structured classifications of chemical entities that can be used for navigation and filtering of chemical space. ChEBI is a prominent example of a chemical ontology, widely used in life science contexts including to annotate metabolites in genome-scale models. However, ChEBI is manually maintained and as such does not scale to the full range of metabolites in all organisms. There is a need for tools that are able to automatically classify chemical data into chemical ontologies, which can be framed as a hierarchical multi-class classification problem, based on chemical structures, which are represented as connected graphs of atoms and bonds. I will discuss recent efforts to evaluate machine learning approaches for this task, comparing different learning frameworks including logistic regression, decision trees and LSTMs, and different encoding approaches for the chemical structures, including cheminformatics ‘fingerprints’ (feature vectors) and character-based encodings from chemical line notation structural representations.